From 4588e766cb7dd5fed058c4818ea6b46c5c56c154 Mon Sep 17 00:00:00 2001
From: Toshinari Itoko <itoko@jp.ibm.com>
Date: Thu, 11 Apr 2024 17:18:04 +0900
Subject: [PATCH 1/9] Add layer fidelity notebook

---
 notebooks/layer_fidelity.ipynb | 2230 ++++++++++++++++++++++++++++++++
 1 file changed, 2230 insertions(+)
 create mode 100644 notebooks/layer_fidelity.ipynb

diff --git a/notebooks/layer_fidelity.ipynb b/notebooks/layer_fidelity.ipynb
new file mode 100644
index 0000000..af5360c
--- /dev/null
+++ b/notebooks/layer_fidelity.ipynb
@@ -0,0 +1,2230 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "alleged-prairie",
+   "metadata": {},
+   "source": [
+    "# Run Layer Fidelity (EPLG100) experiment using qiskit-experiments"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "`pip install \"qiskit-experiments>=0.7\" qiskit-ibm-provider` is required before running this notebook."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "f49e876a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from qiskit_experiments.library.randomized_benchmarking import LayerFidelity"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "78f23522",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import math\n",
+    "import numpy as np\n",
+    "import rustworkx as rx\n",
+    "import matplotlib.pyplot as plt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "detailed-limitation",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from qiskit_ibm_provider import IBMProvider"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "0d3dd525",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "provider = IBMProvider()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "fa501105",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Device ibm_kyoto Loaded with 127 qubits\n",
+      "Two Qubit Gate: ecr\n"
+     ]
+    }
+   ],
+   "source": [
+    "#specify backend and two-qubit gate to be layered \n",
+    "backend = provider.get_backend('ibm_kyoto')\n",
+    "twoq_gate = \"ecr\"\n",
+    "print(f\"Device {backend.name} Loaded with {backend.num_qubits} qubits\")\n",
+    "print(f\"Two Qubit Gate: {twoq_gate}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "997d7bc9",
+   "metadata": {},
+   "source": [
+    "### Find the best qubit chain based on reported 2q-gate errors\n",
+    "If you want to benchmark EPLG (error per layered gate) on 100 qubits, set `num_qubits_in_chain==100`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "7bc47055",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "num_qubits_in_chain = 100\n",
+    "coupling_map = backend.target.build_coupling_map(twoq_gate)\n",
+    "G = coupling_map.graph"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "8615f6d8",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Selecting the best from 313980 candidate paths (will take a few minutes)\n",
+      "Predicted LF from reported 2q-gate EPGs: 0.36495139725549536\n"
+     ]
+    }
+   ],
+   "source": [
+    "def to_edges(path):\n",
+    "    edges = []\n",
+    "    prev_node = None\n",
+    "    for node in path:\n",
+    "        if prev_node is not None:\n",
+    "            if G.has_edge(prev_node, node):\n",
+    "                edges.append((prev_node, node))\n",
+    "            else:\n",
+    "                edges.append((node, prev_node))\n",
+    "        prev_node = node\n",
+    "    return edges\n",
+    "    \n",
+    "def path_cost(path, correct_by_duration=True):\n",
+    "    \"\"\"Compute cost of a path. The path is given in node indices form, e.g. [0, 1, 2].\n",
+    "    Use `to_edges` function to obtain edge list, e.g. [(0, 1), (1, 2)].\"\"\"\n",
+    "    max_duration = max(backend.target[twoq_gate][qs].duration for qs in to_edges(path))\n",
+    "    \n",
+    "    def gate_error(qpair):\n",
+    "        duration = backend.target[twoq_gate][qpair].duration\n",
+    "        factor = max_duration / duration if correct_by_duration else 1.0\n",
+    "        return factor * backend.target[twoq_gate][qpair].error\n",
+    "    \n",
+    "    gate_cost = -np.prod([1-0.8*gate_error(qs) for qs in to_edges(path)])\n",
+    "    readout_cost = 0\n",
+    "    return gate_cost + readout_cost\n",
+    "\n",
+    "def flatten(paths):\n",
+    "    return [val for ps in paths.values() for vals in ps.values() for val in vals]\n",
+    "\n",
+    "paths = rx.all_pairs_all_simple_paths(G.to_undirected(multigraph=False), min_depth=num_qubits_in_chain, cutoff=num_qubits_in_chain)\n",
+    "if not flatten(paths):\n",
+    "    raise Exception(f\"No qubit chain with length={num_qubits_in_chain} exists in {backend.name}. Try smaller num_qubits_in_chain.\")\n",
+    "\n",
+    "print(f\"Selecting the best from {len(flatten(paths))} candidate paths (will take a few minutes)\")\n",
+    "best_qubit_chain =  min(flatten(paths), key=path_cost)\n",
+    "assert(len(best_qubit_chain) == num_qubits_in_chain)\n",
+    "print(f\"Predicted LF from reported 2q-gate EPGs: {-path_cost(best_qubit_chain)}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "efc3c14d",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([112, 108, 107, 106, 105, 104, 111, 122, 121, 120, 119, 118, 110,\n",
+       "       100, 101, 102,  92,  83,  82,  81,  80,  79,  91,  98,  97,  96,\n",
+       "        95,  94,  90,  75,  76,  77,  71,  58,  59,  60,  61,  62,  63,\n",
+       "        64,  65,  66,  73,  85,  86,  87,  88,  89,  74,  70,  69,  68,\n",
+       "        55,  49,  50,  51,  36,  32,  31,  30,  17,  12,  11,  10,   9,\n",
+       "         8,   7,   6,   5,   4,   3,   2,   1,   0,  14,  18,  19,  20,\n",
+       "        21,  22,  23,  24,  25,  26,  27,  28,  35,  47,  46,  45,  44,\n",
+       "        43,  42,  41,  40,  39,  38,  37,  52,  56], dtype=uint64)"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np.array(best_qubit_chain)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "e89c690a",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[[(112, 108),\n",
+       "  (106, 107),\n",
+       "  (105, 104),\n",
+       "  (122, 111),\n",
+       "  (121, 120),\n",
+       "  (119, 118),\n",
+       "  (110, 100),\n",
+       "  (101, 102),\n",
+       "  (83, 92),\n",
+       "  (82, 81),\n",
+       "  (80, 79),\n",
+       "  (98, 91),\n",
+       "  (96, 97),\n",
+       "  (94, 95),\n",
+       "  (75, 90),\n",
+       "  (76, 77),\n",
+       "  (71, 58),\n",
+       "  (59, 60),\n",
+       "  (62, 61),\n",
+       "  (63, 64),\n",
+       "  (65, 66),\n",
+       "  (73, 85),\n",
+       "  (87, 86),\n",
+       "  (89, 88),\n",
+       "  (74, 70),\n",
+       "  (68, 69),\n",
+       "  (49, 55),\n",
+       "  (50, 51),\n",
+       "  (32, 36),\n",
+       "  (30, 31),\n",
+       "  (17, 12),\n",
+       "  (11, 10),\n",
+       "  (8, 9),\n",
+       "  (7, 6),\n",
+       "  (4, 5),\n",
+       "  (3, 2),\n",
+       "  (1, 0),\n",
+       "  (18, 14),\n",
+       "  (19, 20),\n",
+       "  (22, 21),\n",
+       "  (23, 24),\n",
+       "  (25, 26),\n",
+       "  (27, 28),\n",
+       "  (47, 35),\n",
+       "  (46, 45),\n",
+       "  (44, 43),\n",
+       "  (42, 41),\n",
+       "  (39, 40),\n",
+       "  (37, 38),\n",
+       "  (56, 52)],\n",
+       " [(107, 108),\n",
+       "  (105, 106),\n",
+       "  (104, 111),\n",
+       "  (122, 121),\n",
+       "  (120, 119),\n",
+       "  (118, 110),\n",
+       "  (101, 100),\n",
+       "  (102, 92),\n",
+       "  (82, 83),\n",
+       "  (81, 80),\n",
+       "  (79, 91),\n",
+       "  (97, 98),\n",
+       "  (96, 95),\n",
+       "  (94, 90),\n",
+       "  (76, 75),\n",
+       "  (77, 71),\n",
+       "  (59, 58),\n",
+       "  (61, 60),\n",
+       "  (62, 63),\n",
+       "  (64, 65),\n",
+       "  (73, 66),\n",
+       "  (86, 85),\n",
+       "  (88, 87),\n",
+       "  (89, 74),\n",
+       "  (70, 69),\n",
+       "  (55, 68),\n",
+       "  (50, 49),\n",
+       "  (51, 36),\n",
+       "  (31, 32),\n",
+       "  (17, 30),\n",
+       "  (11, 12),\n",
+       "  (9, 10),\n",
+       "  (8, 7),\n",
+       "  (6, 5),\n",
+       "  (4, 3),\n",
+       "  (1, 2),\n",
+       "  (0, 14),\n",
+       "  (18, 19),\n",
+       "  (21, 20),\n",
+       "  (22, 23),\n",
+       "  (25, 24),\n",
+       "  (27, 26),\n",
+       "  (28, 35),\n",
+       "  (47, 46),\n",
+       "  (44, 45),\n",
+       "  (43, 42),\n",
+       "  (40, 41),\n",
+       "  (38, 39),\n",
+       "  (52, 37)]]"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# decompose the chain into two disjoint layers\n",
+    "all_pairs = to_edges(best_qubit_chain)\n",
+    "two_disjoint_layers = [all_pairs[0::2], all_pairs[1::2]]\n",
+    "two_disjoint_layers"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "inclusive-vietnam",
+   "metadata": {},
+   "source": [
+    "## Define a LayerFidelity experiment"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "rational-branch",
+   "metadata": {},
+   "source": [
+    "- `physical_qubits`: Physical qubits to use in this benchmarking\n",
+    "- `two_qubit_layers`: List of list of qubit pairs (a list of qubit pairs corresponds with a two-qubit gate layer), qubit direction matters\n",
+    "- `lengths`: List of layer lengths (the number of depth points)\n",
+    "- `backend`: Backend to be benchmarked\n",
+    "- `num_samples`: How many random circuits should be sampled for each layer length\n",
+    "- `seed`: Seed for the random number generator\n",
+    "\n",
+    "[Optional]\n",
+    "- `two_qubit_gate`: Two-qubit gate name to use in layers (If not specified, one of 2q-gates supported in the backend is automatically set.)\n",
+    "- `one_qubit_basis_gates`: One-qubit gate names to use for random 1q-Clifford layers (If not specified, all 1q-gates supported in the backend are automatically set.)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "russian-billion",
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "CPU times: user 20.2 ms, sys: 504 µs, total: 20.7 ms\n",
+      "Wall time: 20.5 ms\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%time\n",
+    "lfexp = LayerFidelity(\n",
+    "    physical_qubits=best_qubit_chain,\n",
+    "    two_qubit_layers=two_disjoint_layers,\n",
+    "    lengths=[2, 4, 8, 16, 30, 50, 70, 100, 150, 200, 300, 500],\n",
+    "    backend=backend,\n",
+    "    num_samples=12,\n",
+    "    seed=42,\n",
+    "    # two_qubit_gate=\"ecr\",\n",
+    "    # one_qubit_basis_gates=[\"rz\", \"sx\", \"x\"],\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "50e33867",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Options(max_circuits=None, lengths=[2, 4, 8, 16, 30, 50, 70, 100, 150, 200, 300, 500], num_samples=12, seed=42, two_qubit_layers=[[(112, 108), (106, 107), (105, 104), (122, 111), (121, 120), (119, 118), (110, 100), (101, 102), (83, 92), (82, 81), (80, 79), (98, 91), (96, 97), (94, 95), (75, 90), (76, 77), (71, 58), (59, 60), (62, 61), (63, 64), (65, 66), (73, 85), (87, 86), (89, 88), (74, 70), (68, 69), (49, 55), (50, 51), (32, 36), (30, 31), (17, 12), (11, 10), (8, 9), (7, 6), (4, 5), (3, 2), (1, 0), (18, 14), (19, 20), (22, 21), (23, 24), (25, 26), (27, 28), (47, 35), (46, 45), (44, 43), (42, 41), (39, 40), (37, 38), (56, 52)], [(107, 108), (105, 106), (104, 111), (122, 121), (120, 119), (118, 110), (101, 100), (102, 92), (82, 83), (81, 80), (79, 91), (97, 98), (96, 95), (94, 90), (76, 75), (77, 71), (59, 58), (61, 60), (62, 63), (64, 65), (73, 66), (86, 85), (88, 87), (89, 74), (70, 69), (55, 68), (50, 49), (51, 36), (31, 32), (17, 30), (11, 12), (9, 10), (8, 7), (6, 5), (4, 3), (1, 2), (0, 14), (18, 19), (21, 20), (22, 23), (25, 24), (27, 26), (28, 35), (47, 46), (44, 45), (43, 42), (40, 41), (38, 39), (52, 37)]], two_qubit_gate='ecr', one_qubit_basis_gates=('sx', 'id', 'x', 'rz'), clifford_synthesis_method='rb_default')"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "lfexp.experiment_options"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "c033c62c",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Two Qubit Gate: ecr\n",
+      "One Qubit Gate Set: ('sx', 'id', 'x', 'rz')\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(f\"Two Qubit Gate: {lfexp.experiment_options.two_qubit_gate}\")\n",
+    "print(f\"One Qubit Gate Set: {lfexp.experiment_options.one_qubit_basis_gates}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d19b5a19",
+   "metadata": {},
+   "source": [
+    "### Quick look at a RB circuit\n",
+    "`circuits_geenrator` is useful for that."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "114f5349",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "CPU times: user 3.31 s, sys: 24.6 ms, total: 3.33 s\n",
+      "Wall time: 3.34 s\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 8781.79x6104.28 with 1 Axes>"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "%%time\n",
+    "# look at one of the first three 2Q direct RB circuits quickly\n",
+    "circ_iter = lfexp.circuits_generator()\n",
+    "first_three_circuits = list(next(circ_iter) for _ in range(3))\n",
+    "first_three_circuits[1].draw(output=\"mpl\", style=\"clifford\", idle_wires=False, fold=-1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5866f7eb",
+   "metadata": {},
+   "source": [
+    "### Generating all RB circuits takes a bit more time"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "a4dfc21d",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "288 circuits are generated.\n",
+      "CPU times: user 43 s, sys: 68.5 ms, total: 43.1 s\n",
+      "Wall time: 43.2 s\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%time\n",
+    "# generate all circuits to run\n",
+    "circuits = lfexp.circuits()\n",
+    "print(f\"{len(circuits)} circuits are generated.\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "therapeutic-merchant",
+   "metadata": {},
+   "source": [
+    "## Run the Layer Fidelity experiment\n",
+    "\n",
+    "Run = Generate circuits and submit the job to the backend.\n",
+    "\n",
+    "Set `exp_data.auto_save = True` to store the experiment data using qiskit-ibm-experiment service."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "iraqi-dominican",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Run experiment: ID=368f151b-63e9-49db-a36e-66a365948644 with jobs ['cqxrpb9p3cg0008cjds0']]\n",
+      "CPU times: user 1min 59s, sys: 371 ms, total: 2min\n",
+      "Wall time: 2min 17s\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%time\n",
+    "#number of shots per job\n",
+    "nshots = 400\n",
+    "\n",
+    "# Run the LF experiment (generate circuits and submit the job)\n",
+    "exp_data = lfexp.run(shots=nshots)\n",
+    "# exp_data.auto_save = True\n",
+    "print(f\"Run experiment: ID={exp_data.experiment_id} with jobs {exp_data.job_ids}]\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f0104130",
+   "metadata": {},
+   "source": [
+    "## Wait for the result\n",
+    "Retrieve the result after the job is completed and run analysis (fit RB curves and estimate process fidelity for each 1Q/2Q direct RB)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "banner-trader",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "CPU times: user 5h 27min 43s, sys: 3min 18s, total: 5h 31min 1s\n",
+      "Wall time: 6h 15min 9s\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "ExperimentData(LayerFidelity, 368f151b-63e9-49db-a36e-66a365948644, job_ids=['cqxrpb9p3cg0008cjds0'], metadata=<4 items>, figure_names=['DirectRB_Q112_Q108.svg', 'DirectRB_Q106_Q107.svg', 'DirectRB_Q105_Q104.svg', 'DirectRB_Q122_Q111.svg', 'DirectRB_Q121_Q120.svg', 'DirectRB_Q119_Q118.svg', 'DirectRB_Q110_Q100.svg', 'DirectRB_Q101_Q102.svg', 'DirectRB_Q83_Q92.svg', 'DirectRB_Q82_Q81.svg', 'DirectRB_Q80_Q79.svg', 'DirectRB_Q98_Q91.svg', 'DirectRB_Q96_Q97.svg', 'DirectRB_Q94_Q95.svg', 'DirectRB_Q75_Q90.svg', 'DirectRB_Q76_Q77.svg', 'DirectRB_Q71_Q58.svg', 'DirectRB_Q59_Q60.svg', 'DirectRB_Q62_Q61.svg', 'DirectRB_Q63_Q64.svg', 'DirectRB_Q65_Q66.svg', 'DirectRB_Q73_Q85.svg', 'DirectRB_Q87_Q86.svg', 'DirectRB_Q89_Q88.svg', 'DirectRB_Q74_Q70.svg', 'DirectRB_Q68_Q69.svg', 'DirectRB_Q49_Q55.svg', 'DirectRB_Q50_Q51.svg', 'DirectRB_Q32_Q36.svg', 'DirectRB_Q30_Q31.svg', 'DirectRB_Q17_Q12.svg', 'DirectRB_Q11_Q10.svg', 'DirectRB_Q8_Q9.svg', 'DirectRB_Q7_Q6.svg', 'DirectRB_Q4_Q5.svg', 'DirectRB_Q3_Q2.svg', 'DirectRB_Q1_Q0.svg', 'DirectRB_Q18_Q14.svg', 'DirectRB_Q19_Q20.svg', 'DirectRB_Q22_Q21.svg', 'DirectRB_Q23_Q24.svg', 'DirectRB_Q25_Q26.svg', 'DirectRB_Q27_Q28.svg', 'DirectRB_Q47_Q35.svg', 'DirectRB_Q46_Q45.svg', 'DirectRB_Q44_Q43.svg', 'DirectRB_Q42_Q41.svg', 'DirectRB_Q39_Q40.svg', 'DirectRB_Q37_Q38.svg', 'DirectRB_Q56_Q52.svg', 'DirectRB_Q107_Q108.svg', 'DirectRB_Q105_Q106.svg', 'DirectRB_Q104_Q111.svg', 'DirectRB_Q122_Q121.svg', 'DirectRB_Q120_Q119.svg', 'DirectRB_Q118_Q110.svg', 'DirectRB_Q101_Q100.svg', 'DirectRB_Q102_Q92.svg', 'DirectRB_Q82_Q83.svg', 'DirectRB_Q81_Q80.svg', 'DirectRB_Q79_Q91.svg', 'DirectRB_Q97_Q98.svg', 'DirectRB_Q96_Q95.svg', 'DirectRB_Q94_Q90.svg', 'DirectRB_Q76_Q75.svg', 'DirectRB_Q77_Q71.svg', 'DirectRB_Q59_Q58.svg', 'DirectRB_Q61_Q60.svg', 'DirectRB_Q62_Q63.svg', 'DirectRB_Q64_Q65.svg', 'DirectRB_Q73_Q66.svg', 'DirectRB_Q86_Q85.svg', 'DirectRB_Q88_Q87.svg', 'DirectRB_Q89_Q74.svg', 'DirectRB_Q70_Q69.svg', 'DirectRB_Q55_Q68.svg', 'DirectRB_Q50_Q49.svg', 'DirectRB_Q51_Q36.svg', 'DirectRB_Q31_Q32.svg', 'DirectRB_Q17_Q30.svg', 'DirectRB_Q11_Q12.svg', 'DirectRB_Q9_Q10.svg', 'DirectRB_Q8_Q7.svg', 'DirectRB_Q6_Q5.svg', 'DirectRB_Q4_Q3.svg', 'DirectRB_Q1_Q2.svg', 'DirectRB_Q0_Q14.svg', 'DirectRB_Q18_Q19.svg', 'DirectRB_Q21_Q20.svg', 'DirectRB_Q22_Q23.svg', 'DirectRB_Q25_Q24.svg', 'DirectRB_Q27_Q26.svg', 'DirectRB_Q28_Q35.svg', 'DirectRB_Q47_Q46.svg', 'DirectRB_Q44_Q45.svg', 'DirectRB_Q43_Q42.svg', 'DirectRB_Q40_Q41.svg', 'DirectRB_Q38_Q39.svg', 'DirectRB_Q52_Q37.svg', 'DirectRB_Q112.svg', 'DirectRB_Q56.svg'])"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "%%time\n",
+    "exp_data.block_for_results()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "white-variation",
+   "metadata": {},
+   "source": [
+    "### Print out the result data as a table"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "fabulous-platinum",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Get the result data as a pandas DataFrame\n",
+    "df = exp_data.analysis_results(dataframe=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "a3d31324",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>name</th>\n",
+       "      <th>experiment</th>\n",
+       "      <th>components</th>\n",
+       "      <th>value</th>\n",
+       "      <th>quality</th>\n",
+       "      <th>backend</th>\n",
+       "      <th>run_time</th>\n",
+       "      <th>qubits</th>\n",
+       "      <th>reason</th>\n",
+       "      <th>a</th>\n",
+       "      <th>alpha</th>\n",
+       "      <th>b</th>\n",
+       "      <th>chisq</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>41b501b0</th>\n",
+       "      <td>LF</td>\n",
+       "      <td>LayerFidelity</td>\n",
+       "      <td>[Q112, Q108, Q107, Q106, Q105, Q104, Q111, Q12...</td>\n",
+       "      <td>0.08425551354634768+/-nan</td>\n",
+       "      <td>bad</td>\n",
+       "      <td>ibm_kyoto</td>\n",
+       "      <td>2024-03-21 16:29:46.643000+09:00</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9b098b6b</th>\n",
+       "      <td>EPLG</td>\n",
+       "      <td>LayerFidelity</td>\n",
+       "      <td>[Q112, Q108, Q107, Q106, Q105, Q104, Q111, Q12...</td>\n",
+       "      <td>0.024679263638514026+/-nan</td>\n",
+       "      <td>bad</td>\n",
+       "      <td>ibm_kyoto</td>\n",
+       "      <td>2024-03-21 16:29:46.643000+09:00</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5efada9c</th>\n",
+       "      <td>SingleLF</td>\n",
+       "      <td>LayerFidelity</td>\n",
+       "      <td>[Q112, Q108, Q106, Q107, Q105, Q104, Q122, Q11...</td>\n",
+       "      <td>0.2888253412891042+/-nan</td>\n",
+       "      <td>bad</td>\n",
+       "      <td>ibm_kyoto</td>\n",
+       "      <td>2024-03-21 16:29:46.643000+09:00</td>\n",
+       "      <td>[112, 108, 106, 107, 105, 104, 122, 111, 121, ...</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9770d221</th>\n",
+       "      <td>SingleLF</td>\n",
+       "      <td>LayerFidelity</td>\n",
+       "      <td>[Q107, Q108, Q105, Q106, Q104, Q111, Q122, Q12...</td>\n",
+       "      <td>0.292+/-0.011</td>\n",
+       "      <td>bad</td>\n",
+       "      <td>ibm_kyoto</td>\n",
+       "      <td>2024-03-21 16:29:46.643000+09:00</td>\n",
+       "      <td>[107, 108, 105, 106, 104, 111, 122, 121, 120, ...</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "              name     experiment  \\\n",
+       "41b501b0        LF  LayerFidelity   \n",
+       "9b098b6b      EPLG  LayerFidelity   \n",
+       "5efada9c  SingleLF  LayerFidelity   \n",
+       "9770d221  SingleLF  LayerFidelity   \n",
+       "\n",
+       "                                                 components  \\\n",
+       "41b501b0  [Q112, Q108, Q107, Q106, Q105, Q104, Q111, Q12...   \n",
+       "9b098b6b  [Q112, Q108, Q107, Q106, Q105, Q104, Q111, Q12...   \n",
+       "5efada9c  [Q112, Q108, Q106, Q107, Q105, Q104, Q122, Q11...   \n",
+       "9770d221  [Q107, Q108, Q105, Q106, Q104, Q111, Q122, Q12...   \n",
+       "\n",
+       "                               value quality    backend  \\\n",
+       "41b501b0   0.08425551354634768+/-nan     bad  ibm_kyoto   \n",
+       "9b098b6b  0.024679263638514026+/-nan     bad  ibm_kyoto   \n",
+       "5efada9c    0.2888253412891042+/-nan     bad  ibm_kyoto   \n",
+       "9770d221               0.292+/-0.011     bad  ibm_kyoto   \n",
+       "\n",
+       "                                  run_time  \\\n",
+       "41b501b0  2024-03-21 16:29:46.643000+09:00   \n",
+       "9b098b6b  2024-03-21 16:29:46.643000+09:00   \n",
+       "5efada9c  2024-03-21 16:29:46.643000+09:00   \n",
+       "9770d221  2024-03-21 16:29:46.643000+09:00   \n",
+       "\n",
+       "                                                     qubits reason     a  \\\n",
+       "41b501b0                                               None   None  None   \n",
+       "9b098b6b                                               None   None  None   \n",
+       "5efada9c  [112, 108, 106, 107, 105, 104, 122, 111, 121, ...   None  None   \n",
+       "9770d221  [107, 108, 105, 106, 104, 111, 122, 121, 120, ...   None  None   \n",
+       "\n",
+       "         alpha     b chisq  \n",
+       "41b501b0  None  None  None  \n",
+       "9b098b6b  None  None  None  \n",
+       "5efada9c  None  None  None  \n",
+       "9770d221  None  None  None  "
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# LF and LF of each disjoing set\n",
+    "df[(df.name == \"LF\") | (df.name == \"EPLG\") | (df.name == \"SingleLF\")]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "d80948c0",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>name</th>\n",
+       "      <th>experiment</th>\n",
+       "      <th>components</th>\n",
+       "      <th>value</th>\n",
+       "      <th>quality</th>\n",
+       "      <th>backend</th>\n",
+       "      <th>run_time</th>\n",
+       "      <th>qubits</th>\n",
+       "      <th>reason</th>\n",
+       "      <th>a</th>\n",
+       "      <th>alpha</th>\n",
+       "      <th>b</th>\n",
+       "      <th>chisq</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>315b252c</th>\n",
+       "      <td>ProcessFidelity</td>\n",
+       "      <td>LayerFidelity</td>\n",
+       "      <td>[Q112, Q108]</td>\n",
+       "      <td>0.9741+/-0.0018</td>\n",
+       "      <td>bad</td>\n",
+       "      <td>ibm_kyoto</td>\n",
+       "      <td>2024-03-21 16:29:46.643000+09:00</td>\n",
+       "      <td>(112, 108)</td>\n",
+       "      <td>large_spam</td>\n",
+       "      <td>0.403517</td>\n",
+       "      <td>0.972388</td>\n",
+       "      <td>0.253707</td>\n",
+       "      <td>1.211738</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8ba264a8</th>\n",
+       "      <td>ProcessFidelity</td>\n",
+       "      <td>LayerFidelity</td>\n",
+       "      <td>[Q106, Q107]</td>\n",
+       "      <td>0.9750+/-0.0031</td>\n",
+       "      <td>bad</td>\n",
+       "      <td>ibm_kyoto</td>\n",
+       "      <td>2024-03-21 16:29:46.643000+09:00</td>\n",
+       "      <td>(106, 107)</td>\n",
+       "      <td>large_spam</td>\n",
+       "      <td>0.192179</td>\n",
+       "      <td>0.973366</td>\n",
+       "      <td>0.212858</td>\n",
+       "      <td>2.313055</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>fd70efcf</th>\n",
+       "      <td>ProcessFidelity</td>\n",
+       "      <td>LayerFidelity</td>\n",
+       "      <td>[Q105, Q104]</td>\n",
+       "      <td>0.9999999999999999+/-nan</td>\n",
+       "      <td>bad</td>\n",
+       "      <td>ibm_kyoto</td>\n",
+       "      <td>2024-03-21 16:29:46.643000+09:00</td>\n",
+       "      <td>(105, 104)</td>\n",
+       "      <td>large_spam</td>\n",
+       "      <td>0.003853</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.25289</td>\n",
+       "      <td>0.97619</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>329c0c1a</th>\n",
+       "      <td>ProcessFidelity</td>\n",
+       "      <td>LayerFidelity</td>\n",
+       "      <td>[Q122, Q111]</td>\n",
+       "      <td>0.939+/-0.008</td>\n",
+       "      <td>bad</td>\n",
+       "      <td>ibm_kyoto</td>\n",
+       "      <td>2024-03-21 16:29:46.643000+09:00</td>\n",
+       "      <td>(122, 111)</td>\n",
+       "      <td>large_spam</td>\n",
+       "      <td>0.433793</td>\n",
+       "      <td>0.934506</td>\n",
+       "      <td>0.24728</td>\n",
+       "      <td>3.67749</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>b1455dcc</th>\n",
+       "      <td>ProcessFidelity</td>\n",
+       "      <td>LayerFidelity</td>\n",
+       "      <td>[Q121, Q120]</td>\n",
+       "      <td>0.9858+/-0.0005</td>\n",
+       "      <td>bad</td>\n",
+       "      <td>ibm_kyoto</td>\n",
+       "      <td>2024-03-21 16:29:46.643000+09:00</td>\n",
+       "      <td>(121, 120)</td>\n",
+       "      <td>large_chisq</td>\n",
+       "      <td>0.652881</td>\n",
+       "      <td>0.984827</td>\n",
+       "      <td>0.239197</td>\n",
+       "      <td>3.405308</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                     name     experiment    components  \\\n",
+       "315b252c  ProcessFidelity  LayerFidelity  [Q112, Q108]   \n",
+       "8ba264a8  ProcessFidelity  LayerFidelity  [Q106, Q107]   \n",
+       "fd70efcf  ProcessFidelity  LayerFidelity  [Q105, Q104]   \n",
+       "329c0c1a  ProcessFidelity  LayerFidelity  [Q122, Q111]   \n",
+       "b1455dcc  ProcessFidelity  LayerFidelity  [Q121, Q120]   \n",
+       "\n",
+       "                             value quality    backend  \\\n",
+       "315b252c           0.9741+/-0.0018     bad  ibm_kyoto   \n",
+       "8ba264a8           0.9750+/-0.0031     bad  ibm_kyoto   \n",
+       "fd70efcf  0.9999999999999999+/-nan     bad  ibm_kyoto   \n",
+       "329c0c1a             0.939+/-0.008     bad  ibm_kyoto   \n",
+       "b1455dcc           0.9858+/-0.0005     bad  ibm_kyoto   \n",
+       "\n",
+       "                                  run_time      qubits       reason         a  \\\n",
+       "315b252c  2024-03-21 16:29:46.643000+09:00  (112, 108)   large_spam  0.403517   \n",
+       "8ba264a8  2024-03-21 16:29:46.643000+09:00  (106, 107)   large_spam  0.192179   \n",
+       "fd70efcf  2024-03-21 16:29:46.643000+09:00  (105, 104)   large_spam  0.003853   \n",
+       "329c0c1a  2024-03-21 16:29:46.643000+09:00  (122, 111)   large_spam  0.433793   \n",
+       "b1455dcc  2024-03-21 16:29:46.643000+09:00  (121, 120)  large_chisq  0.652881   \n",
+       "\n",
+       "             alpha         b     chisq  \n",
+       "315b252c  0.972388  0.253707  1.211738  \n",
+       "8ba264a8  0.973366  0.212858  2.313055  \n",
+       "fd70efcf       1.0   0.25289   0.97619  \n",
+       "329c0c1a  0.934506   0.24728   3.67749  \n",
+       "b1455dcc  0.984827  0.239197  3.405308  "
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# 1Q/2Q process fidelities\n",
+    "df[(df.name == \"ProcessFidelity\")].head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1ebf0edf",
+   "metadata": {},
+   "source": [
+    "### Display plots (1Q/2Q simultaneous direct RB curves)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "unlike-python",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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qTZTj68BlGcr7/VhV30GsH5B3cWzr/e8nPoPWxOexCfhMFp/lSo60KI4n3r8BK9jNyZC+u7K+p5vtn4sVdJoTx7gPKyCdm5buKuCNxDG2Jf5+T1oZbMW6uuqx1Tc9DDiDVaNgsO77pn6uqY864HnSBk7pYX9nY11t+hNlvwI463jf5ZTyX5nlfmZhdf+qwmrhbRLfrXMzpLVhdbnbj/W9fgbrxLO71vunYQXFIFYA/w+OHiBnCce23p+JFdQCiXWp/1/eAH57nO/bUY9uPsNMjyWJNBN7SJOe1ysSy475/5Uhj/u62d6+tHR2rOC9H+v/+UbgqgzbKwXuwxrBNAgcxGpHNCND2l8Ba7L5PpwKD0kctFJKqeMQkY9h9Xr4jLFqRIYMsQZPuh+oMFbN1qASkXuxrsDnmiEaaBK3aqqBLxpjfjPY+RkIek9fKaWyZIz5FVZjzftF5JrBzk+aR7FqJNLnbhgsi7GGwh2SAT/h41i1TNnckjsl6JW+UkqdIhLtRM4wxoyomeNOlFhDMr9ljHltsPMyUDToK6WUUiOEVu8rpZRSI8Qp3WWvrKzMTJw4sU+21dHRQW5u7vETqh5pOZ48LcO+oeV48rQM+0Zfl+PatWsbjDHlmdad0kF/4sSJrFmzpk+2tXLlSpYsWdIn2xrJtBxPnpZh39ByPHlahn2jr8tRRPZ3t06r95VSSqkRQoO+UkopNUJo0FdKKaVGCA36Siml1AihQV8ppZQaITToK6WUUiOEBn2llFJqhNCgr5RSSo0Qp/TgPH1t69at7Nq1i2nTpjF79uzBzo5SSinVKxr0s9TZ2cmf/vQnjDFs3LgRQAO/UkqpYUWr97MUCoUwxuBwODDGsGvXrsHOklJKKdUrGvSz5Ha7ERGi0SgiwrRp0wY7S0oppVSvaPV+lnJycrjyyiv1nr5SSqlhS4N+L8yePVuDvVJKqWFrUKr3ReR2EdkrIp0islZELugh7UMiYjI8OgYyz0oppdRwN+BBX0SuBu4H7gUWAKuAp0VkfDdv+SxQkfbYAyzv/9wqpZRSp47BuNL/AvCQMeZXxphtxphPA9XAJzIlNsa0GmNqkg9gCjAZ+NXAZVkppZQa/gY06IuIC1gIPJu26lngvCw38zFgizFmVV/mTSmllDrViTFm4HYmUgkcBhYbY15KWf4t4HpjzIzjvL8Qq1bgq8aY+7tJcytwK4DP51v45JNP9kne/X4/OTk5RCIRnE4nDoe2gTwRfr+fvLy8wc7GsKZl2De0HE+elmHf6OtyvOiii9YaYxZlWjfcItcNWLUTj3SXwBjzIPAgwKJFi8ySJUv6ZMf/+te/2LZtG7FYDLvdzrJly/D5fH2y7ZFk5cqV9NVnMlJpGfYNLceTp2XYNwayHAf6nn4DEAPSo6UPqMni/R8D/mSMaerrjB1PJBIhHA4DEA6HqaqqGugsKKWUUidlQIO+MSYMrAUuSVt1CVYr/m6JyFnAPAapAZ+I0NHRQWtrKx0dHbjd7sHIhlJKKXXCBqP1/o+Am0TkFhGZJSL3A5XALwBE5GEReTjD+24FdhljVg5cVo+IRqOAFfwB6urqBiMbSiml1Akb8Hv6xpjfi0gp8A2sPvebgcuNMfsTSY7pry8i+cA1wLcHLKMZiAgigjGmK/grpZRSw8WgNOQzxjwAPNDNuiUZlrUDg9pENCcnB6/XSygUwu12M3PmzMHMjlJKKdVrOsueUkopNUJo0M9SZ2cngUCAWCxGR0cHa9asGewsKaWUUr2iQb8XjDHE43GMMWzZsoXa2trBzpJSSimVteE2OM+Ae/yNAzyx+gBtbTZqg/MwxpBvC2OL2Hjmobe4eclMrju7u7mClFJKqaFDg/5xXHf2eK47ezz/+te/+NHLTUSjUc53H6CgoIBrr71WR+VTSik1bGjQz1IkEsFms5GTk0OOO4f58+drwFdKKTWs6D39LDmdTkSEeDyOx+Nh1qxZg50lpZRSqlf0Sj9LDoeDyZMnEwgEWPa+pXqVr5RSatjRoN8LXq8Hr9ejAV8ppdSwpNX7vRCPG5rbOli/fr1211NKKTXs6JV+L7T6A+zYc5AXW6qw2+0sW7ZMr/qVUkoNG3ql3wvVjS20RG00m1xisRhVVVWDnSWllFIqaxr0s7S3OczW+gitcRd/ri2hPuKisrJysLOllFJKZS3r6n0R+QhwLdbUtzlpq40xZkpfZmyo2dMSxWCdJcXFRk7FNK3aV0opNaxkFfRF5JvA3cBmYAMQ6sc8DUmTixwIUWKA3QYXzZ862FlSSimleiXbK/2bgfuNMZ/vz8wMZZOKXZw5ycPuOj+fWTyV80+bNNhZUkoppXol26BfCjzVnxkZDkYX5dERESZUlA92VpRSSqley7Yh34vAvP7MyHARjUZYv2Wn9tNXSik17GR7pf854M8i0gj8A2hKT2CMifdhvoakltZWGuvrWedvgwPruPpq7aevlFJq+Mg26O9MPP+2m/WmF9salqLRKPsP1tEZcxAJR+gIRamqqtKgr5RSatjINlB/Gyuwj1iRSIQYNoI4iRtD2KD99JVSSg0rWQV9Y8xd/ZyPIc/pdCI2IY4Nh9PJOy58h17lK6WUGlZ6XSUvInlAMdBsjPH3fZaGlsffOMATqw/Q3h4h5iklHg6xXcbz3Vebuf/NV7j2rPFcd/b4wc6mUkopdVxZD8MrIpeKyBqgBdgHtIjIahG5pJ/yNiRcd/Z4nvr0+dw0x8W40jwcNpjlbuYDk+CpT5+vAV8ppdSwkVXQF5FLgb8DecB/ALcD9wD5wD9O9cAPEIzCqrcbCcXi1Ld08Mb6TaxatWqws6WUUkplLdsr/buAZ4HZxpi7jTG/TNznnwM8hzVE7yltTH6yqIQALqLGxpZt2wc1T0oppVRvZBv05wE/S++Ln3j9ADC/j/M15BS4hFwHgNBuXCAwcer0wc6WUkoplbVsg34IKOhmXT4jZAKeieX5AIRtHmbOnMlp8xdSW1vL+vXrdYQ+pZRSQ162rfdXAv8hIq8bY/YmF4rIeKyq/xf6PmtDz9gSL1uq23G6cxg1ZgL7D9fy5oqniMVi2O12li3TEfqUUkoNXdle6X8FKAR2iMhLIvJ7EXkR2AUUJdaf8sYVewEIR+MEwlF2H6whFovh9XqJxWJUVVUNcg6VUkqp7mU7OM9OETkd+HfgAuAMrPH37wf+yxhT3X9ZHHyPv3GAB1cFidisw+wIx3js9QN4HEIkMI5Zribm5xsdoU8ppdSQlvXgPInA/sV+zMuQdd3Z46kM7mHCaYu46IcvArBgTC5nTygksHkL4XAYEdcg51IppZTqWdaD8ygI+1tITkGweW8Nh6uqiRqhqKgIQKv3lVJKDWndXumLyPPA7caY7Ym/e2KMMUv7NmtDT01NDXbixLDjj9sxxhATB4FAALvdrtX7SimlhrSeqvcl5W8bPc+yJz2sO2WMHj0aO4eJATEj5JeO4pLzZmL8jVRWVmrLfaWUUkNat0HfGHNRyt9LBiQ3Q1x5+SjyvG6aAjGwO8GRQ3soRt5gZ0wppZTKQlYN+UTkRuDvxpjGDOtKgPcYYx7u68wNNTtr22nrjAHQGTXsrGri8Lq3GEUzbrebG264Qa/2lVJKDVnZNuT7LTClm3WTEuuzJiK3i8heEekUkbUicsFx0rtE5NuJ94RE5ICIfKY3++wL26rbjrqPseNwM83BCJFIBL/fz5o1awY6S0oppVTWsu2y19M9+1wgmu0OReRqrP79twOvJJ6fFpHZxpgD3bztSWAscCvWgEA+wJPtPvvKrIoCxGaDuDUFQSAcIWzsxA3YBOrr6wc6S0oppVTWemq9Px9rEJ6k94rIaWnJPMA1WIE4W18AHjLG/Crx+tMichnwCeCrGfLxLmApMMUY05BYvK8X++szM3z5TB2Vy+4aP+G4ISoOjIEIdtzEKCsrG4xsKaWUUlnp6Ur/fcCdib8N8PVu0jUCN2ezM7FGsFkI/DBt1bPAed287f3Am8AXEm0LgsDTwNeMMf5s9ttXPC47HqedAq+TBn+YmNixuXIwNje5OcKZZ545kNlRSimlekWMydwTT0QKscbVF2AP8EFgfVqyEFBrutvIsdusBA4Di40xL6Us/xZwvTFmRob3/BNYAqwAvp3I00+AjcaYqzKkvxXrNgA+n2/hk08+mU3Wjsvv95OXl8d33wjSHIxT1wluG1wywcacojjTSt04HFkPcDhiJctRnTgtw76h5XjytAz7Rl+X40UXXbTWGLMo07qeuuy1Aq0AIjIJqDbGhPssV9lLjhFwXSJPiMingGdExGeMOWpOW2PMg8CDAIsWLTJLlizpk0ysXLmSJUuW8OD2l4mYDuo6Y8SAirETmDaphHfOHt0n+znVJctRnTgtw76h5XjytAz7xkCWY7YT7uzvo/01ADGshnipfEBNN++pBg4nA37CtsTzeKDfJ7JPTrjjePM5GprbiBgb4CQaF5a/eYCG9hBLZ/kQGRFjFCmllBqmsh57X0RuFZH1IhIQkVj6I5ttJGoK1gKXpK26BFjVzdteBSpFJLXuY3riua9ORnp03dnj+cZZTi6Tt7jMvYN5rhocWC34J5fns3BiCYFwVkWglFJKDZqsgn6iAd1PsBrU5WD1y38UaAPexrrXnq0fATeJyC0iMktE7gcqgV8k9vWwiKQO9PM4VmPB34rIHBF5B1aXvz8aY+p6sd+TEolEEBFybAYTN+TarF6K1a1BwOAPZd1rUSmllBoU2V7pfw74Lla3OoAHjDEfASZjtaY/ZqS+7hhjfp/Y3jeADcD5wOUptxDGJx7J9H7gnUAh1knHcuBF4KPZ7rMvOJ1OXC4X+d4cvG4nE0YVABCIxOgIx2gLRAYyO0oppVSvZdvcfBrwEhBPPFwAxphmEfkO8B3gp9nu1BjzAPBAN+uWZFi2A3hXttvvDw6Hg2XLllFVVcW0cC4vHwiyseYQ4UiMt/cdZIwnxrTR+V3pa2trqaqq0ol4lFJKDRnZBv0gYDPGGBGpwbrCfz2xzo9VPX/K8/l8+Hw+cmva2d16GIBQNMaOPYdor9rDmeML8fl81NbWsnz5cmKxGHa7nWXLlmngV0opNeiyrd7fBExN/P0y8DUROVdEzgTuArb3Q96GpNraWg7u2YHbWNX5BhvtkkMoBvsPWicCVVVVhMNW78ZwOExVVdWg5VcppZRKyvZK/0Gsq3uAbwL/who3H6Ada9S8U17yCn5tWy5rO0qw4SQO7Am6qbeVcGBlC57XXmHJOAeBQABjDCKC2+0e7KwrpZRSWffT/33K37tFZA5wLuAFVqWMiX9Kq6qqIhaLcV55FKc08rZtDHtaYjhsNj56/niuPHsa40u9rF+/nuff9mK324nFYoRCocHOulJKKZV9P/1UxpgOY8y/jDF/HSkBH6CyshK73U4gECDfaRhfdqThHs4cGvyhrnQulwsAl8tFZeWIaPKglFJqiOtplr3x3a3LpIdpcU8ZPp+vqwV/wFnEywc6Wbm7hWgcahpbqWsv7Eq3dOlSdu3axbRp07QRn1JKqSGhp+r9fVhj3mfLfnJZGR6SLfhrWjtZd2gnVhEJa7buZXKZl/hsH/X1daxYsYJYLMaBAwcoLS3VwK+UUmrQ9RT0P0rvgv6IUuBx4G9vw4YhjtAcc9LU3ExHONp179/r9RIIBKiqqtKgr5RSatD1NMveQwOYj2HH47RTXlKMnSbigD/uIDe/EH8oetS9f7vdrvf0lVJKDQk6AfwJEhFcnlxiibsaYWOnNii0BCLMqjhy719H5FNKKTVUZBX0ReR/jpPEGGNu7oP8DCtNgTAidN0EOdAUoL4txKyKI/f+lVJKqaEi2yv9izn2/n4JkA+0JB4jQuqY+meML8ZuF2LRRNEYQ72/c3AzqJRSSnUj28F5JmZaLiIXYk2Je30f5mnISh9T/z3vv5Kp5Xlsr2knbqChI0wwEiMQjuJ16Z0TpZRSQ8sJDc6TZIx5Cfgv4Cd9k52hLbVVfiwWo7m+hjy3g3y3dV+/ti1EPA6tQZ1mVyml1NBzUkE/YQ+woA+2M+QlW+G3tLQAMGbMGDwuO163E4BwNE57Z5T6dh12Vyml1NBzUkFfRBzATcChPsnNMGCMOerZ47J3XemHojGrn36L3tdXSik19GTbev/5DItdwHSgFLitLzM1VFVVVSEiFBUVdQ26k+Owk+u2ijESM9S3B2n0e4jG4jjsfVGRopRSSvWNbKOSDZC0RzvwZ2CpMeZX/ZO9oSXToDtOh2C3Cbku62p/Z20HYGjrjA5uZpVSSqk02bbeX9LP+RgWkhPuPPTyLl44EOHZ5bsAaO4IE47FAdhS1cbre5o4Z3IpJbmuwcyuUkopdRTtV9ZLPp+Pr1zl4yspy17d3cCvXnqblTsb8DjtnD62kG37q2k7vFtH5FNKKTVkZB30RWQa8A3gXGAMcBhYBdxjjNndP9kbHkYXuBlfmgs0EIrGOFzfwuodG5hqb8TtdnHttddq4FdKKTXosrqnLyJLgLeA9wCvAw8knt8LbBKRxf2Uv2Gh0OtiXIkXgHDMsO1gPR3BEIFwlPb2drZv3z7IOVRKKaWyv9K/D1gPXGqM8ScXikg+8Gxi/aK+z97wUJDjxOOw43XZCITjHGiPM0mEzriNPFusq3ufUkopNZiybb0/G/h+asAHMMa0A98H5vR1xoYTl8NGodfJuGLraj8Qs2F3OAmJG4/Hw6xZswY5h0oppVT2Qf8QVr/8TFxY9/dHtNGFbiaUJqr4o4aIOAlIDg6HtpVUSik1NGQb9L8P3C0ilakLRWQMcCdwb19nbLgZlZ/DtFH5AISicTpidmw5+cSMNaiPUkopNdiyvQxdDBQAe0TkdaAW8AHnJP5ekmjsB2CMMR/p43wOeQU5TioKc7DZIB6Hxpib8nA7Uaeza8x+pZRSajBlG/TPB6JANTAh8SDxGuCClLQjstVafo4DEWFUroua9jCdjjxKSt3MO328dtdTSik1JGQ7It+k/s7IcOew2yjJczGxNJea9jD+zgg19U0892odC6ZUaOBXSik16HRGmJNUW1vL+vXrqa2tZXRBDlN8eQBEjRB1uGmLCIcPj/h2jkoppYaA3ozI5wU+inV/vwRoAl4AfmuMCfZP9oa22tpali9fTiwWw263c/6l72NymRX04wj1QSHXY6egVK/ylVJKDb5sp9YdDazEmkp3P1ADTAauBD4tIkuMMbX9lcmhpLa2lqqqKiorK6mqqiIWi+H1egkEAnQ01+N2lpHvdtAeihJwFbFw4Rhs3sLBzrZSSimV9ZX+fwLFwAXGmFeTC0XkPOBPWF36burz3A0x6Vf2S5cuPWqq3U2tLh5Zt5tQNAZAtT/GI2vq+P1bDRTkOLn2rPFcd/b4QT4KpZRSI1W2Qf/dwFdSAz6AMWaViHwD+F6f52wISr+yD4VCLFu2rOvK3+fzMXN8HX9ee5CnNtVgt8EN54ynJM/FskXjsdtksA9BKaXUCJZt0M8Duhth5lBi/SmvsrLyqCv7ZKBPbZlfWeRhqi8fNtUQi8Phlk4KPU5aAmFK89yDmHullFIjXbZBfwfwYeCfGdbdAIyIaeR8Pt8xV/bpinOdlOS6EKwBC7ZWt3HamALq2js16CullBpU2Qb9HwIPi4gPeBxrUJ7RwDXAO7FOCEaE9Cv7dIUeJyKCxykEIoZDTQFcDhv7G4PMqtAGfUoppQZPVv30jTGPArcBpwG/Bv4O/AY4HbjNGPN4b3YqIreLyF4R6RSRtSJyQQ9pl4iIyfCY2Zt99pfUfvoAboedIo+TfI8TgM5onNZAmMaOEOFofDCzqpRSaoTLup++MeZBEfk1MIMj/fR3GGN6FclE5GrgfuB24JXE89MiMtsYc6CHt85J7DOpvjf77Q/prfmXLVuGz+ejsshDQY6T2rYwnZEY+5uCTPfl0xwI4yvIGexsK6WUGqF6vNIXkZtEZIOI+EXkEPAD4G1jzKvGmG29DfgJXwAeMsb8KrGNT2PdLvjEcd5XZ4ypSXnETmDffSq1NX8sFuuaTW9UgZv8HOtKPxo3bDncil2EmtYROYaRUkqpIaLboC8i1wH/A3ixqvOrgc9zEtPoiogLWAg8m7bqWeC847x9jYhUi8gKEbnoRPPQlzK15gfrvr5NBJfDKt4DzQFcDmF/owZ9pZRSg0eMyTwpnoi8gdUdb1nyqlpE7gTuAPJO5EpbRCqBw8BiY8xLKcu/BVxvjJmR4T0zgIuANwEXVqPB2xLbeDlD+luBWwF8Pt/CJ598srfZzMjv95OXd2zPxGg0SiQSwel04nBYd0uMMdy5KkCtH0IGPA547xQnOXZ4R6UTt+PY/vqZtnMq6q4cVfa0DPuGluPJ0zLsG31djhdddNFaY8yiTOt6ii7TgTvTgvsDwJ3AeGBvn+WwB8aYHVhdBpNeE5GJwJeAY4K+MeZB4EGARYsWmSVLlvRJPlauXEm223pl814Ot2/CKjg74RjYCkYzrbKAOTNHMbbYe1T67toGnIp6U44qMy3DvqHlePK0DPvGQJZjT/f0Czm64Rwpr4tPcH8NQAxIj2g+rPH8s/UGMO0E89DvXt9h3du3A2CIGdhc1YrbYedw87FV/N21DVBKKaX60vG67NlEpOtBMo6lLU+sOy5jTBhYC1yStuoSYFUv8j0fq43BkHTOjEpsWIPzJNW0dmITONgUIP2WSndtA5RSSqm+dLybx692s/yNtNcmi20l/Qh4RERWJ7Z/G1AJ/AJARB4GMMbcmHj9OWAfsAXrnv4NwPuxZvgbks4/bRLnTjnMzlo/RR4n2+s76YzEqG7rpMjroi0YpdDr7Erv8/lYunQpu3btYtq0aads1b5SSqnB1VOgvrs/dmiM+b2IlALfACqAzcDlxpj9iSTp09C5sLoKjgWCWMH/CmPMP/ojf31lTGkBcXEww5fP9vp9BCMx9tT5OXNiCTVtnUcF/draWlasWEEsFuPAgQOUlpZq4FdKKdXnug36xph+CfqJbT+A1Sgw07olaa//E2tq32ElEAgSDvopdXuxAbE4bK5qY8nMct6ub2fG6PyutOmz91VVVWnQV0op1edO3b5hA6S2tvaYCXhqa2vZs2cPoRis6zhESW4FDR1RmjrChKNx2jujBMJRvC6r+PWevlJKqYGgQf8kdNfVrqqqCmMMbqeDeCzO2DwbDR3QGYlxqLmTsnw3dW2dTCyz+mVmM3ufUkopdbKyanWvMuuuq11lZSUigolFcdth4YQSAIKRGDtr2sh12tnbEDhqWz6fjwULFmjAV0op1W806J+E7qrlfT4fkydPpqKigve/60Kmjy3DboO4gW017eTm2KlqCeqse0oppQaUVu+fhJ6q5b1eD16vhzmTKlm5ow5fQQ5VLZ20dUZo8IcxxtDgD1FZ5BnEI1BKKTWS6JX+STpetXyx1wVGmFNRCEAwHGN3nR+Xw86BpkDG9yillFL9IesrfREZA/w7cCFQAvw/Y8zmxOA5rxlj0gfsGXEef+MAT6w+cNSyax58neaOMJGoNRJ/KBJn8+FWzp5Uwv6GDs6cWILdduwEPEoppVRfyyroi8gcrMltYsBrwAKsQXMAJgBnAdf1RwaHk+vOHs91Z6ePLQRr9jWxu87PN/9vM5GY4WBTgGAkTiRuaOwIMSo/ZxByq5RSaqTJtnr/PmAbMAn4IJB6aboKOKeP8zUk1dbWsn79empra3v1vooiD5GYYVxidr1gJMa+hg7sIlS3HDsBj1JKKdUfsg365wPfM8b4OXoeGYBaYHSf5moIikajLF++nBdffJHly5f3KvAXe52IGBaMLwIgEI6x+XArBR4Hbzd0HDMBj1JKKdUfsg36PfUtK8MaE/+UFolETnj6W6/LQZ7byYLxxQgQiRl21bUjInR0RmkNRvov40oppVRCtkF/NfBv3axbRvez8Z0ynE7nSQ2VO77Ei9MmFHisZhQdoRgHmgI47MKh5uAJ3zpQSimlspVt6/3/AP4lIs8Cj2NV8b9TRD4LfACrRf8pzeFwnNRQub7CHLZWtzFzdAFv7G0iEIqyo6add84axZs7D9G+6Xni8aOH81VKKaX6UlZX+saYF7HmsJ8E/A9WQ77vARcA7x8p3fVOZqjcEq8LDJw/rQyAYDTO5qoWXHYbNQ3NtIasLn3hcLhXtw6UUkqpbGXdT98Y83fg7yIyFRgFNBpjdvRbzoaJTLPsZeJx2cn3OMjPseN22AhF47QGItS0deJ2OjncEcPYOhAR3G73AB6BUkqpkSKrK30R+ZaIVAIYY3YbY1YlA76IVIjIt/ozk0NVcpa9bFv0jy/xEozEGV9iDb3bEYqxu66DHFuMoKOAvPwCvF4voVBoILKvlFJqhMm2Id+dwNhu1lUm1o843c2y1x1fQQ7RuOGcyaWANdXupsMtjCorBbuTQNyOy+XqdSNBpZRSKhvZBv2exoktBkbkpWl3s+x1pyTXhTGwaEIJdhvEDFS3BBF3LnNnz8JTPp6lS5dqIz6llFL9ott7+iKyBLg4ZdHHReQ9ack8wBXAlj7P2TDQ0yx7meQ47ZTkOonHoSzXRW17mEAkxvo9NTTvfgt/BEzDHkpLSzXwK6WU6nM9NeRbDHwj8bchcz/9MLAV+Ewf52vY8Pl8vQrQE0pz2Xiohfnji3lmSy3BSJzVb9czobOTiHFQ3x5i27ZtGvSVUkr1uW6r940xdxtjbMYYG1b1/jnJ1ymPHGPMGcaY1wYuy8Pb6MIc4gbOm2J13QtH4zSF4nQaOw4xtMRzENFZ95RSSvW9rLrsJQK/6gPFXhcOm1Ca5yLfbac9FCNq7LRKHqNopdOey6Sp0wc7m0oppU5BWffTTxKRUcAxc8EaYw5kSK7S2G3CuBIP1S2dlOa5aQ8FaOmM0UoJdopwEeP6hzdSlPc2156VeapepZRS6kRk20/fJiL3ikgjUA3szfBQWRpfkktnNMZVC4/0grQTp9TWyZnOw1w8KsRfP/UODfhKKaX6VLbV9p8DPgnch3V//17gHqxg/zbwsf7I3KmqLM8NRphUlkuuyw5AHKEzbsNlM+QWl9PUER7kXCqllDrVZBv0/w34NvD9xOv/NcbcCcwCDgN6SdoLHpfVdS8WN0wpz0sstRNxeDj33HMZP3YMexs6BjWPSimlTj3ZBv3JwBpjTAyIYvXPxxgTAf4b+Gi/5O4UNrEsF38oykUzRyFAHMDmwFFQTpHXyc5aP6FobJBzqZRS6lSSbdBv5UjjvSpgRso6B1DSl5kaCUYXeogbmO7LI9dttaeMxuJsqW7DYbMRi8epag4Oci6VUkqdSrIN+uuB2Ym/nwHuFpFrReRDwHeBdf2RuVNZkceJ0y7YRZjus6r4ozHDhgPNxI2hIMfJ1uo2jDGDnFOllFKnimyD/n8DgcTfdwI1wGPA7wEn8Kk+z9kpzmYTJpTm0tYZ5aIZowCrir89FOVgc4Bct4OmjjDNgUhW26utrWX9+vXHnelPKaXUyJXt4DzPpfxdIyJnAVMAL7AtcW9f9dK4Ei+76/xMGZWHwwbROLQFIqzb38KEklycdht7G/yU5PZ89yQ5xW8sFsNut7Ns2TIdxlcppdQxsu2nf6OIlCZfG8tuY8xGIF9Ebuy3HJ7CSnNdgKGmNUg8UYvvD8fYcLCFQCRKkdfJjho/4Wi8x+30dopfpZRSI1O21fu/xbqyz2RSYr3qpRynnfL8HHbW+rGlDLffHoqw9fCRBn2HmgPdbwS6pvRtaWk56rVSSimVKtug39MMMLlY3fjUCZhSnkuORDDxGNZkhhCLxnl9bxMGQ6HHyabDrcTjPTfoi0ajXQ+llFIqk27v6YvIfOCMlEXvFZHT0pJ5gGuAXX2ftZGhotCDOxak3B6iJe6m09jpiMRp6ghxuCXI2CIv1a1Bats7qSj0ZNzGtm3bCAat7n3BYFCn5lVKKZVRTw353ofVUh+sS9Cvd5OuEbi5LzM1kuS6HUyuLMOxsZpCCROJuohhpyMUY+2+ZsbO9+J1Odh8uLXboJ+cildEMMbo1LxKKaUy6ql6/7+x7tdPxqre/2DideqjEhhljPlr/2bz1LZo2lhKysrxlRZSnGuNgRQIRdlS1UZnNEZBjoOa1s5ux+OfOXMm+fn5uFwu8vPzmTlz5kBmXyml1DDR7ZW+MaYVayQ+RGQSUKVd8/pHRVEOLpcTjzOHyRVu/m9DFcFonGAkxtaqNs4YX4zLbmNHTRvnTik75v0+n493vetd7Nq1i2nTpmnVvlJKqYyyashnjNmfDPgi4haR20XkpyLyNRHpdVPxxPv3ikiniKwVkQuyfN/5IhIVkc293edQ5nU58DjtROOGhROLyUsMyxsMR3l9TyMGQ3Gui7frOugIHdtQr7a2lmeeeYYdO3bwzDPP6AA9SimlMuo26IvIt0VkS9oyN/AG8BPgdqzpddcnagKyIiJXA/djTc+7AFgFPC0iPc7UJyLFwMPAimz3NZwUeJxEYnFGF+QwrsS6d98ajNLoD1Pd0olNBJsNdte1H/Pebdu24ff7CYVC+P1+tm3bNtDZV0opNQz0dKX/TuAfacs+CZwO/AAoBM4BIsA3erHPLwAPGWN+ZYzZZoz5NFANfOI47/sN8DvgtV7sa9jwuuzWHwYunjkKl92GATrCUd7Y1wRAidfF1ur2Y2bfS23Il/qslFJKpZLuJnQRkVrgU8aYP6QsewlrkJ6xJvFGEfks8HljzMTj7kzEhTWG/7Vp2/0ZcJoxZnE377sduB64EPgmcJUxJr37YDLtrcCtAD6fb+GTTz55vGxlxe/3k5eX1yfbyuS3m0M0BOK8Y6wDu8Bj28K0hcFlg+Ic4b1TnOQ6heZgnBkldsbk27veG41GaWpq6mq5X1JSgsOR1QjLA66/y3Ek0DLsG1qOJ0/LsG/0dTledNFFa40xizKt6ykyFAJdN4cTAfss4I/m6DOFt4CKLPNSBthTt5tQi1WzcAwRmYvVdfAcY0zseFexxpgHgQcBFi1aZJYsWZJl1nq2cuVK+mpbSY+/cYAnVh9IvHIQdsT42/4oTrsNbHEgSjgOdpcbf04558zyEYrGCIRjvGPBGCtdQm1tLVVVVVRWVg7phnz9UY4jjZZh39ByPHlahn1jIMuxp6B/GJgIvJR4fTbgwroHn8oJdPR5zuhqQ/B74IvGmL39sY/BdN3Z47nu7CNNGYLhGH9adwhfvpuq1k5++MwO2kNRgqEob+5r4vxpZeQ47DR3RNhb38H00fmDmHullFLDTU/39F8GPiciRWJdXn8Ga/bXv6elmw8cynJ/DUAMSL8U9WFN15uuApgF/DbRaj8KfAuYk3j9riz3Oyx4XHZGF+bgD0WpLMphbKJBX0swSiga562DLQAU5zpZf7C5ayKe2tpannjiCZ577jmeeOIJbb2vlFIqo56C/t1YV/q1QAtwJfCgMWZ/WrprgFey2ZkxJgysBS5JW3UJx9YggFXbMBfrxCL5+AWwO/F3pvcMazN8+XSEYwjC4unleJxWg77OcIxXdjcQjcdxO+yEY4Y99X4Atm/fTnt7O+FwmPb2drZv3z64B6GUUmpI6mlwnr2J8fdvAYqB1caYR1LTiMhorMD7cC/2+SPgERFZDbwK3IY1st8vEtt8OLH/GxNjAxzVJ19E6oCQMeaU6qufNLowB4dNiMbizK4soMjjIhjppDkQxuu2s6OmnTmVhZR6Xbx1qIXJ5Xkkm1ikPyullFKpemzibYw5gFWd3t36GuDTvdmhMeb3IlKK1c2vAiuoX55Sg9Bjf/1TndNuY7ovn5217ZTluVkwoYjGrbWEY4ZgOMaLO+uZXVmAy2Fj1ZZGfvXSHhxi2Bc4HYACe5RX1xnu2/QK1551dJsBpZRSI9ug9OsyxjwAPNDNuiXHee9dwF19nqkhZFJZLluqWgE4d3Ipq/c20eAP0xII43U52NcQYFJZLpedNprzppby/gVj+PKTawgEAtzzvtlDuvW+UkqpwZPVMLxqYBXnuijNc+EPRako9DDTl4/DBpE4dEaivLCjDoPBabcRicV5u86P1+uhrKxUA75SSqluadAfomZXFOAPWfMbXTCjnIIcJwBtwSiHmwPsbQgAUJrrZsPBFqKx+KDlVSml1PCgQb+f1NbWsn79+hPuPldR5MEmNqLxOJPLchlX4sUGBKNxonF4bmtN19W+CFQ1+WloaNTuekoppbqlQb8f1NbWsnz5cl588UWWL19+QoHY7bAzbVQeLYEIgrBkxijyPVYTjJZAiLq2ELtqrS579kiQPYdqOFhVc8L7U0opderToN8PqqqqiMVieL1eYrEYVVVVJ7SdyeW5hBPV9rMq8vEV5CBARzhONB7nuW21xI2hpbmZWNxQH8uhOmg/4f0ppZQ6tXXbel9Enu/FdowxZmkf5OeUUFlZid1uJxAIYLfbqaysPKHtlOS6KPa6CISjeF0OFk8vp66tk5ZglCZ/GIfNxvaaNnDk0hRzE4/BU80VXO4s6tsDUkopdUro6UrfBkjKYyawBGuUPk/ieQkwI7FeJfh8PpYtW8bixYtZtmzZCbeoFxFmVxTQ1mk16Js3roiSXDc2se7tR+Jx/rW1loPNnRisDyxihC01/TIVglJKqWGupxH5liT/FpH3A/cD5xpj3khZfjbWhDj3918Whyefz9cn3efGl3pZs9/qmpfjsHPO5BJaAmGaAhEa/WGcNqGlPYIABgGBSEfryR+AUkqpU0629/T/A/hmasAHSLy+C7inj/M17J1s6/0kp93G3DGFNAbCAJw9qZQirwuHDULROOGYYW8blDrCFNojnJ3fit1bSHuidkAppZRKyjboTwPqu1lXB0ztm+ycGvqi9X6qyeV52IBoPE6u28H508oo8LgAaPSHCMfBnVfI+LJ83rvkHEqLCnhzb5OOwa+UUuoo2Qb9vcDHu1n3cWBfn+TmFHEirfd7qhnIcdqZObqA5sTV/lmTSijNdeK0C+GYIRI1NAXjOD25FBVZ9/0PNgfZ16D39pVSSh2R7dj7dwOPichm4I9Y0+36gKuwGvhd3z/ZG55623o/WTMQi8Ww2+0ZG/9N8+WxtaqNuDHkOOxcNHMUf153mAZ/mLr2TnKcdg41BTAYBKEs18XqfU2MLvTgcdmPm+fa2lqqqqqorKzUoXyVUuoUldWVvjHmSeBSoBX4KvCzxHMLcKkx5vf9lcHhyOfzsXTpUiZNmsTSpUuPG0SrqqoIh62r+HA4nLFmID/HyZRRuTR3WOkWjC9mVL4bj9NGzEAkFqetM8rOxIA9bqcdYwzrDzQfN7+1tbU88cQTPPfcczzxxBM6uI9SSp2isp5lzxjzL+BfImIDyoAGY4wO+J5BbW0tK1asIBaLceDAAUpLe54Ix+12EwgEMMYgIrjd7ozpZo4uYHedH2MMTpuN0QU5XUE+HDMY4vxkxS5GFbgRhAumlRGOGiaW5VJZ5Ol2/9u3b6etrQ2Azs5Otm/frlf7Sil1Cur11LqJQF/XD3k5ZaTe0w8EAlRVVfUYREOhEF6vF7vdTiwWIxQKZUxXnOtibLGXRn+IIq+Lq88cR1Mgwr6GDlqCERw2oTzfzYXTy1kyfRQAgXCUVbsbeM+8SnKcmav529vbj2r0197efhJHr5RSaqjKehheEZksIneIyAMi8j9pj9/0ZyaHm97e06+srMTlslrju1yuHtOfNqaQQCSGMQa7zcalc3zk5zhw2CAYiROJGl7Z1UBNWxAAr8tBJB5n9d7Gblvz5+fnIyJdj/z8/BM8cqWUUkNZVlf6icF5lmOdJNQB6Zei2jcsRXJEvmwbxvUmfXm+m4mlXqpbOinNczN1VB7TffmEojHq2sPUtncyvsTL/64/zMcumIzDZqMs182ehg4qizxMHXVsQJ85cybr168nHA7jcrmYOXPmSZeBUkqpoSfb6v3/AFYC1xtjuuuvr1L0dkS+3qSfN7aY/Y2HiccNNpvw7rmj2dvQQUcoSkc4TksgQtwYXt7VwEUzRiEi+PJzeH1PE2V5boq8rmP2fe2112rrfaWUOsVlW70/GfihBvyhodDrZGZFAY0Bq8KlyOPiohnlFHpdCNASjGAMvLq7gaoWq5rfabfhcdp5ZVcDkdix7S99Ph8LFizQgK+UUqewbIP+dqC0PzOiemdOZQHGQDQRwEc7AriiAQpzrI80Gez/vP5w1/S8hR4nLcEwGw62DEqea2trCQaD2iVQKaUGSbZB/8vA10Rkcn9mRmXP63Jw+tgiGjvCHDx4kNWvv4YvdIhYZwCPA2IGGv1hmjtCrNxxpLPFqPwctla1sbfeP6D5TQ5A1N7e3idDEyullOq9bIP+XVhX+ttEZLOIvJT2eLH/sqi6M82Xh9Nu48ChKowxFDlilNs68Nji2ATaQ1HCMcPrexrZVWt1w7OJUJ7n5tW3G2jwZ+4a2B+S3RhtNlvWQxMrpZTqW9kG/RiwA1iFNfFOLO2hg/QMArfDzhkTisgpthrrxWIxxjvaKPS68eVbA/zUtHZit9n48/pDXWP3uxw28txOVm6voyMUHZC8JrsxxuPxrLoxKqWU6ntZtd43xizp53yoEzS5LI/Tp03A6xDaGmuprKwk6inh0df3U+hx0BqMUtMaxFeQw/I1B/m3d0zCZbeR53bQ1BHi5V31LJ3lw2nPesiGE5Lslrhu3ToWL16sDQaVUmoQ9O8vvep3NptwzuRSisp9LDzzTMaNG8ekslzOnVJKfo6za9CelkCY2rZO/rm5GpMYVqEk102DP8SafQMzDa/P58Pj8WjAV0qpQZLt4DwXHi+NMealk8+OOhHFuS7mjSvirUOtVBTkALBkRjlv1/sxxmrJ3xKM4nHZ2XCwhfElucwfVwRYDft21rbjddmZN654EI9CKaVUf8t2cJ6VHH/UvePP36r6zayKAvY2dODvjJKX48Bhs3HlGWN58KU9VBTlUNXSSXVriAklHv62sYqSXCfjS3KxiVBR4OHlrQfZu3sn586e2G9X4qld9vRqXymlBl621fsXARenPT4E/A7YB7ynPzKnsue02zh3SimtnRHicev8rCzPzWWn+XDYhLJcaxS+Qy1BbAJPrD7Q1Xq/ra2VnRve5C+rtvKLx/7cL93ptMueUkoNvqyCvjHmxQyPPxtjPgr8FXhv/2ZTZWNUfg6zKwuo7zjSFW/B+GLmjy3C67aT67IRi0N1ayfRODzy+n7aOyM0NTURj0XJtUXZ0+Fiw84DfZ437bKnlFKDry8a8v0dWNYH21F94PSxheS5HbR3RgAQhMtPr2BssZfSPDcuuxCMxKlr66QjFOXx1QeI2+yEQiFCwQD2SICN9VGqWgJ9mi/tsqeUUoOvL4L+DLSf/pDhdti5cFo5HaFY1xj7DpuNqxeNIz/HSUVhDg6b0BGO0dQRpq4txLM7W3G63Hi9Xrw5Ljx2w4ptdRxs6rvAn+yyl5+fz7Jly/SevlJKMfDDk2fbev/GDItdwGnAzcCf+zJT6uQU57o4d0opr+xuoKIwB5sIuW4H1509nt+8vJexxR4ONAVo64zi74xy2AYb4pXYMOTaYqzf2oFt+x5W723i3981g4lluX2SL+2yp5RSRyTbOvl8PpYvXz4gF0TZtt5/qJvlIeD3wGf7JDeqz0wuz6WuvZO3GzoYnW914/Pl53DVwrH8/s0DjC/xsr8xQBwocjtoD4ZwEeW83HqWLp5NUVER4WicF3fWE40bpo7KG9wDUkqpU0ymtk79HfSzrd6flOFRYYzxGGNuMsa09lcG1YkRERZOKKEwx0lrMNK1fLovn8vnVhA3hnElHgCaglGiQBAH6zoK2XfgIGAN1zsq382ru+vZUtU6IAP4KKXUSDEYbZ2ybb2/P8ND+1wNcS6HjQunlROKxgiGY13LF04o4dI5owEYV2wFfoOdODbq4rk8sy9COGq1B3DabfgKclizr4nX9zR2TeWrlFLq5AxGW6deNeQTkfeIyA9E5DeJ5yv6K2OqbxR6nSyd5aMlGO4K5ABnTypl6UwfIlDksWONvSQYhPqQ8LvX9tGemIzHYbNRUehhd52fF7bXHXUCoZRS6sQNdFunrIK+iOQnps/9K9b9+8sTz38VkZUiojd8hzBfQQ4XTCunzh866kr9vKmlXDitnBynA5DEUiEUNdS0dvLrl9+mrr0TsKbkrSj00BQI888tNTR3hAf+QJRSSp2UbK/07wXOAD4MeIwxFYAHuDGx/N7e7FREbheRvSLSKSJrReSCHtIuFpFVItIoIkER2S4iX+zN/hRMLMvlnEkl1LR3do3YJwiLZ5RzyWwfHqcNt0OwiTVBT01bJ+2dUf7nlb3sbejo2k5prhuM4e8bq9ld59f7/EopdRIGustetkH/SuAbxpjHjDExAGNMzBjzGPDNxPqsiMjVwP1YJwoLgFXA0yIyvpu3+IEfAxcCs4F7gLtF5PZs96ksM0bnM29sETVtncRNSuCfPoqJZbk4bDYmlubictgIReMcag7SGY3z2Bv7eWNfY9fsfPk5TkrzXLy6u4FVbzcSimZX3T/QX+7eqq2tZf369UM2f0qpU8tgDE+ebdAvBbZ2s25rYn22vgA8ZIz5lTFmmzHm00A18IlMiY0xa40xTxpjthhj9hpjHgWeAbqtHVCZiQjzxhYxq6KAmtYjV/wA5XluppRb/fHHFnvIdzuIG6zAH4nxzOYa/rjmEJ2JAO+026gszGF/YwdPb6rpGse/O0N97P1k/l588cUhmT+l1KlnMIYnz7af/l6sSXWey7Du8sT64xIRF7AQ+GHaqmeB87LcxoJE2ruySa+OZrMJiyYW47ALv3llLxsPtiAiXevDsThNHWGMMeS67HSEY9S0hSjIcbCtpo3qF4N86MxxVBR4EBFG5efg74zyj43VnDamkNPGFOJyHHsuORj9UXsjmT+v10sgEBhy+VNKnXoGo8ueZHNPVkQ+D9wH/BZ4DOvKfDRwDXAL8AVjzP1ZbKcSOAwsNsa8lLL8W8D1xpgZPbz3EFCOdaJytzHm292kuxW4FcDn8y188sknj3t82fD7/eTlnTrtFY0x7G+Ls7slTpEb4tEokUgEp9NJCAcvH4rSHIpjDDR2Wm377QKFLnDahfmj7EwvsmGzWScMcWNoDRly7MKsUjvFOUcH/mg0SlNTE06nk0gkQklJCQ5Htuec/S+Zv6Shlr9Up9p3cbBoOZ48LcOTF41GCQQCeL3ePvvNueiii9YaYxZlWpfVHowx/yUi5VhV8zclFgsQBr6XTcDvAxcAecA5wPdFZK8x5pEMeX0QeBBg0aJFZsmSJX2y85UrV9JX2xpKtle38X+vbWHH1jXYiCMinHfeeXxu3hie3VLDugPNFOXbONwSJBiJ0xSCklwn29rstNo8vH/+GMry3F3b6whFaQlGGO3L4/SxReS6j3zFamtrWbduHeedd96QvIqura2lqqqKysrKIZm/pFP1uzjQtBxPnpZh3xjIcsx27P1C4NvAD7CCbgnQBLxujGnuxf4agBiQ/ovqA2p6eqMxJnkLYZOI+LCq948J+qp3ZlYU8Eq4jk5jw223Y49HqKqqYty4cVxxegVjS7z8Y2M1FYUe/KEIde1hmjrCtAetWwG/fGkPS2aUc86kEuw2G7luBx6Xnf1NAfbUdzBvXBHTffkZq/yHGp/PN6SDvVJKnazj/hKLiANoBC4xxjQbY55OtOJ/upcBH2NMGFgLXJK26hKsVvzZsgHu46ZSWTnv9GlMcrYSi8UI4ui6ryQI88cWccsFkyjLc+Nx2plY6sXtsBGJQ01riEZ/iGe31PKLl/awt6EDg8EmQlmum9JcF+sPNPPXDYdZvX0fjz3+BG1tbTzxxBPaUE4NqqHek0Sp/nLcK31jTFREarGu0PvCj4BHRGQ18CpwG1AJ/AJARB5O7PfGxOtPYzUU3JF4/4XAF4EH+ig/I97s2bO5/irYsn0X/txKHAVlxOIGe+J+/aj8HG4+fyKvvd3Eyl11jCny0JG46m/rjOIPRemMRHnk9X1MG5XPpXNGU5LrwmG3sbPWz8oddXQEg7R1TsJeZyOPSv7ym3Xk5+dz7Vnjue7s7nprKtX3BmNmM6WGimxbDTyK1WDvHye7Q2PM70WkFPgGUAFsBi43xuxPJEmPAHbg+8BEIAq8DdxB4iRB9Y3Zs2cze/ZsYnHD5sOtvHWwhfwcB/k5TgDsNhvnTytj+ug8/rK+iljcMKnMSX17iPZQlMaOCK3BCIFQjLfr/Zw5sYTzppSyeHo5i6eXs2nzJn6/vhWXx8uESB2XnV7KlZeeS47TPshHrkaaqqoqwmFrRMlwOKw9NdSIkm3Q3wdcJyJvAv+H1Xr/qGb/xpj/yXanxpgH6OZK3RizJO31fwP/ne221YlJbcQ2b5yPMcUeXt3dQE1bkFF5OV2t9JNX/RsPtfKvbbWU5jopyXVR29ZJZzROQ0eYts4IL+yo4819TZw5sZjzppRRVFhEjAY6YzaCxkFV2Muf1x1ixuh8ppTnUeR1DXIJqJHC7XYTCASIxWIEAgHcbr1TqEaObIP+zxLPY7D62aczQNZBXw0tyerOWCyG3W7vqu68fG4FGw+1sPlwGwVpV/0Lxhczq7KAl3c18MbeRkYX5BA1htq2TsIxQ4M/TEvAOgF4c18z5a4oTcaLhIUORjMvEqUk18WuWj9bq9rwFeQwY3Q+owtzcDsG5+p/uLTeVycnFArhdrux2Wy43W5CoZ4HllKqP6W2LxmI351sg/6kfs2FGlTdDUzjtNtYOKGEcSVeVu9poqo1SGmuqyso5zjsXDLLxxnji3lpZz1bDrcyptBDOB6noT2cGOgnQksgQrVdiGHDZiCC0GbcOGw2yvLcGGPwh6K8vKsemwjjS7xMGZVHWZ4bp31gWv13d+KjTj3JQB+Px7tOAJQaDIPRviTbfvr7j59KDVfJUaECgUDGUaFG5edw+dwK9jZ0sGZ/My3BCGW57q6GfqW5Lj6wYAwXTCvj5d0NbDncSkVhDnFjaPCHCEbihKLW3aA4EMHGmwfayM9rYE5FIYUeJ/k51iMeN1S3drK3IYDdBuNKvEwszaU8392v9/91RL6RIxQK4fV6sdvteL1evdJXg2YwRirt9fA/IpJ+6WWMTrU2rPl8PpYtW9Zj1bbNJkwZlceYYg/bqtvYWtWGCFYrfZv1lSjLc/OB+VbwX72niQ0HWxiV78YATe2d+CMGEAzCgaYQf1hziKdd1Uzz5bNoYgmTy3PJcdgpTtzfj8UNta2d7GvoQEQo8ToZX5rLqHw3xbmuPq0FON6Jjzp1VFZW4nJZ3zGXy6WftRo0gzEMb7dBX0RGA78Bfm+MeTixzI41Cl8qv4hMN8Zoh9dhLNuBaXKcdhaML2bG6Hx21LSzrboNgBKv1UUPoCzXzeVzK1gys5xNB1t59e0GTNxJqCWIQYhjiGOnvTNKe2eUen+IDYdayHU5mDE6j3lji5lcnkuuy0GR10UR1rDBwUiMTYdaiMatsQBK8lxUFuZQlp9DocdJrst+1DwCvT3+4534qFODz+dj6dKl7Nq1i6VLl+pnrQbNYHwXe7rSvx04A7gqbbkAvwKqEn9fjdXX/u7+yKAamrwuR1fw31XbztbqdmIxQ4HHgddlfa28Tged0Tj+UJSOUBSIY3AABocNkpP8RePQFozSFoxS29bJm3ub8brsjC/xcvrYIiaV5TK6MAev68i248bQGYmxtaqdmLFOPJx2oTTXTXmBmxKvizy3A6/bnnXDwOEwIt9AN/o5FdXW1rJixQp8Ph8rVqygtLRUy1INitraWp555hkqKyt55plnBuS72FPQvwz4lTEmmLbcAL80xqwDEJF64EY06I9IXpeDeeOKmVVRyMGmAFuq2qhuDeJ22CnyOLv66b/xxhvs3bufstGVbK1qwe8ZTdyZhwGi8TidkRhtnVEiMUNHOEZHOEa9P8ymw614XHZy3Q6mj8pj5ugCxhR7GJXvPuokgMR2/KEo9YdDxOIGEevLmuOwWTUGXidFHicetwOP047HacftODJp0FBXW1vLo48+ypgxY3j00Ue54YYbNFidgKqqKoLBoFV7FAxq+w014B5/4wBPrD5Ae3s71W2TsDfY8Zgx/N8ADFrWU9CfAXwrw/L0X8idibRqGDvZ7mouh40po/KYXJ5LfXuIXXXt7G8MEDeGXJeDiooK9u3bh8sGPnuQDyyqoKDMx+46PxsOtlDd2kl+jpNIzBCOxggkAn84ZggHo7QGo1S1dPLq7kZyXHY8ThuVxV5m+fIZU+zFV+CmyOskz+0gz3301zoas04GmjrCRGLWzIEiBmMEBHKcNvLcDnIT7811Ochx2nE6BKfdhtNm6/rbYZMTvoVwstasWYPf77d6O/j9rFmzhiuuuGJQ8jKcBYNBgsEg8Xi862+lBtJ1Z1tB/e9//zv/+Wo7klPAvOg+3jVvDldccX6/7runoJ8D+FMXGGNiIlKBNXFOUmcirRqm+rK7mogwqiCHUQU5LJoYo6a1kx017XQUlTNuxmkE21uZe/pcxo0bB8DCCSUsnFBCRyjKvqYOdlS3s6uunTy3k5iJE4nFCUfjBMIxgpE4kbgh0hmlvRPq2sO8daAFl8OGy2Ejx2HDV5DDlFF5jCv2UpLrpDjXTZ7bTp7dkXG2BmMM0bghHI3TEQpRFQsSjae0SxXAHD0Slctuw+W0kWO34XTYcCWe3Q4bDlvi2S7YbYJNjjzbbGCXxN8iiI3E39aziDXfgU2sckw+J7W3tx+V9/TXKjv19fVd5Soi1NfXD3KO1EjVFPdQFS9AgjaazBQWxT39vs+egn4dMBl4JXVhhgZ7kwD9XzOM9Vd3NbfDzoTSXCaU5rLvUBVvr9qBO7+cjTv348gvo7ykiHy3A4fdmp1vTkUhcyoKicXj1LaFONgcYGdtOwebghR4IBaPE4kZIrE4oYh1EhCNG0LROKFonHag3h9mc1UbDhvWlbndhttuozTPxejCHMYVeynNc1PgsQYbynM7rKCdZU8AYwwxY4jHIRIzdEajxOOGuDHE4oa4sdobGKyeCtB13mC9Sq0kMBn/TFlivdMmNuw2qPFMZHe0AX+Tg9poKaW5k/m/DYcRARvWSUPy5AFSThyS+UicRCSzYOtKdyRNav5siQ2l3/2Q5JtSl8mxVYBWWklPmpmk/nn8N6Sm6G3FS6xoHPXx3bg7bNTHc4kVjWP9gV7NHXbcPA0HfdHl6u2WGIV9UHYj1duRQuL4cQGIjVDu4DbkewX4MPDwcbZxI9bEOWqYGojuas31tRTaQowthjx/K1O8nRSUeDnYGCAUi2HDah+Q63Jgt9moLPJQWeTh7EmlRONx6tpC1LQF2VPfwf6mAMFwDBEhHI0TjydqAGJxOiPWCUA0bt3jJ2KdDDR0hNlRa1VcOWzgtNmw223YbNY9/0KPkxKvm7J8N+V5LvJynOS67eQ47Xhc9q42ADYRHFaUHRDGWKcPGCicMoHaw/ux2WBMRTmTJ4yzTi4SNRHG0FVLkXgXyc60XT/wXa8znGakp82Ql6zyfNxjymozfSaZ7zf2NvHGnkYAGiIzMW8birxzWLeqGVY1c/bkUs6eVDKwmRvmDvnj5NVojdOJKsrPR2x2InFDSb6Xd8wa1+/77Cno/xh4RUR+CNxhjImmrkxMufufwBLggn7Loep3A9FdLbU/qtthY/608fh8pZw9sYTWYISG9hCHWoJUtwaJG+uqKcdpJ9dlx2E/chJwxvgSDAZ/KEZ9eyc1rZ0cbApS1RLAH4piz7URixvCsTgYQ9QYItG41TYgatUMdJ0QROMAtGHdKoCOrvzaBex2Gw6xhh222awrULdDyHHa8Sbu/+e7HRR4rBoDj9uB227d/3fZbV01CFaNQ6J9gF1w2ASbTbK7ok1emQtsWL+euppqxlSOoaamms2bNnDGgjP6/LM6VV0+t4LL51YA8PBr+2hsauLzVywY5FwNbwUuoTRPRzQ8UfZokGIJ4sxxssR1gBLbfKCgX/fZbdA3xrwmIl/GCuw3iMhzwIHE6vHAJUAZ8FVjzGv9mkvV7/q7u1ryxGLdunUsXry4a182m1Cc66I418W00fnE4obWYIQmf4jqtk5qWzsJJYKzTaTrqjvf7SDfncfksjyYYu2jMxqjuSNMgz9MbVsnNW1B6tvD+DujiSBrBftYojo+HoeYMUQTtwzCsTjRmHUNHDMQi8YTg1LEszrG5L15m02wg3WiIHLU/XnhSHW60y44Eo0DXQ4bTpvgtNu7ThqcDsFtt056HHZh5+4WotEi2tvstEULaHq7kUhRY1e7AJEjbQi6qvoTZSyJ/JFS5W+9J1EFbzuyDICU93dV0SduIwAptxCOVGun3idPbOLo0xpJeV9KOtLSCZnvFaS+95h13XwmqXlJ1R6K0NwZ52BTB2NLvJnfO+wq7NVwc+jQIWzxKEUuBzmdjWzfvr3fe5LI8arsROQi4CtY89gnG+x1Ai8B/2mMeb5fc3gSFi1aZNasWdMn21q5ciVLlizpk20NRQM12Uxvy9EYqwufdSIQprYtSL0/TCwe7wpGbod1IuByZK5zj8bjtAQitAQjtAUjNHaEqW/rpClgvY7FDSYeo62tDQPEEfJy87DZ7RhjnSBYDf6sdgXJ51jcuo8fG9Dq6tSdWUHJbrNqI5JBNvWe/pGAfHQwPSpd4t/UNgdy9D9HtpOenUzBOeWPZHuGbpJmWJ76ju6cXDCOxA1NHWHAYMM66XQcp9tmpnYDx81FjwlO/oTimDYig6Czs5OcnKPbceup0vF1hKL4Q7HE70sMr03wECQvL79PuuyJyFpjzKJM6447DK8x5gXghcRofKWJxY3GmNgJ50gNKUN5shkR6eqGN6bIw1wKiccNHeEo/lCU1kCE+vYQ9R0hmgLWdXkyoLkdVj98l8Oa2KcsQzWkwWoIuPatLWzpqCdibISxUZTvIaewCH8wij8cJRC2GuyJOLoauFknA8feH0/eX0+eUMdTGvgdebZqGuJdJxVWfYKJG+s5kTZDiRyzJJY4CVG9JcSBxo70QUZVr3R2DnYOhjk7Re4oH8g9wIc//OGhM/Z+IsjX9WNe1CAZbpPN2GzSNUFPRaGHmdZtWiKxOB2hqFUzEAjTFAjT0hGhwR+y2gmINQqgXRLd7hw2Xt/TxKu7GwgGIzRFRgPgkSj1zXE8nX4uTAwuZLDaBHRGYgQiMTrD1t+dkRid0Rj+TuvMPRCO0pnoWRCKxghHrW6Hyers1KtvSJ4gWC3x4skWeSmt8Y9UgRtCUUNVSxCD1RjRV5CDy2E70jDOAGKOaZAXNxxp7EfKepPe2O9Io8GjnjmynrTlRyoKTcq/R9Ydc95yTI+Fo/OQjfSkvZ36IxKNEU15i0PAYc9+MqehMNHIUMgDQDwex2YboFatpyJjiMRiuDA4HL2eCueEDMxe1JB2qkw247QnR96DMUVH+rvG49a4/cFIjGA4RntnhNag9ThjfBGzKwtob2vjdy9uIx6PM8vbzvnnnktpcRFOu41ILI7DJomaAzuFvexKazBEooZQzDoBSD7CUUM0FicSj3e1K4jE4l1pkycMkURbgwPNgaPvk4vgcdqJGesYk10GU2seBCF510MkuURSOhRmewypFe8GjFj7Sa+NTyRMPRnoMUBlkZH0O/8nW2seDoepa+8EseE2EQrzcnG7XEfflzixTY84gUAAr7f/+5afqkKhTpr9YSYk2u4NyVn21KlnoCabGaxx4202ITcx4l4mkVictzZt5bF4mE7jwB4OUOoMU57vxh+O0tEZpTPRmFBSglwcg10Eh82G3Wa1ynfYrIZ09pSR+wTB5ZBu2xxka+uBWn5c2woiuIlx9bxxzB5/bDkajHVLAbrGEEi9lWDdYkgZcyBZ05AcYyB5uyFxApG63kDX62QtReptiK5bHuZIsE/tdmi1meha0fUe6zmRJpE++Z6uGhBIWW6O3EY56uBT3pt8nXixp6GDPfVWDw2Py0ksFkfsOQSiEIiGmViWx6Sy3K4dZbptk1rG6WczPdVqHE+mpKa7FSfo2LqYk1dXF6S8PL9PtzmShEJO9uxvw+e2Y48N8ix7amTp79b7yXYDPp+P5cuXD6l2A067jdVb36bW5GMQ1kTHsujwQT64+Eh3uHiiG2AoeuQKPFndH4xYwwYHw1Z1vz8cJRSJW1e8cbpavlsBDRDrh9e6CrdODqxR+ziq9X1y9L5kNX9OtIMyRwhjdzPD0UhONHP5CYkTDxiw8QSGm5aWFt5++22mTJlCUVHRYGdn2Nq0uZm5p40d7GwMa2+PL8TWvJ//t3hgfhM16KsBkWw3YLPZiMViQ67dQIutAEM7duLEEFpsR/eVtdmEHJs1WE82ksP7RmOGaGIkwWiiGj8Wt9aFIjGryj9qdRc8Up1vVfUHozEiMcNrexpZs6+JcCRMMGrHIbC2o5ht61pxbtrMmRNLOCtlUJkjV3TS9Wx12bNqHrq6D8rRXee6RuhLNLtPfd3VNU+OvE46annX34l1gzRPQU9aWlp45ZVXKCoq4pVXXuH888/XwK8GTUeHn5xIlMbGRg366tSROjjPUGw3MMuXi4124thwSJxZvtyT2p6I1Q/fOkfIvpFYJtecNY64gbXr1vOvFSsYO248+w8cZPGSizht7txE9X33PQOSr60xCqyW/ga6Tj6SJygk3p9sI2CwTlAw1ngGJqX6P3l74Oiqf2s/yZOOuDEppxzWsqPu0B917z/Lcj2RAkx708HD9bRFBFtUaIsIuw/XM86eYfqQ7vJ2vExkeUzp50MnVPE+yC36mjsNtW3aev9EVVdX8ea6tzhrUjF/+tOfAJg9e3a/7lODvhoQ3Q3OM1ScN3sC41/cSzBmY2lxE+fNfsdgZ6mLiGAXyPPmEAt34pQ4RIKUFXgoyXUNdvaOy5ijexR09RroWn/k/nmmk4DU3gXpaY7uLdD9PfjU99WPdvC/VW9RXpRHfSTI+8+axKjyUT02OTzeycnxYm9vexicyD5OevsnsIO81j2cN2d032dmhPjnwQ1MdjQxKqeIvcawa9cuDfojxUANjqOOlpzXGoDcEmydIVbFJrN++S5gV7/Oa91boVAIr9eL3W7H6/USCoUGO0tZSd5KSFkyWFkBoHDCGG685irWrVvHu5cOvRPQ4aTQLZTn6zC8J2rhnGns274RhxhEhGnTpvX7PjXoDwFDeXCcvjJUG/Il57Wura3l8ccfJ+KO4HQ4uW7ZdUMif6kqKytxuawre5fLNeRukQwnPp8Pj8cz5D5jNbIkr+p37drFlVde2e9X+aBte4eE1MFxko3cTjWZGvINJdu2bcPv9xMKhfD7/Wzbtm2ws3QMn8/H0qVLcbvdLF26VAOWUqeA2bNnU1hYOCABHzToDwmnyuA4PRnqDflEJHHv2XoMxVbntbW1rFixglAoxIoVK6itrR3sLA1bqWNGKDWSaNAfApKN3BYvXjxkqr37WvIY8/Pzh+QxlpeXHzVLXHl5+SDn6FhDvbZkuEjeampvb2f58uUa+NWIovf0h4j+HhxnKBjK91FTG8nFYrEh2UhuqNeWDBdDfcwIpfqTXukrxfBoJDfUa0uGCz15UiOZBn2lONJIbtKkSUO6kdxQri0ZLrRBpBrJtHpfKY40kovFYhw4cIDS0lINBqeo5Gft8/lYsWKFftZqRNErfaUYGd0mlaWqqopwOAxY0+zqZ60G00D3JNGgrxQjo9uksrjdbgKBALFYjEAggNutI8qpwTEYPUm0el8pjjSS06GQT33DdThjdeoZjJ4kGvSVShgJ3SbV8OipoUaGwehJotX7SqkRRbs+qqFiML6LGvRPEbW1taxfv15HFzvF6fCxfUO7PqqhYqC/i4MS9EXkdhHZKyKdIrJWRC7oIe0HReRZEakXkXYReUNE/t9A5neoSzYGefHFF3VY0ZMw1E+cdPhYpdTJGvCgLyJXA/cD9wILgFXA0yLS3aTli4HngSsS6f8B/G9PJwojjXY3O3nD4cRJx95XSp2swbjS/wLwkDHmV8aYbcaYTwPVwCcyJTbGfNYY8z1jzGpjzG5jzN3AWuD9A5floU27m5284XDipMPHKqVO1oC23hcRF7AQ+GHaqmeB83qxqXygua/yNdxpd7OTNxxOnJKf87p161i8eLF+zkqpXhNjzMDtTKQSOAwsNsa8lLL8W8D1xpgZWWzjk8D3gNOMMfszrL8VuBXA5/MtfPLJJ/sk736/n7y8vD7Z1kg2lMsxGo0SiURwOp04HEO3N+tQLsPhRMvx5GkZ9o2+LseLLrporTFmUaZ1Q/eXLQMRuRL4AXB1poAPYIx5EHgQYNGiRWbJkiV9su+VK1fSV9saybQcT56WYd/Qcjx5WoZ9YyDLcaDv6TcAMSC9XtIH1PT0RhG5CngEuNEY81T/ZE8ppZQ6dQ1o0DfGhLEa4V2StuoSrFb8GYnIMqyAf5Mx5o/9l0OllFLq1DUY1fs/Ah4RkdXAq8BtQCXwCwAReRjAGHNj4vU1WAH/i8BLIjI6sZ2wMaZpgPOulFJKDVsDHvSNMb8XkVLgG0AFsBm4POUefXp//duw8vnfiUfSi8CS/syrUkopdSoZlIZ8xpgHgAe6Wbekp9dKKaWUOjE69r5SSik1QmjQV0oppUYIDfpKKaXUCDGgI/INNBGpBzIO4nMCyrDGGVAnR8vx5GkZ9g0tx5OnZdg3+rocJxhjyjOtOKWDfl8SkTXdDWuosqflePK0DPuGluPJ0zLsGwNZjlq9r5RSSo0QGvSVUkqpEUKDfvYeHOwMnCK0HE+elmHf0HI8eVqGfWPAylHv6SullFIjhF7pK6WUUiOEBn2llFJqhNCgr5RSSo0QGvSzICK3i8heEekUkbUicsFg52moEJELReSvInJYRIyI3JS2XkTkLhGpEpGgiKwUkTlpaYpF5BERaU08HhGRooE8jsEkIl8VkTdFpE1E6kXkKRE5LS2NlmMPROSTIrIxUYZtIvKaiFyRsl7L7wQkvptGRH6askzLsgeJsjFpj5qU9YNafhr0j0NErgbuB+4FFgCrgKdFJH0K4JEqD2t65M8CwQzrvwz8O/Bp4EygDnhORPJT0jwOnAFclnicATzSj3keapZgzTp5HnAxEAX+JSIlKWm0HHt2CPgK1jEvAp4H/iIipyfWa/n1koicA9wKbExbpWV5fDuwpo5PPuamrBvc8jPG6KOHB/AG8Ku0ZbuA7w523obaA/ADN6W8FqAa+HrKMg/QDnw88XoWYIB3pKQ5P7FsxmAf0yCVYx4QA96r5XhS5dgEfFzL74TKrhB4G7gIWAn8NLFcy/L4ZXcXsLmbdYNefnql3wMRcQELgWfTVj2LdVWmejYJGE1K+RljgsBLHCm/c7FOFlalvO9VoIORW8b5WLVwzYnXWo69ICJ2EbkG6+RpFVp+J+JB4I/GmBfSlmtZZmdyovp+r4g8KSKTE8sHvfw06PesDLADtWnLa7E+ONWzZBn1VH6jgXqTOJ0FSPxdx8gt4/uBDcBriddajlkQkbki4gdCwC+ADxhjNqHl1ysi8jFgKvCNDKu1LI/vDeAmrGr5j2Ed8yoRKWUIlJ/jZDeglOo7IvIjrKq8840xscHOzzCzA5iPVTV9FfA7EVkyiPkZdkRkBlb7pfONMZHBzs9wZIx5OvW1iLwO7AE+Arw+KJlKoVf6PWvAurfqS1vuA2qOTa7SJMuop/KrAcpFRJIrE3+PYoSVsYj8F3AtcLExZk/KKi3HLBhjwsaY3caYtcaYr2LVlnweLb/eOBerhnOLiERFJAosBm5P/N2YSKdlmSVjjB/YAkxjCHwXNej3wBgTBtYCl6StuoSj77eozPZifUm7yk9EcoALOFJ+r2Hdez035X3nArmMoDIWkfs5EvC3p63WcjwxNsCNll9v/AWrpfn8lMca4MnE3zvRsuyVRPnMxGrAN/jfxcFu6TjUH8DVQBi4BatV5f1YjSwmDHbehsIj8eWcn3gEgG8l/h6fWP8VoBX4IHAa1o9HFZCfso2ngU2JL/a5ib+fGuxjG8Ay/BnQhtVdb3TKIy8ljZZjz2X4PawfzolYQeu7QBx4t5bfSZftShKt97UssyqvH2LVjkwCzgb+lvj/PWEolN+gF9BweAC3A/uwGgitBS4c7DwNlQdWH3OT4fFQYr1gdWGpBjqBF4HT0rZRDDya+I/Rlvi7aLCPbQDLMFP5GeCulDRajj2X4UPA/sT/0TrgX8ClWn59UrbpQV/LsufySgbxMHAY+BMwe6iUn86yp5RSSo0Qek9fKaWUGiE06CullFIjhAZ9pZRSaoTQoK+UUkqNEBr0lVJKqRFCg75SSik1QmjQV2oEEpGbRMSIyNTBzotSauBo0FdKKaVGCA36SqkhS0Tcg50HpU4lGvSVUscQkTNF5I8ickhEgiKyQ0TuFRFPSpqfiEitiDjT3psvIu0i8r2UZeUi8gsROSwiIRHZLiK3pr0vecvhQhH5g4i0YM1NnszPcyLSmMjPHhF5oH9LQalTj2OwM6CUGpLGY01N+xDQDszBmkxpMnBNIs3PgU8BHwCWp7z3OqwZwX4JICIFwCuAB2vM8b3ApcDPRcRtjPlJ2r4fA54ArgIcIpIHPAOsBm5K5GcicF6fHKlSI4iOva/UCCQiNwG/BaYZY3YfJ60Adqxg/zBQboxpTKxbCcSMMUtT0q8D6owxlyVefxP4OjDXGLMrJd2vsE4YRhtjoil5+m9jzOdT0i0C3gTmGWM2nuShKzWiafW+UuoYIlIgIt8XkbexZq6LAI9gzRA2LSXpA8BFIjIt8b4zgQUkrvITLsOqpt8rIo7kA+vqvRSYnbb7/017vQtoAX4pIjeIyLi+OEalRiIN+kqpTH4L3Ab8GLgEOBP4ZGJdTkq6/wVqgI8nXt+GNa3oUylpRgEXYp04pD7+kFhfmrbv6tQXxphW4KLEdh8ADojIZhG58gSPTakRS+/pK6WOIiI5wPuAu4wx96csn5ue1hgTEZFfA7eLyH9i3QK4zxgTTUnWiDXH/We72eWO9M1m2M8G4MpEDcEi4KvAchGZZ4zZnPXBKTXC6ZW+UiqdG+sefiRt+U3dpP8lUIR15e4GfpW2/p/ATOCAMWZNhkd7thkzxkSNMa8D38T6/ZqV7XuVUnqlr9RId5mI1KQtawVeB/5dRKqBBuCjwJhMGzDGHBaRv2I1ynvKGHMwLcl/AVcDL4vIf2Fd2edinQhcYIx5X08ZFJH3ALcCf8Fq+Z8LfAarFf9rWR6nUgoN+kqNdOnd5QC2AO/B6pL3MyCI1SXvs8DfutnOH7CC/i/TVxhjWkXkPKwuf1/BOnlowQr+f8oij7sSefgmUIEV7N8ELjHGHMri/UqpBO2yp5Q6aSLyGPAOYLIxJj7Y+VFKZaZX+kqpEyYi5wDzsarvv6ABX6mhTa/0lVInTEQM4Meq/v94Wqt9pdQQo0FfKaWUGiG0y55SSik1QmjQV0oppUYIDfpKKaXUCKFBXymllBohNOgrpZRSI4QGfaWUUmqE0KCvlFJKjRAa9JVSSqkRQoO+UkopNUJo0FdKKaVGCA36Siml1AihQV8ppZQaITToK6WUUiOEBn2llFJqhNCgr5RSSo0QGvSVUkqpEUKDvlJKKTVCaNBXSimlRggN+koppdQIoUFfKaWUGiE06CullFIjhAZ9pZRSaoTQoK+UUkqNEBr0lVJKqRFCg75SSik1QmjQV0oppUYIx2BnQI1M5eXlL9nt9vGDnQ+l1ImJxWIH6uvrLxzsfKje0aCvBoXdbh9fU1PTMNj5yOTHP/5x6e9+97uytWvX7ujLtEqdSkaPHq0n7cOQVu8rdYoLBoPyoQ99aGJeXt6CsrKyeXfddZevp7Q333zzuFGjRp1eUFAw/4YbbhgfCoUkud7r9S5Ifdjt9oUf+chHxgGsWLEi97zzzptWWFg4v7i4eN673/3uyfv373em76Ozs1MmT548x+fznd4/R3y0u+++e1RZWdm8vLy8BR/60IcmBoNB6S7t//3f/+VPmjRpjsfjWXD22WdP37lzpyu5rqdy7OzslMsuu2zymDFj5orIwr/97W/5qdttaGiwf/CDH5xYUlIyr6SkZN4XvvCFyuS6w4cPO9773vdOGjVq1On5+fnzzzjjjJnPP/98bnL9U089lT99+vTZ+fn584uKiuZfcsklU/bu3XtMuSqVDQ36Sp3ivvjFL1bu2bPHvXfv3o3PPvvsjp/+9Kej//jHPxZkSvuNb3xj9IYNG7ybNm3asn379s2bNm3y3nHHHRXJ9YFAYH3yUV1d/Zbb7Y5fc801zQCNjY32W265pWHv3r0bDxw4sCkvLy9+ww03TEzfx5133ukrKSmJ9uYYxowZM3fHjh2u46c82p/+9KeCH//4xxX//Oc/d+zdu3fj/v373f/+7/9emSltdXW144YbbpjyzW9+s6qxsXHD/PnzAx/60IcmJ9cfrxzPO+88///8z//sLSsri6Rv+7bbbhsXDAZt+/fv3/Taa69t+8Mf/lBy//33lwK0trbaFi5cGHjzzTe3NTc3b7juuusarrzyyqmtra02gPnz5wefffbZXe3t7Ruqq6vfmjJlSujmm2+e0NuyUAo06KsR6mtf+9rocePGnZabm7tgypQpcx5++OGi7tKKyMJ77rln1NixY+cWFxfP+/jHPz42FosdlebWW28dW1BQMH/MmDFzly9f3hUI7r///tLJkyfPyc3NXTB27Ni5P/jBD8r676gy+8Mf/lD69a9/vbq8vDx2xhlndF5//fX1Dz30UMZ8PPPMM0Wf/OQn63w+X6yysjL6iU98ou6JJ57ImPbhhx8uLikpiV566aV+gGXLlrV99KMfbS4pKYnn5+fHP/vZz9atW7cuL/U927dvd/3hD38o/fKXv1zd90d6rIceeqj02muvbVi0aFFneXl57Otf/3rV8uXLMx7PY489VjR16tTOj370o81er9f84Ac/qNqxY4d3/fr1OdBzOebk5JhvfetbdZdeeqnfZjv2Z3XFihWFd9xxR01+fn58xowZ4euvv77h4YcfLgOYPXt2+K677qqdMGFCxOFw8MUvfrEhEonYNm7cmAMwbty46MSJE7tOJOx2u9m/f7+7H4pLjQAa9NWINHXq1NDLL7+8o62tbf0dd9xR9fGPf3xSpqropKeeeqpo7dq1W1evXr3tmWeeKbr//vu7Asdbb72VO2PGjM6mpqYNn/nMZ2o+9alPTYzH4wD4fL7oU089tbu9vX39L3/5y73f+ta3xr3yyiveTPt45pln8vLz8+d393jmmWfyMr2vJ/X19fb6+nrnmWeeGUgumz9/fnDnzp053b0nmXcAYwy1tbXOxsZGe3q6xx57rHTZsmWNmYIcwPPPP583derUYOqyT3ziE+Pvuuuuw16v1/T2WE7Ezp07PfPmzes69rPOOivY2NjoqKmpOeZ4tmzZ4pk9e3ZX2oKCgvi4ceNCb731Vs6JlGO69HLdtWuXJ1O6VatWeSKRiMyePTuUXLZr1y5Xfn7+/Nzc3DN++ctf+j772c/WZLtfpVJp0Fcj0kc/+tHmiRMnRux2Ox/72MeaJ0yYEHr55Zdzu0v/pS99qcbn88WmTZsWvu2222r/8Ic/lCTXVVZWhv/93/+9weFwcPvttzfW19c7Dx065AC45pprWufMmROy2WxcccUV/ne84x1tL7zwQsbgfemll/rb29s3dPdIXlH3RrKKuLS0tKtqoqioKNbR0XFM0AO4+OKLWx944AFfVVWV48CBA46f//znowD8fv9RvxU7d+50vfnmm/m33nprxsaYb7zxhue+++6r/P73v38ouezhhx8uisVicuONN7YcL9/JIJd8VFdXuxYuXDgn+foXv/hFyfG2ARAIBGzFxcVdx15SUhIDaG1tPeb4Ozo6bIWFhUdV4eTn58fa2trsvS3HdBdeeGHbd7/73Yrm5mbb5s2b3Y8//nhZZ2fnMb+/TU1NtptuumnSF77wharUfU2bNi2cqN7f8JWvfKVqzpw5ndnsV6l0GvTViPTTn/60dObMmbOTQWT37t2e+vr6bnuzTJw4MZz6d21tbVetQHl5eVfVa35+fhygra3NDrB8+fKCefPmzSwsLJyfn58//8UXXyxsaGgYsF4zhYWFcYDm5uau4NTa2mrLzc2NZUp/7733Vp922mmB+fPnzz7vvPNmXXHFFS0Oh8OMHTv2qPvUv/71r0vPOOMM/8yZM8Pp29i8ebP7ve9977R77733wGWXXeYHaGtrs33zm98c+7Of/exANvlOBrnko6KiIrx27dotyde33XZbUzbb8Xq98ZaWlq5jb25utiXK5Zjjz83NjSc/tyS/328rKCiI9bYc0z344IMHcnJy4tOmTZv7/ve/f+oHPvCBJp/Pd1TZ+f1+ufTSS6edccYZHd/97nczXsn7fL7Yxz/+8YZly5ZNjUSOaTqg1HFp0Fcjzs6dO11f+MIXJtx///0HmpubN7S3t2+YOnVq0Jjua5z37dvX1Yhs//79Lp/Pd9xf3GAwKB/5yEemfO5zn6utq6t7q729fcPixYtbu9vPP//5z7z01vGpj3/+85+9rt4vLy+PlZeXR1avXt11S2HDhg3e6dOnZ7xSzMvLMw8//PCBurq6jYcOHdpUWloanTNnTsBuP/qCdvny5aXXX3/9MVf5O3fudF166aXTv/jFL1Z/8pOf7ArMmzdvdh8+fNh10UUXzSwrK5t3/fXXT2loaHCWlZXNO5EGetmaPn168K233uo69tWrV3tLS0ujo0ePPiZYz5kzJ7h169autG1tbbaDBw+6582b19nbckzn8/lif/3rX/c2NDS8tXv37i3xeFzmz5/fkVwfDAblsssum1pRURF+7LHH9ve0rUgkIk1NTY6mpqasahmUSqVBX4047e3tNhFh9OjREbAa2+3evTvj/dWk++67b3R9fb199+7dzl/84hejrrzyyuNeaXZ2dko4HLaNGjUq4nQ6zfLlywteffXVjK3mAS677DJ/auv49Efyqrm3rrrqqsZ77723or6+3r5+/fqcxx57rOymm27KWC2/d+9e5759+5zxeJwVK1bk/vCHP6z81re+VZWa5rnnnsutra11fuQjH2lOf+/SpUun33zzzXVf/vKX61PXnXnmmcE9e/ZsXLdu3ZZ169Zt+fGPf7yvtLQ0sm7dui1Tpkw5prYg3eHDhzfNmDHjuOnSfeQjH2l84oknytauXZvT0NBgv/feeyuWLVuW8divu+66ll27duU89NBDRYFAQL7yla9UTJ8+PbhgwYJOOH45BoNBCQQCAhAOhyUQCEjyPv6WLVvcNTU19mg0yvLlywsee+yxsjvvvLMaIBQKyRVXXDElJycn/qc//Wlv+gnW7373u6K33nrLHYvFqKqqcnz6058eN2vWrIDP58uqlkGpVBr01YizcOHCzltvvbX2wgsvnFVeXj5v06ZNngULFvQYUK+44oqWBQsWzF60aNGcd77zna2f+9znjjuwUHFxcfyee+45cOONN04pLCyc//jjj5cuXbq0te+OJDv33Xdf1cSJE0OTJk06/Z3vfOeMT37yk7VXXXVVG1j3zr1e74Jdu3a5ALZv3+5+xzveMTM3N3fBzTffPOnOO+889MEPfrAtdXu//e1vyy699NKW4uLieOryn/70p2WHDh1y//CHP6xMraEAcDqdjB8/Ppp8lJaWxkSE8ePHRx2OY+92JPPV3ePnP/95Vvf0r7rqqrZPfepTNe9617tmTJw48fSxY8eG77vvvq6TmKlTp85JbquysjL6yCOPvH333XePKSkpWbB27dq85cuX78mmHBPbOi03N/eMuro655VXXjktNzf3jGS5vvbaa97TTz99Tn5+/oJvfetbY3/961/vXbRoUSfAv/71r9wXXnih8NVXXy0oLCw8pmbn0KFDrssvv3x6Xl7egtNPP32OzWbjL3/5y9vZHL9S6aSnKk2l+svo0aP3DdUR+dKJyMJNmzZtPu2000LHT63UyDB69OiympqaiYOdD9U7eqWvlFJKjRAa9JVSSqkRQifcUeo4jDFrBzsPSinVF/RKXymllBohNOgr1Q/uuOOO0VdffXW3k6KMGTNm7l/+8pf87tYn7dixwyUiC5MDsVx44YXTfvKTn5T2YVaVUiOIBn01ZIwZM2ZuTk7OGV6vd0Fpaem8K6+8cmJy+NOh6sc//nGp3W5fmNqd7MYbbxz/ve99r+b3v/99j4OsnIiXXnpp16c//enG5L4XLlw4o6/3cbJ27NjhOvvss6d7PJ4FkyZNmtPTyU2ib/+UwsLC+T6f7/T//M//LE+uyzRYkYgsfOihh4rSt3PuuedOTz05AvjsZz9bOX369NkOh2Nh6lS2/ak30xhDz9P+9lSOb775Zs75558/rbi4eJ6ILEzf7o4dO1yLFy+eWlBQML+srGzejTfeOD61bFatWuWZM2fOLI/Hs2DOnDmzVq1a1TVOxd133z1q7Nixc/Py8haMGjXq9Jtvvnmcjv536hjSP6hq5HnyySd3BQKB9WvWrNm6cePG3K9+9asV6WmG2g/Q/PnzjxpU5+GHH85qqNlT1dVXXz157ty5gfr6+g133nnn4Q9/+MNTqqqqMrYfuuaaayZNmDAhXFdX99Zf/vKXXd/5znfGPPXUU/lw7GBFy5cv3+31euPp4wb8/Oc/L4lEIpK+7alTp4buueeeQ4sXL27pTf537NjhGjNmzNzevCepN9MYH2/a357K0eVymSuvvLL5Jz/5ScYTy1tvvXV8WVlZtKam5q1169Ztef311/O+//3vjwJr0Kirrrpq6rJlyxqbmpo2XHvttY1XXXXV1M7OTgG46qqrWjds2LDV7/ev37Rp05YtW7Z4vvOd7/R48qKGDw36akiaNGlS5OKLL27dtm2bB6y+8t/97nfLJ0yYcNrEiRPnAtx3331l48ePP62wsHD+xRdfPHXfvn1d4+GvWbMm57zzzptWWFg4v7S0dN4dd9wxGiAWi3VNq1tUVDT/8ssvn1xbW2sHCAQC8r73vW9SUVHR/Pz8/PmnnXbarIMHDzrAuqoeO3bs3Nzc3AVjxoyZe7zBYb7whS9Uvu9975uUfP2zn/2spLKycm5RUdH8r3zlK6NT0/aUp3RnnXXWjB/96Edl69aty/nSl740YcOGDXler3dBYlx/b2lp6bxo9MhU9b/73e+KZsyYMbuXxX/CNm7c6N66dav3Bz/4QVVeXp656aabWqZPnx589NFHi9PTtra22lavXp1/zz33VLvdbnPuuecGL7/88ubf/OY3Gae+feihh0rf/e53NxcUFHQNCtTY2Gj//ve/f9TEPkmf/vSnG5ctW9aWl5cXT1/XX3ozjXFP0/4erxznzZsX+vznP98wf/78YKZtHzx40L1s2bJmr9drxo8fH73ooovatm7d6gH4xz/+kR+NRuWb3/xmncfjMd/4xjfqjDH87W9/yweYM2dOqKysLAbWbIA2m43du3frVL6nCA36akjavXu3c8WKFYWnn35611SmTz31VNHq1au37dixY/Nf//rX/HvuuWfM448/vqempuatcePGha666qrJYE2q8u53v3v6JZdc0lZdXf3W7t27N73rXe9qB7j33ntH/f3vfy9auXLljurq6reKiopit9xyy3iAn/3sZ6Xt7e32gwcPbmxubt7w85//fH9iEhbb1772tfF///vfd3Z0dKxftWrV9tQpVo9n7dq1OV/60pcm/OY3v9lbXV39VmNjo6O2trZrvPme8tSdM844o/MHP/jB/mQtQ2Jc/0BRUVH0z3/+c9eV5aOPPlp6zTXXNGbaxte+9rXRPU3lm+3xpdqwYYNn7NixodTR+ubMmRPcsmXLMVPQJoeoTR0gzBjDjh07jhkSua2tzfb0008X33TTTUcdy+c+97kx//Zv/1aXPiHQYOjt9Ls9Tfvbm3LM5BOf+ETtk08+Wdze3m7bu3ev8/nnny+87LLLWgE2bdqUM3PmzGDqlMgzZ84Mbtq0qavcf/GLX5Tk5eUtqKiomL9t2zbPJz/5yfoMu1HDkAZ9NaRcd911U/Pz8+dfeOGFM88555z273znO9XJdXfccUeNz+eL5eXlmUcffbTk6quvbjz//PMDHo/H/PjHPz68YcOG3B07driWL19eVFZWFr377rtrvV6vKS4ujl988cUdAL/97W/Lv/3tbx+eMmVKxOPxmO9+97tVTz/9dHEkEsHpdJrm5mbH1q1b3Q6HgwsuuCBQUlISBxARs379eo/f75cJEyZEkkOoArz11lt5qcFyxYoVR03R+8QTTxRffPHFre9+97v9Ho/H/OhHP6oSka5I11Oeeuuaa65pfPTRR0sBamtr7a+88krBRz/60YzzBNx77701PU3l2+udY81rkJ+ff9SY8IWFhTG/339MzUVxcXH8jDPO8H/ta1+rCAQC8sorr3iffvrp4mAweMzv0iOPPFJUVFQUvfzyy9uTy1566SXvm2++mfe1r32t7kTymuoXv/hFSfLzW7hw4Zzq6uqjpvZNDqfbk95Ov9vTtL+9KcdMli5d6t+5c6enuLh4weTJk08//fTTO2644YYWAL/fby8oKDhmCuH29vaubd92221Nfr9//caNGzffeOON9WPGjBn0kyrVNzToqyHl8ccf393e3r6hqqpq06OPPnogLy+vKzhOmjSpa8KVmpoa14QJE7qGxS0sLIwXFRXF9u/f7zx48KBzwoQJGWc/q66udl1//fVTkz/mp5122hy73c6hQ4ecn/jEJ5ouvvji1uuuu27yqFGjTr/tttvGhkIhKSgoiD/00EN7HnzwwfKKiop5S5Ysmbp+/fquK6558+b5U4Pl0qVLO1L3WVVV5RwzZkxX3gsKCuJFRUXRbPLU2/K75ZZbGp9//vmitrY22+9+97uShQsX+idMmDBgP9j5+fnx9MDU1tZmy8vLyzg5zBNPPLH3wIED7jFjxpx+++23j//ABz7QWFFRcczEOo899ljZsmXLGpNXp7FYjE9+8pMT7rvvvgNOZ6+L6Ri33XZbU/LzW7t27ZaKioqjpvadNm3acSf76e30uz1N+9vbckwVi8V4z3veM+0973lPc3t7+7rq6uoNLS0t9ttvv30sQF5eXqy9vf2o336/329PP8kAmDt3bmjOnDnBW265pdueKGp40aCvhg2RI221Ro8eHd6/f3/Xfca2tjZbS0uLfcKECZFx48ZFDhw4kPEepM/ni/z5z3/emfqDHgqF1k2aNCnidrvNfffdV/32229vefnll7c/99xzhQ888EApwJVXXtm2atWqXdXV1W9Nmzatszc/ghUVFZHDhw93XSm2t7fbWlpauhq29ZSnbMsjadKkSZH58+f7H3nkkaInn3yy9LrrrstYtQ9Wt8KeJrXJ9vhSzZ8/P3jo0CF3MoABbNmyxTtnzpyMJ2HTp08Pv/DCC7ubm5vf2rhx4/bGxkbHGWeccdRJ0+7du52rV6/Ov+WWW7qOpbm52b5lyxbvRz7ykSllZWXzzjnnnFkAFRUV805kCuK+0Nvpd3ua9re35Ziqrq7OUV1d7fryl79c7/F4zOjRo2M33XRT44oVKwoB5s6d27l9+3Zv8vYKwPbt2z1z587N2D4gGo1K6v81Nbxp0FfD0nXXXdf0+9//vnTVqlWeYDAon/3sZ8fMmzevY8aMGeFly5a11NfXO7/97W+PCgaD0tzcbHv++edzAf7t3/5/e3cb01b1BgD8uX2j9FLWlpeWXsbbeFuhtGXNph80m4uQjM1g6oYb4CTxgxvJvvABCUNjIosmStyWAHEuCmuYA5e4iDhxrm5uatapAxb/Qiu28G+HZaMTWF+4ffl/IOWPCi1MobA+v6Rf6DnnOfcm5ek9vec+VbajR48mDw0NcQAArFYrS6vVCgAAPv30U/6NGzeiPR4PCAQCL4vF8jMYDP/o6ChLq9UKJicnGdHR0f6YmBjf/N9DQ9m/f7/98uXLG7744osYl8tF1NTUSP1+/1zGDjanYJKSkuixsTFO4K7rgPLy8nvHjx+XDA0NRVdWVt5frP+bb745FqyU75IPcJ6CggJ3bm6uo7a2VupwOIj29nbB4OBgdEVFhX2h9j/++CPXbrczXC4X0dzcLLp27VpsfX397/PbvP/++3FKpXI6Ly9vbmVHJBJ5TSZTX6BU7yeffGIAANDr9T9v3779AcBsydpAeVuPxwMOh4OYf5PjYnJycmYsFsvAwxz/csoYByv7G+o8+nw+cDgchNvtJgBmb0INbPdLSkryUBQ188477yTQNA13795ltre3x+Xm5joAAHbt2jXFZDL9jY2NiU6nkzh27FgCAMDu3bunAACampriLRYLC2D2fpSmpqakJ554YvLvR4DWI0z6aF0qLS2dqqurs5aVlW2SSCQKk8kUFSiDKhQKfT09PUOff/65QCwWK7KysuSXLl3iAwAcPXrUtmvXrvtFRUXZJEmqtm3blvv999+TALPL8Pv27dvE5/NVMpks//HHH586fPjwPZ/PRxw/flxMUVSBQCBQXr9+nd/a2rrkPfhqtdr11ltvjbz44ovpEolEIRQKPWKxeG65ONicgtm9e/dUVlaWUywWK4RCoSLw94qKivtWqzWqqKjIzufzV+3O9YDOzs7hW7dukSKRSPXqq68mnzlz5lepVOoBmN1el5mZmRdo293dHZuRkSEXCoXK06dPJ1y4cMEQaBtw7ty5uPLy8j+tWDAYjD+V6pVIJB4AgOTkZJrL5foBAA4cOJBKkmRhd3e36OTJk0kkSRYGVm7+qqWlRRRs1WMpv+kDLK+Mcaiyv8HOo8Fg4JAkWahWq/MAAEiSLMzMzMyf19d46dKlDXFxccqsrKx8Npvtb25uHgUA4HK5/q6uLuNHH30UJxQKVVqtNr6rq8sYOG/ffvttTEFBQV50dLTqmWeeydq5c+cfJ06csCzl+NHah6V1UVisp9K669HGjRvzT548aS4tLZ0K3Rqh5cPSuusTXukj9Ij58MMPBQRBwJ49ezDhI4T+BKvsIfQI2bp1a47RaOSeOnXqNyZzSbu7EEIRBJM+Qo+QGzduDIZ7DgihtQuX9xFCCKEIgUkfIYQQihCY9BFCK0Kn0/GUSmWuWq3O2bNnT3pgTzlCKHww6SOEVkRGRgZ97dq1wZs3bw6mpKTMdHR0CMI9J4QiHSZ9hNYJjUaTduTIEWnolmsjZmpqKh2oncDhcPwMBgMfCoJQmGHSRwitqKGhIY5Op4t9/vnn/1jJOMeOHUvIz8/fzOFwCjUaTdpi7ZxOJ7Fv375UqVQqJ0lSlZubK+vs7Ixdyjih+iK01mHSRygMHqZs7no0MTHBOHDgQHpbW9tvUVFRK3qlT1EUXVtbe2fv3r1Bn/RI0zSRnJxM63S6wcnJyZ9ee+01S1VV1abBwUFOqHFC9UVorcOkj9AqoShKXl9fL8nOzpaRJFloMBg4xcXFm4RCoYKiKPkbb7yROL/99evXo2Uy2WaSJFUlJSUZbrd77vNKEMSW27dvz1U+++syvNFoZBcVFW0SCoUKgUCgfOGFF1IC75lMJvZicYPFXEh6enpedna2bGRkhAUAoNfruXw+X/nDDz9waZqGZ599NqOhocGqUCjcwcb5Nxw8ePB+ZWXl/bi4uKBVdWJjY31NTU3WnJycGSaTCfv37/+Doij3d999xws1Tqi+CK11mPQRWkXnz58X9fT0GO7evXurtLR0k1wud9y5c6f/yy+/HGxtbRWfP38+FgDA5XIRe/fuzSwrK7s3MTFx67nnnrNfvHhRsJQYHo8HSkpKsjZu3DhjNpsHrFZrX3l5+QTAbK31kpKSzIXiPkzMgYGBn3k8nq+rq0vgdruJgwcPph85cmRsy5Ytrvfee0/U19cX09jYKN26dWvOqVOnhAuNsWPHjkw+n69c6LVjx47M5Z3h5RsdHWWZzWauQqEIWbb23+yLUDjgE/kQWkUvv/zy75mZmfTly5fJiYkJ1ttvv30HAEAmk81UVlaOnz17VqTRaCZ1Oh3p8XiIhoYGG4PBgKqqKvuJEyfES4nx9ddfkzabjd3a2jrKZrMBAKC4uHgaAODKlSuLxuXxeL7lxoyJifE/9dRTkwMDA9F1dXVJLBbL//rrr48BAFRXV09UV1dPhJqvTqczLuW4VoLb7SbKysoyNBrNPZVKtazE/U/6IhQumPQRWkWpqak0AMDw8DBnfHycw+fzlYH3fD4foVarpwAARkdH2YmJiTSD8f/FuOTk5CUtkZtMJg5FUTOBhD9fsLihYra0tIhqampSAQDUavX01atXDQAAcrnc2dDQkGy321nffPPNf1is9fFvxev1gkajSWez2b4PPvhgZLX6IhRO6+PTidAjgiAIPwBAWlraDEVRbrPZfHuhdhRF0Tabje3z+SCQhC0WS1R6erobAIDL5foePHgwl51tNhuboqiZwNhWq5VD0zT8NfEHi/vZZ5/FBIt56NChiUOHDv3tyr2goMBpsVg49fX1/y0sLFz2Fe+TTz6ZdfPmzZiF3pv/5eLf5PP5oKysLG18fJz11VdfGZZzk+E/6YtQuOFv+giFwfbt2x+QJOmtr6+XTE9PEx6PB/R6PffKlSs8AICdO3c+YDKZ/sbGxkS32020tbUJ+vv7524W27x5s7OtrU3k8Xjg448/jtXr9fz5YyckJNDV1dXJk5OTDIfDQfT29pKh4oaKuZjTp0/HAQAcPnz43sOci6tXrxocDsdPC72Wk/BpmgaHw0F4vV7C6/USDoeDWGyXREVFRYrBYOD29vYaA88SWOo4wfoitNZh0kcoDFgsFvT09Bj7+/uj09LSCkQikfKll15Ks9vtTAAALpfrP3fu3K8dHR3xIpFI2dnZKSouLr4f6P/uu++O9Pb2CjZs2KDSarVxTz/9tH3+2N3d3cbh4eGolJSUAoqiCjo6OkSh4oaKuRCdTsdrb29PFIvFdF9fH3dlztbS1NbWSkmSLGxubpZcuHBBRJJkYW1trRRgdjXhlVdekQDMPjfg7NmzCb/88gsvKSlJwePxVDweT9XS0iIKNU6ovgitdYTfj19U0eqTSCSmsbGxoPup0drmdDoJuVwuq6qqGtfr9eS2bdum6+rqxsM9L7Q6JBJJ/NjYWFq454GWB6/0EUIPpaamRhofH0/X1dXZ5HK54+LFixtcLhcW1UFoDcOkjxBaNp1Ox9NqtQnt7e0mBoMB5eXldrPZzH3sscdywj03hNDicHkfhQUu7yO0vuHy/vqEV/oIIYRQhMCkjxBCCEUITPoIIYRQhMCkjxBCCEUITPoIIYRQhMCkjxBCCEUILLiDwsLr9Y5IJJKUcM8DIfRwvF4vVhdch3CfPkIIIRQhcHkfIYQQihCY9BFCCKEI8T+VKUqSTqZQFAAAAABJRU5ErkJggg==",
+      "text/plain": [
+       "<qiskit_experiments.framework.containers.figure_data.FigureData at 0x353a44d00>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<qiskit_experiments.framework.containers.figure_data.FigureData at 0x353a479d0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<qiskit_experiments.framework.containers.figure_data.FigureData at 0x353a44d30>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<qiskit_experiments.framework.containers.figure_data.FigureData at 0x333780c10>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<qiskit_experiments.framework.containers.figure_data.FigureData at 0x333781e70>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Plot 1Q/2Q simultaneous direct RB curves\n",
+    "for i in range(0, 5):\n",
+    "    display(exp_data.figure(i))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "151428c3",
+   "metadata": {},
+   "source": [
+    "### Filter qubit pairs with bad process fidelity "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "3207af8e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>name</th>\n",
+       "      <th>experiment</th>\n",
+       "      <th>components</th>\n",
+       "      <th>value</th>\n",
+       "      <th>quality</th>\n",
+       "      <th>backend</th>\n",
+       "      <th>run_time</th>\n",
+       "      <th>qubits</th>\n",
+       "      <th>reason</th>\n",
+       "      <th>a</th>\n",
+       "      <th>alpha</th>\n",
+       "      <th>b</th>\n",
+       "      <th>chisq</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>4a949ae1</th>\n",
+       "      <td>ProcessFidelity</td>\n",
+       "      <td>LayerFidelity</td>\n",
+       "      <td>[Q7, Q6]</td>\n",
+       "      <td>0.795+/-0.034</td>\n",
+       "      <td>bad</td>\n",
+       "      <td>ibm_kyoto</td>\n",
+       "      <td>2024-03-21 16:29:46.643000+09:00</td>\n",
+       "      <td>(7, 6)</td>\n",
+       "      <td>large_spam</td>\n",
+       "      <td>0.203485</td>\n",
+       "      <td>0.780861</td>\n",
+       "      <td>0.225911</td>\n",
+       "      <td>6.248698</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                     name     experiment components          value quality  \\\n",
+       "4a949ae1  ProcessFidelity  LayerFidelity   [Q7, Q6]  0.795+/-0.034     bad   \n",
+       "\n",
+       "            backend                          run_time  qubits      reason  \\\n",
+       "4a949ae1  ibm_kyoto  2024-03-21 16:29:46.643000+09:00  (7, 6)  large_spam   \n",
+       "\n",
+       "                 a     alpha         b     chisq  \n",
+       "4a949ae1  0.203485  0.780861  0.225911  6.248698  "
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pfdf = df[(df.name == \"ProcessFidelity\")]\n",
+    "pfdf[pfdf.value < 0.8]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "28f3ee4c",
+   "metadata": {},
+   "source": [
+    "### Invesitigate analysis results labeled \"bad\" quality"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "a3924a1b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>name</th>\n",
+       "      <th>experiment</th>\n",
+       "      <th>components</th>\n",
+       "      <th>value</th>\n",
+       "      <th>quality</th>\n",
+       "      <th>backend</th>\n",
+       "      <th>run_time</th>\n",
+       "      <th>qubits</th>\n",
+       "      <th>reason</th>\n",
+       "      <th>a</th>\n",
+       "      <th>alpha</th>\n",
+       "      <th>b</th>\n",
+       "      <th>chisq</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>315b252c</th>\n",
+       "      <td>ProcessFidelity</td>\n",
+       "      <td>LayerFidelity</td>\n",
+       "      <td>[Q112, Q108]</td>\n",
+       "      <td>0.9741+/-0.0018</td>\n",
+       "      <td>bad</td>\n",
+       "      <td>ibm_kyoto</td>\n",
+       "      <td>2024-03-21 16:29:46.643000+09:00</td>\n",
+       "      <td>(112, 108)</td>\n",
+       "      <td>large_spam</td>\n",
+       "      <td>0.403517</td>\n",
+       "      <td>0.972388</td>\n",
+       "      <td>0.253707</td>\n",
+       "      <td>1.211738</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8ba264a8</th>\n",
+       "      <td>ProcessFidelity</td>\n",
+       "      <td>LayerFidelity</td>\n",
+       "      <td>[Q106, Q107]</td>\n",
+       "      <td>0.9750+/-0.0031</td>\n",
+       "      <td>bad</td>\n",
+       "      <td>ibm_kyoto</td>\n",
+       "      <td>2024-03-21 16:29:46.643000+09:00</td>\n",
+       "      <td>(106, 107)</td>\n",
+       "      <td>large_spam</td>\n",
+       "      <td>0.192179</td>\n",
+       "      <td>0.973366</td>\n",
+       "      <td>0.212858</td>\n",
+       "      <td>2.313055</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>fd70efcf</th>\n",
+       "      <td>ProcessFidelity</td>\n",
+       "      <td>LayerFidelity</td>\n",
+       "      <td>[Q105, Q104]</td>\n",
+       "      <td>0.9999999999999999+/-nan</td>\n",
+       "      <td>bad</td>\n",
+       "      <td>ibm_kyoto</td>\n",
+       "      <td>2024-03-21 16:29:46.643000+09:00</td>\n",
+       "      <td>(105, 104)</td>\n",
+       "      <td>large_spam</td>\n",
+       "      <td>0.003853</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.25289</td>\n",
+       "      <td>0.97619</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>329c0c1a</th>\n",
+       "      <td>ProcessFidelity</td>\n",
+       "      <td>LayerFidelity</td>\n",
+       "      <td>[Q122, Q111]</td>\n",
+       "      <td>0.939+/-0.008</td>\n",
+       "      <td>bad</td>\n",
+       "      <td>ibm_kyoto</td>\n",
+       "      <td>2024-03-21 16:29:46.643000+09:00</td>\n",
+       "      <td>(122, 111)</td>\n",
+       "      <td>large_spam</td>\n",
+       "      <td>0.433793</td>\n",
+       "      <td>0.934506</td>\n",
+       "      <td>0.24728</td>\n",
+       "      <td>3.67749</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>b1455dcc</th>\n",
+       "      <td>ProcessFidelity</td>\n",
+       "      <td>LayerFidelity</td>\n",
+       "      <td>[Q121, Q120]</td>\n",
+       "      <td>0.9858+/-0.0005</td>\n",
+       "      <td>bad</td>\n",
+       "      <td>ibm_kyoto</td>\n",
+       "      <td>2024-03-21 16:29:46.643000+09:00</td>\n",
+       "      <td>(121, 120)</td>\n",
+       "      <td>large_chisq</td>\n",
+       "      <td>0.652881</td>\n",
+       "      <td>0.984827</td>\n",
+       "      <td>0.239197</td>\n",
+       "      <td>3.405308</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>ca0ff259</th>\n",
+       "      <td>ProcessFidelity</td>\n",
+       "      <td>LayerFidelity</td>\n",
+       "      <td>[Q119, Q118]</td>\n",
+       "      <td>0.9922+/-0.0004</td>\n",
+       "      <td>bad</td>\n",
+       "      <td>ibm_kyoto</td>\n",
+       "      <td>2024-03-21 16:29:46.643000+09:00</td>\n",
+       "      <td>(119, 118)</td>\n",
+       "      <td>large_chisq</td>\n",
+       "      <td>0.570897</td>\n",
+       "      <td>0.991682</td>\n",
+       "      <td>0.25806</td>\n",
+       "      <td>3.158789</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>156ce79e</th>\n",
+       "      <td>ProcessFidelity</td>\n",
+       "      <td>LayerFidelity</td>\n",
+       "      <td>[Q82, Q81]</td>\n",
+       "      <td>0.9786+/-0.0008</td>\n",
+       "      <td>bad</td>\n",
+       "      <td>ibm_kyoto</td>\n",
+       "      <td>2024-03-21 16:29:46.643000+09:00</td>\n",
+       "      <td>(82, 81)</td>\n",
+       "      <td>large_chisq</td>\n",
+       "      <td>0.612284</td>\n",
+       "      <td>0.977174</td>\n",
+       "      <td>0.256304</td>\n",
+       "      <td>4.146358</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>df2acdc7</th>\n",
+       "      <td>ProcessFidelity</td>\n",
+       "      <td>LayerFidelity</td>\n",
+       "      <td>[Q80, Q79]</td>\n",
+       "      <td>0.9818+/-0.0006</td>\n",
+       "      <td>bad</td>\n",
+       "      <td>ibm_kyoto</td>\n",
+       "      <td>2024-03-21 16:29:46.643000+09:00</td>\n",
+       "      <td>(80, 79)</td>\n",
+       "      <td>large_chisq</td>\n",
+       "      <td>0.652751</td>\n",
+       "      <td>0.980545</td>\n",
+       "      <td>0.253304</td>\n",
+       "      <td>3.196182</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>e40cc5a4</th>\n",
+       "      <td>ProcessFidelity</td>\n",
+       "      <td>LayerFidelity</td>\n",
+       "      <td>[Q98, Q91]</td>\n",
+       "      <td>0.9756+/-0.0011</td>\n",
+       "      <td>bad</td>\n",
+       "      <td>ibm_kyoto</td>\n",
+       "      <td>2024-03-21 16:29:46.643000+09:00</td>\n",
+       "      <td>(98, 91)</td>\n",
+       "      <td>large_chisq</td>\n",
+       "      <td>0.580621</td>\n",
+       "      <td>0.974021</td>\n",
+       "      <td>0.222527</td>\n",
+       "      <td>3.307072</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>91fa4b5e</th>\n",
+       "      <td>ProcessFidelity</td>\n",
+       "      <td>LayerFidelity</td>\n",
+       "      <td>[Q76, Q77]</td>\n",
+       "      <td>0.906+/-0.014</td>\n",
+       "      <td>bad</td>\n",
+       "      <td>ibm_kyoto</td>\n",
+       "      <td>2024-03-21 16:29:46.643000+09:00</td>\n",
+       "      <td>(76, 77)</td>\n",
+       "      <td>large_spam</td>\n",
+       "      <td>0.250542</td>\n",
+       "      <td>0.899955</td>\n",
+       "      <td>0.194955</td>\n",
+       "      <td>2.241609</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15ed0950</th>\n",
+       "      <td>ProcessFidelity</td>\n",
+       "      <td>LayerFidelity</td>\n",
+       "      <td>[Q62, Q61]</td>\n",
+       "      <td>0.9833+/-0.0009</td>\n",
+       "      <td>bad</td>\n",
+       "      <td>ibm_kyoto</td>\n",
+       "      <td>2024-03-21 16:29:46.643000+09:00</td>\n",
+       "      <td>(62, 61)</td>\n",
+       "      <td>large_chisq</td>\n",
+       "      <td>0.585973</td>\n",
+       "      <td>0.982237</td>\n",
+       "      <td>0.245123</td>\n",
+       "      <td>3.005546</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>435599c8</th>\n",
+       "      <td>ProcessFidelity</td>\n",
+       "      <td>LayerFidelity</td>\n",
+       "      <td>[Q63, Q64]</td>\n",
+       "      <td>0.9822+/-0.0007</td>\n",
+       "      <td>bad</td>\n",
+       "      <td>ibm_kyoto</td>\n",
+       "      <td>2024-03-21 16:29:46.643000+09:00</td>\n",
+       "      <td>(63, 64)</td>\n",
+       "      <td>large_chisq</td>\n",
+       "      <td>0.669473</td>\n",
+       "      <td>0.981054</td>\n",
+       "      <td>0.247685</td>\n",
+       "      <td>4.958896</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>f0c016b0</th>\n",
+       "      <td>ProcessFidelity</td>\n",
+       "      <td>LayerFidelity</td>\n",
+       "      <td>[Q65, Q66]</td>\n",
+       "      <td>0.9910+/-0.0004</td>\n",
+       "      <td>bad</td>\n",
+       "      <td>ibm_kyoto</td>\n",
+       "      <td>2024-03-21 16:29:46.643000+09:00</td>\n",
+       "      <td>(65, 66)</td>\n",
+       "      <td>large_chisq</td>\n",
+       "      <td>0.660338</td>\n",
+       "      <td>0.990358</td>\n",
+       "      <td>0.267629</td>\n",
+       "      <td>3.898633</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>79279b21</th>\n",
+       "      <td>ProcessFidelity</td>\n",
+       "      <td>LayerFidelity</td>\n",
+       "      <td>[Q32, Q36]</td>\n",
+       "      <td>0.9889+/-0.0007</td>\n",
+       "      <td>bad</td>\n",
+       "      <td>ibm_kyoto</td>\n",
+       "      <td>2024-03-21 16:29:46.643000+09:00</td>\n",
+       "      <td>(32, 36)</td>\n",
+       "      <td>large_chisq</td>\n",
+       "      <td>0.675131</td>\n",
+       "      <td>0.9882</td>\n",
+       "      <td>0.253997</td>\n",
+       "      <td>4.471692</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>bccc0de8</th>\n",
+       "      <td>ProcessFidelity</td>\n",
+       "      <td>LayerFidelity</td>\n",
+       "      <td>[Q8, Q9]</td>\n",
+       "      <td>0.9643+/-0.0027</td>\n",
+       "      <td>bad</td>\n",
+       "      <td>ibm_kyoto</td>\n",
+       "      <td>2024-03-21 16:29:46.643000+09:00</td>\n",
+       "      <td>(8, 9)</td>\n",
+       "      <td>large_chisq</td>\n",
+       "      <td>0.548188</td>\n",
+       "      <td>0.961901</td>\n",
+       "      <td>0.257352</td>\n",
+       "      <td>4.591003</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4a949ae1</th>\n",
+       "      <td>ProcessFidelity</td>\n",
+       "      <td>LayerFidelity</td>\n",
+       "      <td>[Q7, Q6]</td>\n",
+       "      <td>0.795+/-0.034</td>\n",
+       "      <td>bad</td>\n",
+       "      <td>ibm_kyoto</td>\n",
+       "      <td>2024-03-21 16:29:46.643000+09:00</td>\n",
+       "      <td>(7, 6)</td>\n",
+       "      <td>large_spam</td>\n",
+       "      <td>0.203485</td>\n",
+       "      <td>0.780861</td>\n",
+       "      <td>0.225911</td>\n",
+       "      <td>6.248698</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2ee122bb</th>\n",
+       "      <td>ProcessFidelity</td>\n",
+       "      <td>LayerFidelity</td>\n",
+       "      <td>[Q1, Q0]</td>\n",
+       "      <td>0.9821+/-0.0007</td>\n",
+       "      <td>bad</td>\n",
+       "      <td>ibm_kyoto</td>\n",
+       "      <td>2024-03-21 16:29:46.643000+09:00</td>\n",
+       "      <td>(1, 0)</td>\n",
+       "      <td>large_spam</td>\n",
+       "      <td>0.442856</td>\n",
+       "      <td>0.9809</td>\n",
+       "      <td>0.230101</td>\n",
+       "      <td>0.208882</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>d12da5c7</th>\n",
+       "      <td>ProcessFidelity</td>\n",
+       "      <td>LayerFidelity</td>\n",
+       "      <td>[Q18, Q14]</td>\n",
+       "      <td>0.988+/-0.009</td>\n",
+       "      <td>bad</td>\n",
+       "      <td>ibm_kyoto</td>\n",
+       "      <td>2024-03-21 16:29:46.643000+09:00</td>\n",
+       "      <td>(18, 14)</td>\n",
+       "      <td>large_spam</td>\n",
+       "      <td>0.034896</td>\n",
+       "      <td>0.987474</td>\n",
+       "      <td>0.258215</td>\n",
+       "      <td>1.719684</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>81cea09a</th>\n",
+       "      <td>ProcessFidelity</td>\n",
+       "      <td>LayerFidelity</td>\n",
+       "      <td>[Q22, Q21]</td>\n",
+       "      <td>0.9720+/-0.0008</td>\n",
+       "      <td>bad</td>\n",
+       "      <td>ibm_kyoto</td>\n",
+       "      <td>2024-03-21 16:29:46.643000+09:00</td>\n",
+       "      <td>(22, 21)</td>\n",
+       "      <td>large_chisq</td>\n",
+       "      <td>0.589132</td>\n",
+       "      <td>0.970173</td>\n",
+       "      <td>0.243312</td>\n",
+       "      <td>3.683361</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6e24abbe</th>\n",
+       "      <td>ProcessFidelity</td>\n",
+       "      <td>LayerFidelity</td>\n",
+       "      <td>[Q25, Q26]</td>\n",
+       "      <td>0.9918+/-0.0004</td>\n",
+       "      <td>bad</td>\n",
+       "      <td>ibm_kyoto</td>\n",
+       "      <td>2024-03-21 16:29:46.643000+09:00</td>\n",
+       "      <td>(25, 26)</td>\n",
+       "      <td>large_chisq</td>\n",
+       "      <td>0.61865</td>\n",
+       "      <td>0.991259</td>\n",
+       "      <td>0.259028</td>\n",
+       "      <td>3.356521</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>36f100f1</th>\n",
+       "      <td>ProcessFidelity</td>\n",
+       "      <td>LayerFidelity</td>\n",
+       "      <td>[Q37, Q38]</td>\n",
+       "      <td>0.9828+/-0.0008</td>\n",
+       "      <td>bad</td>\n",
+       "      <td>ibm_kyoto</td>\n",
+       "      <td>2024-03-21 16:29:46.643000+09:00</td>\n",
+       "      <td>(37, 38)</td>\n",
+       "      <td>large_spam</td>\n",
+       "      <td>0.432599</td>\n",
+       "      <td>0.981673</td>\n",
+       "      <td>0.21247</td>\n",
+       "      <td>1.74983</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>cd138baa</th>\n",
+       "      <td>ProcessFidelity</td>\n",
+       "      <td>LayerFidelity</td>\n",
+       "      <td>[Q107, Q108]</td>\n",
+       "      <td>0.962+/-0.004</td>\n",
+       "      <td>bad</td>\n",
+       "      <td>ibm_kyoto</td>\n",
+       "      <td>2024-03-21 16:29:46.643000+09:00</td>\n",
+       "      <td>(107, 108)</td>\n",
+       "      <td>large_spam</td>\n",
+       "      <td>0.14405</td>\n",
+       "      <td>0.95956</td>\n",
+       "      <td>0.22854</td>\n",
+       "      <td>0.819488</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2fa9a210</th>\n",
+       "      <td>ProcessFidelity</td>\n",
+       "      <td>LayerFidelity</td>\n",
+       "      <td>[Q105, Q106]</td>\n",
+       "      <td>0.9997+/-0.0029</td>\n",
+       "      <td>bad</td>\n",
+       "      <td>ibm_kyoto</td>\n",
+       "      <td>2024-03-21 16:29:46.643000+09:00</td>\n",
+       "      <td>(105, 106)</td>\n",
+       "      <td>large_spam</td>\n",
+       "      <td>0.264829</td>\n",
+       "      <td>0.999635</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.298543</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>e8470f6b</th>\n",
+       "      <td>ProcessFidelity</td>\n",
+       "      <td>LayerFidelity</td>\n",
+       "      <td>[Q104, Q111]</td>\n",
+       "      <td>0.9799+/-0.0019</td>\n",
+       "      <td>bad</td>\n",
+       "      <td>ibm_kyoto</td>\n",
+       "      <td>2024-03-21 16:29:46.643000+09:00</td>\n",
+       "      <td>(104, 111)</td>\n",
+       "      <td>large_chisq</td>\n",
+       "      <td>0.505645</td>\n",
+       "      <td>0.978537</td>\n",
+       "      <td>0.253888</td>\n",
+       "      <td>3.324584</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4cb99579</th>\n",
+       "      <td>ProcessFidelity</td>\n",
+       "      <td>LayerFidelity</td>\n",
+       "      <td>[Q102, Q92]</td>\n",
+       "      <td>0.9736+/-0.0008</td>\n",
+       "      <td>bad</td>\n",
+       "      <td>ibm_kyoto</td>\n",
+       "      <td>2024-03-21 16:29:46.643000+09:00</td>\n",
+       "      <td>(102, 92)</td>\n",
+       "      <td>large_chisq</td>\n",
+       "      <td>0.632969</td>\n",
+       "      <td>0.971815</td>\n",
+       "      <td>0.254668</td>\n",
+       "      <td>3.131533</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1e0f2c3f</th>\n",
+       "      <td>ProcessFidelity</td>\n",
+       "      <td>LayerFidelity</td>\n",
+       "      <td>[Q96, Q95]</td>\n",
+       "      <td>0.9834+/-0.0012</td>\n",
+       "      <td>bad</td>\n",
+       "      <td>ibm_kyoto</td>\n",
+       "      <td>2024-03-21 16:29:46.643000+09:00</td>\n",
+       "      <td>(96, 95)</td>\n",
+       "      <td>large_spam</td>\n",
+       "      <td>0.438003</td>\n",
+       "      <td>0.98226</td>\n",
+       "      <td>0.238188</td>\n",
+       "      <td>3.790113</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>b339523a</th>\n",
+       "      <td>ProcessFidelity</td>\n",
+       "      <td>LayerFidelity</td>\n",
+       "      <td>[Q94, Q90]</td>\n",
+       "      <td>0.9773+/-0.0009</td>\n",
+       "      <td>bad</td>\n",
+       "      <td>ibm_kyoto</td>\n",
+       "      <td>2024-03-21 16:29:46.643000+09:00</td>\n",
+       "      <td>(94, 90)</td>\n",
+       "      <td>large_chisq</td>\n",
+       "      <td>0.558095</td>\n",
+       "      <td>0.975839</td>\n",
+       "      <td>0.236842</td>\n",
+       "      <td>3.869758</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>de45c7fd</th>\n",
+       "      <td>ProcessFidelity</td>\n",
+       "      <td>LayerFidelity</td>\n",
+       "      <td>[Q76, Q75]</td>\n",
+       "      <td>0.960+/-0.004</td>\n",
+       "      <td>bad</td>\n",
+       "      <td>ibm_kyoto</td>\n",
+       "      <td>2024-03-21 16:29:46.643000+09:00</td>\n",
+       "      <td>(76, 75)</td>\n",
+       "      <td>large_spam</td>\n",
+       "      <td>0.422947</td>\n",
+       "      <td>0.956955</td>\n",
+       "      <td>0.238076</td>\n",
+       "      <td>3.385566</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>517c46b6</th>\n",
+       "      <td>ProcessFidelity</td>\n",
+       "      <td>LayerFidelity</td>\n",
+       "      <td>[Q77, Q71]</td>\n",
+       "      <td>0.9769+/-0.0018</td>\n",
+       "      <td>bad</td>\n",
+       "      <td>ibm_kyoto</td>\n",
+       "      <td>2024-03-21 16:29:46.643000+09:00</td>\n",
+       "      <td>(77, 71)</td>\n",
+       "      <td>large_spam</td>\n",
+       "      <td>0.432125</td>\n",
+       "      <td>0.975324</td>\n",
+       "      <td>0.234107</td>\n",
+       "      <td>2.477181</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>580222cf</th>\n",
+       "      <td>ProcessFidelity</td>\n",
+       "      <td>LayerFidelity</td>\n",
+       "      <td>[Q73, Q66]</td>\n",
+       "      <td>0.9900+/-0.0004</td>\n",
+       "      <td>bad</td>\n",
+       "      <td>ibm_kyoto</td>\n",
+       "      <td>2024-03-21 16:29:46.643000+09:00</td>\n",
+       "      <td>(73, 66)</td>\n",
+       "      <td>large_chisq</td>\n",
+       "      <td>0.649445</td>\n",
+       "      <td>0.989288</td>\n",
+       "      <td>0.264381</td>\n",
+       "      <td>3.445938</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>f24d523d</th>\n",
+       "      <td>ProcessFidelity</td>\n",
+       "      <td>LayerFidelity</td>\n",
+       "      <td>[Q88, Q87]</td>\n",
+       "      <td>0.9865+/-0.0006</td>\n",
+       "      <td>bad</td>\n",
+       "      <td>ibm_kyoto</td>\n",
+       "      <td>2024-03-21 16:29:46.643000+09:00</td>\n",
+       "      <td>(88, 87)</td>\n",
+       "      <td>large_chisq</td>\n",
+       "      <td>0.623668</td>\n",
+       "      <td>0.985601</td>\n",
+       "      <td>0.236159</td>\n",
+       "      <td>3.406165</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>635751a0</th>\n",
+       "      <td>ProcessFidelity</td>\n",
+       "      <td>LayerFidelity</td>\n",
+       "      <td>[Q11, Q12]</td>\n",
+       "      <td>0.9887+/-0.0005</td>\n",
+       "      <td>bad</td>\n",
+       "      <td>ibm_kyoto</td>\n",
+       "      <td>2024-03-21 16:29:46.643000+09:00</td>\n",
+       "      <td>(11, 12)</td>\n",
+       "      <td>large_chisq</td>\n",
+       "      <td>0.573717</td>\n",
+       "      <td>0.987961</td>\n",
+       "      <td>0.25327</td>\n",
+       "      <td>3.093085</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2df324aa</th>\n",
+       "      <td>ProcessFidelity</td>\n",
+       "      <td>LayerFidelity</td>\n",
+       "      <td>[Q9, Q10]</td>\n",
+       "      <td>0.9739+/-0.0012</td>\n",
+       "      <td>bad</td>\n",
+       "      <td>ibm_kyoto</td>\n",
+       "      <td>2024-03-21 16:29:46.643000+09:00</td>\n",
+       "      <td>(9, 10)</td>\n",
+       "      <td>large_chisq</td>\n",
+       "      <td>0.591139</td>\n",
+       "      <td>0.972162</td>\n",
+       "      <td>0.245885</td>\n",
+       "      <td>3.872122</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>d727e840</th>\n",
+       "      <td>ProcessFidelity</td>\n",
+       "      <td>LayerFidelity</td>\n",
+       "      <td>[Q8, Q7]</td>\n",
+       "      <td>0.835+/-0.016</td>\n",
+       "      <td>bad</td>\n",
+       "      <td>ibm_kyoto</td>\n",
+       "      <td>2024-03-21 16:29:46.643000+09:00</td>\n",
+       "      <td>(8, 7)</td>\n",
+       "      <td>large_spam</td>\n",
+       "      <td>0.421773</td>\n",
+       "      <td>0.824446</td>\n",
+       "      <td>0.252432</td>\n",
+       "      <td>0.985535</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>24c43177</th>\n",
+       "      <td>ProcessFidelity</td>\n",
+       "      <td>LayerFidelity</td>\n",
+       "      <td>[Q0, Q14]</td>\n",
+       "      <td>0.9755+/-0.0008</td>\n",
+       "      <td>bad</td>\n",
+       "      <td>ibm_kyoto</td>\n",
+       "      <td>2024-03-21 16:29:46.643000+09:00</td>\n",
+       "      <td>(0, 14)</td>\n",
+       "      <td>large_spam</td>\n",
+       "      <td>0.472265</td>\n",
+       "      <td>0.973889</td>\n",
+       "      <td>0.213734</td>\n",
+       "      <td>3.678971</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>c5d235ae</th>\n",
+       "      <td>ProcessFidelity</td>\n",
+       "      <td>LayerFidelity</td>\n",
+       "      <td>[Q18, Q19]</td>\n",
+       "      <td>1.000+/-0.031</td>\n",
+       "      <td>bad</td>\n",
+       "      <td>ibm_kyoto</td>\n",
+       "      <td>2024-03-21 16:29:46.643000+09:00</td>\n",
+       "      <td>(18, 19)</td>\n",
+       "      <td>large_spam</td>\n",
+       "      <td>0.270738</td>\n",
+       "      <td>0.999941</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.686612</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>57496351</th>\n",
+       "      <td>ProcessFidelity</td>\n",
+       "      <td>LayerFidelity</td>\n",
+       "      <td>[Q22, Q23]</td>\n",
+       "      <td>0.9693+/-0.0010</td>\n",
+       "      <td>bad</td>\n",
+       "      <td>ibm_kyoto</td>\n",
+       "      <td>2024-03-21 16:29:46.643000+09:00</td>\n",
+       "      <td>(22, 23)</td>\n",
+       "      <td>large_chisq</td>\n",
+       "      <td>0.619221</td>\n",
+       "      <td>0.967299</td>\n",
+       "      <td>0.248939</td>\n",
+       "      <td>3.046559</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>b9ecf0db</th>\n",
+       "      <td>ProcessFidelity</td>\n",
+       "      <td>LayerFidelity</td>\n",
+       "      <td>[Q43, Q42]</td>\n",
+       "      <td>0.9770+/-0.0011</td>\n",
+       "      <td>bad</td>\n",
+       "      <td>ibm_kyoto</td>\n",
+       "      <td>2024-03-21 16:29:46.643000+09:00</td>\n",
+       "      <td>(43, 42)</td>\n",
+       "      <td>large_chisq</td>\n",
+       "      <td>0.583279</td>\n",
+       "      <td>0.975414</td>\n",
+       "      <td>0.256407</td>\n",
+       "      <td>4.525672</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>dda89160</th>\n",
+       "      <td>ProcessFidelity</td>\n",
+       "      <td>LayerFidelity</td>\n",
+       "      <td>[Q52, Q37]</td>\n",
+       "      <td>0.9784+/-0.0009</td>\n",
+       "      <td>bad</td>\n",
+       "      <td>ibm_kyoto</td>\n",
+       "      <td>2024-03-21 16:29:46.643000+09:00</td>\n",
+       "      <td>(52, 37)</td>\n",
+       "      <td>large_spam</td>\n",
+       "      <td>0.436053</td>\n",
+       "      <td>0.976985</td>\n",
+       "      <td>0.187152</td>\n",
+       "      <td>0.723898</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                     name     experiment    components  \\\n",
+       "315b252c  ProcessFidelity  LayerFidelity  [Q112, Q108]   \n",
+       "8ba264a8  ProcessFidelity  LayerFidelity  [Q106, Q107]   \n",
+       "fd70efcf  ProcessFidelity  LayerFidelity  [Q105, Q104]   \n",
+       "329c0c1a  ProcessFidelity  LayerFidelity  [Q122, Q111]   \n",
+       "b1455dcc  ProcessFidelity  LayerFidelity  [Q121, Q120]   \n",
+       "ca0ff259  ProcessFidelity  LayerFidelity  [Q119, Q118]   \n",
+       "156ce79e  ProcessFidelity  LayerFidelity    [Q82, Q81]   \n",
+       "df2acdc7  ProcessFidelity  LayerFidelity    [Q80, Q79]   \n",
+       "e40cc5a4  ProcessFidelity  LayerFidelity    [Q98, Q91]   \n",
+       "91fa4b5e  ProcessFidelity  LayerFidelity    [Q76, Q77]   \n",
+       "15ed0950  ProcessFidelity  LayerFidelity    [Q62, Q61]   \n",
+       "435599c8  ProcessFidelity  LayerFidelity    [Q63, Q64]   \n",
+       "f0c016b0  ProcessFidelity  LayerFidelity    [Q65, Q66]   \n",
+       "79279b21  ProcessFidelity  LayerFidelity    [Q32, Q36]   \n",
+       "bccc0de8  ProcessFidelity  LayerFidelity      [Q8, Q9]   \n",
+       "4a949ae1  ProcessFidelity  LayerFidelity      [Q7, Q6]   \n",
+       "2ee122bb  ProcessFidelity  LayerFidelity      [Q1, Q0]   \n",
+       "d12da5c7  ProcessFidelity  LayerFidelity    [Q18, Q14]   \n",
+       "81cea09a  ProcessFidelity  LayerFidelity    [Q22, Q21]   \n",
+       "6e24abbe  ProcessFidelity  LayerFidelity    [Q25, Q26]   \n",
+       "36f100f1  ProcessFidelity  LayerFidelity    [Q37, Q38]   \n",
+       "cd138baa  ProcessFidelity  LayerFidelity  [Q107, Q108]   \n",
+       "2fa9a210  ProcessFidelity  LayerFidelity  [Q105, Q106]   \n",
+       "e8470f6b  ProcessFidelity  LayerFidelity  [Q104, Q111]   \n",
+       "4cb99579  ProcessFidelity  LayerFidelity   [Q102, Q92]   \n",
+       "1e0f2c3f  ProcessFidelity  LayerFidelity    [Q96, Q95]   \n",
+       "b339523a  ProcessFidelity  LayerFidelity    [Q94, Q90]   \n",
+       "de45c7fd  ProcessFidelity  LayerFidelity    [Q76, Q75]   \n",
+       "517c46b6  ProcessFidelity  LayerFidelity    [Q77, Q71]   \n",
+       "580222cf  ProcessFidelity  LayerFidelity    [Q73, Q66]   \n",
+       "f24d523d  ProcessFidelity  LayerFidelity    [Q88, Q87]   \n",
+       "635751a0  ProcessFidelity  LayerFidelity    [Q11, Q12]   \n",
+       "2df324aa  ProcessFidelity  LayerFidelity     [Q9, Q10]   \n",
+       "d727e840  ProcessFidelity  LayerFidelity      [Q8, Q7]   \n",
+       "24c43177  ProcessFidelity  LayerFidelity     [Q0, Q14]   \n",
+       "c5d235ae  ProcessFidelity  LayerFidelity    [Q18, Q19]   \n",
+       "57496351  ProcessFidelity  LayerFidelity    [Q22, Q23]   \n",
+       "b9ecf0db  ProcessFidelity  LayerFidelity    [Q43, Q42]   \n",
+       "dda89160  ProcessFidelity  LayerFidelity    [Q52, Q37]   \n",
+       "\n",
+       "                             value quality    backend  \\\n",
+       "315b252c           0.9741+/-0.0018     bad  ibm_kyoto   \n",
+       "8ba264a8           0.9750+/-0.0031     bad  ibm_kyoto   \n",
+       "fd70efcf  0.9999999999999999+/-nan     bad  ibm_kyoto   \n",
+       "329c0c1a             0.939+/-0.008     bad  ibm_kyoto   \n",
+       "b1455dcc           0.9858+/-0.0005     bad  ibm_kyoto   \n",
+       "ca0ff259           0.9922+/-0.0004     bad  ibm_kyoto   \n",
+       "156ce79e           0.9786+/-0.0008     bad  ibm_kyoto   \n",
+       "df2acdc7           0.9818+/-0.0006     bad  ibm_kyoto   \n",
+       "e40cc5a4           0.9756+/-0.0011     bad  ibm_kyoto   \n",
+       "91fa4b5e             0.906+/-0.014     bad  ibm_kyoto   \n",
+       "15ed0950           0.9833+/-0.0009     bad  ibm_kyoto   \n",
+       "435599c8           0.9822+/-0.0007     bad  ibm_kyoto   \n",
+       "f0c016b0           0.9910+/-0.0004     bad  ibm_kyoto   \n",
+       "79279b21           0.9889+/-0.0007     bad  ibm_kyoto   \n",
+       "bccc0de8           0.9643+/-0.0027     bad  ibm_kyoto   \n",
+       "4a949ae1             0.795+/-0.034     bad  ibm_kyoto   \n",
+       "2ee122bb           0.9821+/-0.0007     bad  ibm_kyoto   \n",
+       "d12da5c7             0.988+/-0.009     bad  ibm_kyoto   \n",
+       "81cea09a           0.9720+/-0.0008     bad  ibm_kyoto   \n",
+       "6e24abbe           0.9918+/-0.0004     bad  ibm_kyoto   \n",
+       "36f100f1           0.9828+/-0.0008     bad  ibm_kyoto   \n",
+       "cd138baa             0.962+/-0.004     bad  ibm_kyoto   \n",
+       "2fa9a210           0.9997+/-0.0029     bad  ibm_kyoto   \n",
+       "e8470f6b           0.9799+/-0.0019     bad  ibm_kyoto   \n",
+       "4cb99579           0.9736+/-0.0008     bad  ibm_kyoto   \n",
+       "1e0f2c3f           0.9834+/-0.0012     bad  ibm_kyoto   \n",
+       "b339523a           0.9773+/-0.0009     bad  ibm_kyoto   \n",
+       "de45c7fd             0.960+/-0.004     bad  ibm_kyoto   \n",
+       "517c46b6           0.9769+/-0.0018     bad  ibm_kyoto   \n",
+       "580222cf           0.9900+/-0.0004     bad  ibm_kyoto   \n",
+       "f24d523d           0.9865+/-0.0006     bad  ibm_kyoto   \n",
+       "635751a0           0.9887+/-0.0005     bad  ibm_kyoto   \n",
+       "2df324aa           0.9739+/-0.0012     bad  ibm_kyoto   \n",
+       "d727e840             0.835+/-0.016     bad  ibm_kyoto   \n",
+       "24c43177           0.9755+/-0.0008     bad  ibm_kyoto   \n",
+       "c5d235ae             1.000+/-0.031     bad  ibm_kyoto   \n",
+       "57496351           0.9693+/-0.0010     bad  ibm_kyoto   \n",
+       "b9ecf0db           0.9770+/-0.0011     bad  ibm_kyoto   \n",
+       "dda89160           0.9784+/-0.0009     bad  ibm_kyoto   \n",
+       "\n",
+       "                                  run_time      qubits       reason         a  \\\n",
+       "315b252c  2024-03-21 16:29:46.643000+09:00  (112, 108)   large_spam  0.403517   \n",
+       "8ba264a8  2024-03-21 16:29:46.643000+09:00  (106, 107)   large_spam  0.192179   \n",
+       "fd70efcf  2024-03-21 16:29:46.643000+09:00  (105, 104)   large_spam  0.003853   \n",
+       "329c0c1a  2024-03-21 16:29:46.643000+09:00  (122, 111)   large_spam  0.433793   \n",
+       "b1455dcc  2024-03-21 16:29:46.643000+09:00  (121, 120)  large_chisq  0.652881   \n",
+       "ca0ff259  2024-03-21 16:29:46.643000+09:00  (119, 118)  large_chisq  0.570897   \n",
+       "156ce79e  2024-03-21 16:29:46.643000+09:00    (82, 81)  large_chisq  0.612284   \n",
+       "df2acdc7  2024-03-21 16:29:46.643000+09:00    (80, 79)  large_chisq  0.652751   \n",
+       "e40cc5a4  2024-03-21 16:29:46.643000+09:00    (98, 91)  large_chisq  0.580621   \n",
+       "91fa4b5e  2024-03-21 16:29:46.643000+09:00    (76, 77)   large_spam  0.250542   \n",
+       "15ed0950  2024-03-21 16:29:46.643000+09:00    (62, 61)  large_chisq  0.585973   \n",
+       "435599c8  2024-03-21 16:29:46.643000+09:00    (63, 64)  large_chisq  0.669473   \n",
+       "f0c016b0  2024-03-21 16:29:46.643000+09:00    (65, 66)  large_chisq  0.660338   \n",
+       "79279b21  2024-03-21 16:29:46.643000+09:00    (32, 36)  large_chisq  0.675131   \n",
+       "bccc0de8  2024-03-21 16:29:46.643000+09:00      (8, 9)  large_chisq  0.548188   \n",
+       "4a949ae1  2024-03-21 16:29:46.643000+09:00      (7, 6)   large_spam  0.203485   \n",
+       "2ee122bb  2024-03-21 16:29:46.643000+09:00      (1, 0)   large_spam  0.442856   \n",
+       "d12da5c7  2024-03-21 16:29:46.643000+09:00    (18, 14)   large_spam  0.034896   \n",
+       "81cea09a  2024-03-21 16:29:46.643000+09:00    (22, 21)  large_chisq  0.589132   \n",
+       "6e24abbe  2024-03-21 16:29:46.643000+09:00    (25, 26)  large_chisq   0.61865   \n",
+       "36f100f1  2024-03-21 16:29:46.643000+09:00    (37, 38)   large_spam  0.432599   \n",
+       "cd138baa  2024-03-21 16:29:46.643000+09:00  (107, 108)   large_spam   0.14405   \n",
+       "2fa9a210  2024-03-21 16:29:46.643000+09:00  (105, 106)   large_spam  0.264829   \n",
+       "e8470f6b  2024-03-21 16:29:46.643000+09:00  (104, 111)  large_chisq  0.505645   \n",
+       "4cb99579  2024-03-21 16:29:46.643000+09:00   (102, 92)  large_chisq  0.632969   \n",
+       "1e0f2c3f  2024-03-21 16:29:46.643000+09:00    (96, 95)   large_spam  0.438003   \n",
+       "b339523a  2024-03-21 16:29:46.643000+09:00    (94, 90)  large_chisq  0.558095   \n",
+       "de45c7fd  2024-03-21 16:29:46.643000+09:00    (76, 75)   large_spam  0.422947   \n",
+       "517c46b6  2024-03-21 16:29:46.643000+09:00    (77, 71)   large_spam  0.432125   \n",
+       "580222cf  2024-03-21 16:29:46.643000+09:00    (73, 66)  large_chisq  0.649445   \n",
+       "f24d523d  2024-03-21 16:29:46.643000+09:00    (88, 87)  large_chisq  0.623668   \n",
+       "635751a0  2024-03-21 16:29:46.643000+09:00    (11, 12)  large_chisq  0.573717   \n",
+       "2df324aa  2024-03-21 16:29:46.643000+09:00     (9, 10)  large_chisq  0.591139   \n",
+       "d727e840  2024-03-21 16:29:46.643000+09:00      (8, 7)   large_spam  0.421773   \n",
+       "24c43177  2024-03-21 16:29:46.643000+09:00     (0, 14)   large_spam  0.472265   \n",
+       "c5d235ae  2024-03-21 16:29:46.643000+09:00    (18, 19)   large_spam  0.270738   \n",
+       "57496351  2024-03-21 16:29:46.643000+09:00    (22, 23)  large_chisq  0.619221   \n",
+       "b9ecf0db  2024-03-21 16:29:46.643000+09:00    (43, 42)  large_chisq  0.583279   \n",
+       "dda89160  2024-03-21 16:29:46.643000+09:00    (52, 37)   large_spam  0.436053   \n",
+       "\n",
+       "             alpha         b     chisq  \n",
+       "315b252c  0.972388  0.253707  1.211738  \n",
+       "8ba264a8  0.973366  0.212858  2.313055  \n",
+       "fd70efcf       1.0   0.25289   0.97619  \n",
+       "329c0c1a  0.934506   0.24728   3.67749  \n",
+       "b1455dcc  0.984827  0.239197  3.405308  \n",
+       "ca0ff259  0.991682   0.25806  3.158789  \n",
+       "156ce79e  0.977174  0.256304  4.146358  \n",
+       "df2acdc7  0.980545  0.253304  3.196182  \n",
+       "e40cc5a4  0.974021  0.222527  3.307072  \n",
+       "91fa4b5e  0.899955  0.194955  2.241609  \n",
+       "15ed0950  0.982237  0.245123  3.005546  \n",
+       "435599c8  0.981054  0.247685  4.958896  \n",
+       "f0c016b0  0.990358  0.267629  3.898633  \n",
+       "79279b21    0.9882  0.253997  4.471692  \n",
+       "bccc0de8  0.961901  0.257352  4.591003  \n",
+       "4a949ae1  0.780861  0.225911  6.248698  \n",
+       "2ee122bb    0.9809  0.230101  0.208882  \n",
+       "d12da5c7  0.987474  0.258215  1.719684  \n",
+       "81cea09a  0.970173  0.243312  3.683361  \n",
+       "6e24abbe  0.991259  0.259028  3.356521  \n",
+       "36f100f1  0.981673   0.21247   1.74983  \n",
+       "cd138baa   0.95956   0.22854  0.819488  \n",
+       "2fa9a210  0.999635       0.0  1.298543  \n",
+       "e8470f6b  0.978537  0.253888  3.324584  \n",
+       "4cb99579  0.971815  0.254668  3.131533  \n",
+       "1e0f2c3f   0.98226  0.238188  3.790113  \n",
+       "b339523a  0.975839  0.236842  3.869758  \n",
+       "de45c7fd  0.956955  0.238076  3.385566  \n",
+       "517c46b6  0.975324  0.234107  2.477181  \n",
+       "580222cf  0.989288  0.264381  3.445938  \n",
+       "f24d523d  0.985601  0.236159  3.406165  \n",
+       "635751a0  0.987961   0.25327  3.093085  \n",
+       "2df324aa  0.972162  0.245885  3.872122  \n",
+       "d727e840  0.824446  0.252432  0.985535  \n",
+       "24c43177  0.973889  0.213734  3.678971  \n",
+       "c5d235ae  0.999941       0.0  0.686612  \n",
+       "57496351  0.967299  0.248939  3.046559  \n",
+       "b9ecf0db  0.975414  0.256407  4.525672  \n",
+       "dda89160  0.976985  0.187152  0.723898  "
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# find bad quality analysis results\n",
+    "pfdf[pfdf.quality==\"bad\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "4170c823",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "datetime.datetime(2024, 3, 21, 16, 29, 46, 643000, tzinfo=tzlocal())"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "RUN_TIME = df[(df.name == \"LF\")].run_time.values[0]\n",
+    "RUN_TIME"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "43060a5a",
+   "metadata": {},
+   "source": [
+    "## Plot Layer Fidelity (EPLG) by chain length"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "id": "530a366a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# fill Process Fidelity values with zeros\n",
+    "pfdf = pfdf.fillna({\"value\": 0})"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "id": "202188f7",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "101"
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Compute LF by chain length assuming the first layer is full with 2q-gates\n",
+    "lf_sets, lf_qubits = two_disjoint_layers, best_qubit_chain\n",
+    "full_layer = [None] * (len(lf_sets[0]) + len(lf_sets[1]))\n",
+    "full_layer[::2] = lf_sets[0]\n",
+    "full_layer[1::2] = lf_sets[1]\n",
+    "full_layer = [(lf_qubits[0],)] + full_layer + [(lf_qubits[-1],)]\n",
+    "len(full_layer)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "id": "371d7436",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "assert(len(full_layer)== len(lf_qubits) + 1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "id": "80d4c824",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([0.92055217, 0.9741141 , 0.96208789, 0.97503078, 0.99965795,\n",
+       "       1.        , 0.97987882, 0.93859898, 0.99203894, 0.98577528,\n",
+       "       0.99035144, 0.99220233, 0.98818172, 0.98517995, 0.96736989,\n",
+       "       0.9790599 , 0.97357661, 0.97056358, 0.98155472, 0.97860038,\n",
+       "       0.9742577 , 0.98176111, 0.97835567, 0.97564462, 0.9652965 ,\n",
+       "       0.9890947 , 0.98336868, 0.99009675, 0.97734924, 0.93316685,\n",
+       "       0.95964551, 0.90620797, 0.97686608, 0.97599839, 0.97652024,\n",
+       "       0.9850133 , 0.98775448, 0.98334683, 0.98172138, 0.98223841,\n",
+       "       0.98999861, 0.99096077, 0.98995754, 0.99090252, 0.99226826,\n",
+       "       0.99107934, 0.98650061, 0.97199183, 0.98736921, 0.97831851,\n",
+       "       0.98856905, 0.98560185, 0.98954903, 0.98241734, 0.97179765,\n",
+       "       0.97997059, 0.96498726, 0.98893729, 0.98683034, 0.95981566,\n",
+       "       0.98999975, 0.98376994, 0.9887138 , 0.98631117, 0.9739017 ,\n",
+       "       0.96428246, 0.83541798, 0.7945571 , 0.99311771, 0.98481061,\n",
+       "       0.98572814, 0.97602161, 0.9794883 , 0.98209418, 0.97552063,\n",
+       "       0.98825718, 0.99994424, 0.99167623, 0.98821901, 0.97203675,\n",
+       "       0.96934318, 0.98762032, 0.98735948, 0.99180551, 0.98789597,\n",
+       "       0.98846725, 0.98315516, 0.98103707, 0.99263718, 0.98973811,\n",
+       "       0.9851167 , 0.99009538, 0.97695021, 0.98737435, 0.92521576,\n",
+       "       0.98922638, 0.92659756, 0.98281836, 0.97842359, 0.9859088 ,\n",
+       "       0.94850497])"
+      ]
+     },
+     "execution_count": 27,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pfs = [pfdf.loc[pfdf[pfdf.qubits==qubits].index[0], 'value'] for qubits in full_layer]\n",
+    "pfs = list(map(lambda x: x.n if x != 0 else 0, pfs))\n",
+    "pfs[0] = pfs[0]**2\n",
+    "pfs[-1] = pfs[-1]**2\n",
+    "np.array(pfs)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "id": "330f6c75",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "101"
+      ]
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "len(pfs)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "id": "3eb0afe8",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Approximate 1Q RB fidelities at both ends by the square root of 2Q RB fidelity at both ends.\n",
+    "# For example, if we have [(0, 1), (1, 2), (2, 3), (3, 4)] 2Q RB fidelities and if we want to compute a layer fidelity for [1, 2, 3],\n",
+    "# we approximate the 1Q filedities for (1,) and (3,) by the square root of 2Q fidelities of (0, 1) and (3, 4).\n",
+    "chain_lens = list(range(4, len(pfs), 2))\n",
+    "chain_fids = []\n",
+    "for length in chain_lens:\n",
+    "    w = length + 1  # window size\n",
+    "    fid_w = max(np.sqrt(pfs[s]) * np.prod(pfs[s+1:s+w-1]) * np.sqrt(pfs[s+w-1]) for s in range(len(pfs)-w+1))\n",
+    "    chain_fids.append(fid_w)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "id": "c02610b6",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Plot LF by chain length\n",
+    "plt.title(f\"Backend: {backend.name}, {RUN_TIME.strftime('%Y/%m/%d %H:%M')}\")\n",
+    "plt.plot(\n",
+    "    chain_lens,\n",
+    "    chain_fids,\n",
+    "    marker=\"o\",\n",
+    "    linestyle=\"-\",\n",
+    ")\n",
+    "plt.xlim(0, chain_lens[-1]*1.05)\n",
+    "plt.ylim(0.95*min(chain_fids), 1)\n",
+    "plt.ylabel(\"Layer Fidelity\")\n",
+    "plt.xlabel(\"Chain Length\")\n",
+    "plt.grid()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "id": "74603316",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Plot EPLG by chain length\n",
+    "num_2q_gates = [length -1 for length in chain_lens]\n",
+    "chain_eplgs = [1 - (fid ** (1 / num_2q)) for num_2q, fid in zip(num_2q_gates, chain_fids)]\n",
+    "plt.title(f\"Backend: {backend.name}, {RUN_TIME.strftime('%Y/%m/%d %H:%M')}\")\n",
+    "plt.plot(\n",
+    "    chain_lens,\n",
+    "    chain_eplgs,\n",
+    "    marker=\"o\",\n",
+    "    linestyle=\"-\",\n",
+    ")\n",
+    "plt.xlim(0, chain_lens[-1]*1.05)\n",
+    "plt.ylabel(\"Error per Layered Gates\")\n",
+    "plt.xlabel(\"Chain Length\")\n",
+    "plt.grid()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "062ba49f",
+   "metadata": {},
+   "source": [
+    "### Optional: Load Data from Results DB and rerun analysis"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "dd07a015",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# %%time\n",
+    "# # load results from DB with experiment_id\n",
+    "# from qiskit_experiments.framework import ExperimentData\n",
+    "# experiment_id = \"368f151b-63e9-49db-a36e-66a365948644\"  # REPLACE WITH YOUR OWN EXPERIMENT ID\n",
+    "# exp_data = ExperimentData.load(experiment_id, provider=provider)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "3d07c745",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# %%time\n",
+    "# # rerun analysis\n",
+    "# new_exp = LayerFidelity(\n",
+    "#     physical_qubits=exp_data.metadata[\"physical_qubits\"],\n",
+    "#     two_qubit_layers=[[tuple(qpair) for qpair in layer] for layer in exp_data.metadata[\"two_qubit_layers\"]],\n",
+    "#     lengths=[2, 4, 8, 16, 30, 50, 70, 100, 150, 200, 300, 500],\n",
+    "#     backend=backend,\n",
+    "#     num_samples=12,\n",
+    "#     seed=42,\n",
+    "# )\n",
+    "# new_exp.analysis.run(exp_data, replace_results=True)\n",
+    "# exp_data.block_for_results()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "adb248bf",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.13"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

From aa694849a2f65bb4a51847fb72a508e5b213e55a Mon Sep 17 00:00:00 2001
From: Toshinari Itoko <itoko@jp.ibm.com>
Date: Wed, 17 Apr 2024 14:06:57 +0900
Subject: [PATCH 2/9] Use ibm-runtime instead of ibm-provider

---
 notebooks/layer_fidelity.ipynb | 19 +++++--------------
 1 file changed, 5 insertions(+), 14 deletions(-)

diff --git a/notebooks/layer_fidelity.ipynb b/notebooks/layer_fidelity.ipynb
index af5360c..3931102 100644
--- a/notebooks/layer_fidelity.ipynb
+++ b/notebooks/layer_fidelity.ipynb
@@ -10,9 +10,10 @@
   },
   {
    "cell_type": "markdown",
+   "id": "b9b9fbb6",
    "metadata": {},
    "source": [
-    "`pip install \"qiskit-experiments>=0.7\" qiskit-ibm-provider` is required before running this notebook."
+    "`pip install \"qiskit-experiments>=0.7\" qiskit-ibm-runtime` is required before running this notebook."
    ]
   },
   {
@@ -32,7 +33,6 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "import math\n",
     "import numpy as np\n",
     "import rustworkx as rx\n",
     "import matplotlib.pyplot as plt"
@@ -45,17 +45,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from qiskit_ibm_provider import IBMProvider"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "0d3dd525",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "provider = IBMProvider()"
+    "from qiskit_ibm_runtime import QiskitRuntimeService\n",
+    "service = QiskitRuntimeService()"
    ]
   },
   {
@@ -75,7 +66,7 @@
    ],
    "source": [
     "#specify backend and two-qubit gate to be layered \n",
-    "backend = provider.get_backend('ibm_kyoto')\n",
+    "backend = service.backend('ibm_kyoto')\n",
     "twoq_gate = \"ecr\"\n",
     "print(f\"Device {backend.name} Loaded with {backend.num_qubits} qubits\")\n",
     "print(f\"Two Qubit Gate: {twoq_gate}\")"

From 350f594ae25bd12f834344ca0f8bd2d88303b30f Mon Sep 17 00:00:00 2001
From: Toshinari Itoko <itoko@jp.ibm.com>
Date: Fri, 19 Apr 2024 09:35:14 +0900
Subject: [PATCH 3/9] Clear the outputs

---
 notebooks/layer_fidelity.ipynb | 1691 +-------------------------------
 1 file changed, 52 insertions(+), 1639 deletions(-)

diff --git a/notebooks/layer_fidelity.ipynb b/notebooks/layer_fidelity.ipynb
index 3931102..73d29db 100644
--- a/notebooks/layer_fidelity.ipynb
+++ b/notebooks/layer_fidelity.ipynb
@@ -18,7 +18,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": null,
    "id": "f49e876a",
    "metadata": {},
    "outputs": [],
@@ -28,7 +28,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": null,
    "id": "78f23522",
    "metadata": {},
    "outputs": [],
@@ -40,7 +40,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": null,
    "id": "detailed-limitation",
    "metadata": {},
    "outputs": [],
@@ -51,19 +51,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": null,
    "id": "fa501105",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Device ibm_kyoto Loaded with 127 qubits\n",
-      "Two Qubit Gate: ecr\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "#specify backend and two-qubit gate to be layered \n",
     "backend = service.backend('ibm_kyoto')\n",
@@ -83,7 +74,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": null,
    "id": "7bc47055",
    "metadata": {},
    "outputs": [],
@@ -95,19 +86,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": null,
    "id": "8615f6d8",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Selecting the best from 313980 candidate paths (will take a few minutes)\n",
-      "Predicted LF from reported 2q-gate EPGs: 0.36495139725549536\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "def to_edges(path):\n",
     "    edges = []\n",
@@ -150,147 +132,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": null,
    "id": "efc3c14d",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([112, 108, 107, 106, 105, 104, 111, 122, 121, 120, 119, 118, 110,\n",
-       "       100, 101, 102,  92,  83,  82,  81,  80,  79,  91,  98,  97,  96,\n",
-       "        95,  94,  90,  75,  76,  77,  71,  58,  59,  60,  61,  62,  63,\n",
-       "        64,  65,  66,  73,  85,  86,  87,  88,  89,  74,  70,  69,  68,\n",
-       "        55,  49,  50,  51,  36,  32,  31,  30,  17,  12,  11,  10,   9,\n",
-       "         8,   7,   6,   5,   4,   3,   2,   1,   0,  14,  18,  19,  20,\n",
-       "        21,  22,  23,  24,  25,  26,  27,  28,  35,  47,  46,  45,  44,\n",
-       "        43,  42,  41,  40,  39,  38,  37,  52,  56], dtype=uint64)"
-      ]
-     },
-     "execution_count": 8,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "np.array(best_qubit_chain)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": null,
    "id": "e89c690a",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[[(112, 108),\n",
-       "  (106, 107),\n",
-       "  (105, 104),\n",
-       "  (122, 111),\n",
-       "  (121, 120),\n",
-       "  (119, 118),\n",
-       "  (110, 100),\n",
-       "  (101, 102),\n",
-       "  (83, 92),\n",
-       "  (82, 81),\n",
-       "  (80, 79),\n",
-       "  (98, 91),\n",
-       "  (96, 97),\n",
-       "  (94, 95),\n",
-       "  (75, 90),\n",
-       "  (76, 77),\n",
-       "  (71, 58),\n",
-       "  (59, 60),\n",
-       "  (62, 61),\n",
-       "  (63, 64),\n",
-       "  (65, 66),\n",
-       "  (73, 85),\n",
-       "  (87, 86),\n",
-       "  (89, 88),\n",
-       "  (74, 70),\n",
-       "  (68, 69),\n",
-       "  (49, 55),\n",
-       "  (50, 51),\n",
-       "  (32, 36),\n",
-       "  (30, 31),\n",
-       "  (17, 12),\n",
-       "  (11, 10),\n",
-       "  (8, 9),\n",
-       "  (7, 6),\n",
-       "  (4, 5),\n",
-       "  (3, 2),\n",
-       "  (1, 0),\n",
-       "  (18, 14),\n",
-       "  (19, 20),\n",
-       "  (22, 21),\n",
-       "  (23, 24),\n",
-       "  (25, 26),\n",
-       "  (27, 28),\n",
-       "  (47, 35),\n",
-       "  (46, 45),\n",
-       "  (44, 43),\n",
-       "  (42, 41),\n",
-       "  (39, 40),\n",
-       "  (37, 38),\n",
-       "  (56, 52)],\n",
-       " [(107, 108),\n",
-       "  (105, 106),\n",
-       "  (104, 111),\n",
-       "  (122, 121),\n",
-       "  (120, 119),\n",
-       "  (118, 110),\n",
-       "  (101, 100),\n",
-       "  (102, 92),\n",
-       "  (82, 83),\n",
-       "  (81, 80),\n",
-       "  (79, 91),\n",
-       "  (97, 98),\n",
-       "  (96, 95),\n",
-       "  (94, 90),\n",
-       "  (76, 75),\n",
-       "  (77, 71),\n",
-       "  (59, 58),\n",
-       "  (61, 60),\n",
-       "  (62, 63),\n",
-       "  (64, 65),\n",
-       "  (73, 66),\n",
-       "  (86, 85),\n",
-       "  (88, 87),\n",
-       "  (89, 74),\n",
-       "  (70, 69),\n",
-       "  (55, 68),\n",
-       "  (50, 49),\n",
-       "  (51, 36),\n",
-       "  (31, 32),\n",
-       "  (17, 30),\n",
-       "  (11, 12),\n",
-       "  (9, 10),\n",
-       "  (8, 7),\n",
-       "  (6, 5),\n",
-       "  (4, 3),\n",
-       "  (1, 2),\n",
-       "  (0, 14),\n",
-       "  (18, 19),\n",
-       "  (21, 20),\n",
-       "  (22, 23),\n",
-       "  (25, 24),\n",
-       "  (27, 26),\n",
-       "  (28, 35),\n",
-       "  (47, 46),\n",
-       "  (44, 45),\n",
-       "  (43, 42),\n",
-       "  (40, 41),\n",
-       "  (38, 39),\n",
-       "  (52, 37)]]"
-      ]
-     },
-     "execution_count": 9,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "# decompose the chain into two disjoint layers\n",
     "all_pairs = to_edges(best_qubit_chain)\n",
@@ -325,21 +180,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": null,
    "id": "russian-billion",
    "metadata": {
     "scrolled": false
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "CPU times: user 20.2 ms, sys: 504 µs, total: 20.7 ms\n",
-      "Wall time: 20.5 ms\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "%%time\n",
     "lfexp = LayerFidelity(\n",
@@ -356,40 +202,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": null,
    "id": "50e33867",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Options(max_circuits=None, lengths=[2, 4, 8, 16, 30, 50, 70, 100, 150, 200, 300, 500], num_samples=12, seed=42, two_qubit_layers=[[(112, 108), (106, 107), (105, 104), (122, 111), (121, 120), (119, 118), (110, 100), (101, 102), (83, 92), (82, 81), (80, 79), (98, 91), (96, 97), (94, 95), (75, 90), (76, 77), (71, 58), (59, 60), (62, 61), (63, 64), (65, 66), (73, 85), (87, 86), (89, 88), (74, 70), (68, 69), (49, 55), (50, 51), (32, 36), (30, 31), (17, 12), (11, 10), (8, 9), (7, 6), (4, 5), (3, 2), (1, 0), (18, 14), (19, 20), (22, 21), (23, 24), (25, 26), (27, 28), (47, 35), (46, 45), (44, 43), (42, 41), (39, 40), (37, 38), (56, 52)], [(107, 108), (105, 106), (104, 111), (122, 121), (120, 119), (118, 110), (101, 100), (102, 92), (82, 83), (81, 80), (79, 91), (97, 98), (96, 95), (94, 90), (76, 75), (77, 71), (59, 58), (61, 60), (62, 63), (64, 65), (73, 66), (86, 85), (88, 87), (89, 74), (70, 69), (55, 68), (50, 49), (51, 36), (31, 32), (17, 30), (11, 12), (9, 10), (8, 7), (6, 5), (4, 3), (1, 2), (0, 14), (18, 19), (21, 20), (22, 23), (25, 24), (27, 26), (28, 35), (47, 46), (44, 45), (43, 42), (40, 41), (38, 39), (52, 37)]], two_qubit_gate='ecr', one_qubit_basis_gates=('sx', 'id', 'x', 'rz'), clifford_synthesis_method='rb_default')"
-      ]
-     },
-     "execution_count": 11,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "lfexp.experiment_options"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": null,
    "id": "c033c62c",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Two Qubit Gate: ecr\n",
-      "One Qubit Gate Set: ('sx', 'id', 'x', 'rz')\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "print(f\"Two Qubit Gate: {lfexp.experiment_options.two_qubit_gate}\")\n",
     "print(f\"One Qubit Gate Set: {lfexp.experiment_options.one_qubit_basis_gates}\")"
@@ -406,30 +232,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": null,
    "id": "114f5349",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "CPU times: user 3.31 s, sys: 24.6 ms, total: 3.33 s\n",
-      "Wall time: 3.34 s\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 8781.79x6104.28 with 1 Axes>"
-      ]
-     },
-     "execution_count": 13,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "%%time\n",
     "# look at one of the first three 2Q direct RB circuits quickly\n",
@@ -448,20 +254,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": null,
    "id": "a4dfc21d",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "288 circuits are generated.\n",
-      "CPU times: user 43 s, sys: 68.5 ms, total: 43.1 s\n",
-      "Wall time: 43.2 s\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "%%time\n",
     "# generate all circuits to run\n",
@@ -483,20 +279,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": null,
    "id": "iraqi-dominican",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Run experiment: ID=368f151b-63e9-49db-a36e-66a365948644 with jobs ['cqxrpb9p3cg0008cjds0']]\n",
-      "CPU times: user 1min 59s, sys: 371 ms, total: 2min\n",
-      "Wall time: 2min 17s\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "%%time\n",
     "#number of shots per job\n",
@@ -519,29 +305,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": null,
    "id": "banner-trader",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "CPU times: user 5h 27min 43s, sys: 3min 18s, total: 5h 31min 1s\n",
-      "Wall time: 6h 15min 9s\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "ExperimentData(LayerFidelity, 368f151b-63e9-49db-a36e-66a365948644, job_ids=['cqxrpb9p3cg0008cjds0'], metadata=<4 items>, figure_names=['DirectRB_Q112_Q108.svg', 'DirectRB_Q106_Q107.svg', 'DirectRB_Q105_Q104.svg', 'DirectRB_Q122_Q111.svg', 'DirectRB_Q121_Q120.svg', 'DirectRB_Q119_Q118.svg', 'DirectRB_Q110_Q100.svg', 'DirectRB_Q101_Q102.svg', 'DirectRB_Q83_Q92.svg', 'DirectRB_Q82_Q81.svg', 'DirectRB_Q80_Q79.svg', 'DirectRB_Q98_Q91.svg', 'DirectRB_Q96_Q97.svg', 'DirectRB_Q94_Q95.svg', 'DirectRB_Q75_Q90.svg', 'DirectRB_Q76_Q77.svg', 'DirectRB_Q71_Q58.svg', 'DirectRB_Q59_Q60.svg', 'DirectRB_Q62_Q61.svg', 'DirectRB_Q63_Q64.svg', 'DirectRB_Q65_Q66.svg', 'DirectRB_Q73_Q85.svg', 'DirectRB_Q87_Q86.svg', 'DirectRB_Q89_Q88.svg', 'DirectRB_Q74_Q70.svg', 'DirectRB_Q68_Q69.svg', 'DirectRB_Q49_Q55.svg', 'DirectRB_Q50_Q51.svg', 'DirectRB_Q32_Q36.svg', 'DirectRB_Q30_Q31.svg', 'DirectRB_Q17_Q12.svg', 'DirectRB_Q11_Q10.svg', 'DirectRB_Q8_Q9.svg', 'DirectRB_Q7_Q6.svg', 'DirectRB_Q4_Q5.svg', 'DirectRB_Q3_Q2.svg', 'DirectRB_Q1_Q0.svg', 'DirectRB_Q18_Q14.svg', 'DirectRB_Q19_Q20.svg', 'DirectRB_Q22_Q21.svg', 'DirectRB_Q23_Q24.svg', 'DirectRB_Q25_Q26.svg', 'DirectRB_Q27_Q28.svg', 'DirectRB_Q47_Q35.svg', 'DirectRB_Q46_Q45.svg', 'DirectRB_Q44_Q43.svg', 'DirectRB_Q42_Q41.svg', 'DirectRB_Q39_Q40.svg', 'DirectRB_Q37_Q38.svg', 'DirectRB_Q56_Q52.svg', 'DirectRB_Q107_Q108.svg', 'DirectRB_Q105_Q106.svg', 'DirectRB_Q104_Q111.svg', 'DirectRB_Q122_Q121.svg', 'DirectRB_Q120_Q119.svg', 'DirectRB_Q118_Q110.svg', 'DirectRB_Q101_Q100.svg', 'DirectRB_Q102_Q92.svg', 'DirectRB_Q82_Q83.svg', 'DirectRB_Q81_Q80.svg', 'DirectRB_Q79_Q91.svg', 'DirectRB_Q97_Q98.svg', 'DirectRB_Q96_Q95.svg', 'DirectRB_Q94_Q90.svg', 'DirectRB_Q76_Q75.svg', 'DirectRB_Q77_Q71.svg', 'DirectRB_Q59_Q58.svg', 'DirectRB_Q61_Q60.svg', 'DirectRB_Q62_Q63.svg', 'DirectRB_Q64_Q65.svg', 'DirectRB_Q73_Q66.svg', 'DirectRB_Q86_Q85.svg', 'DirectRB_Q88_Q87.svg', 'DirectRB_Q89_Q74.svg', 'DirectRB_Q70_Q69.svg', 'DirectRB_Q55_Q68.svg', 'DirectRB_Q50_Q49.svg', 'DirectRB_Q51_Q36.svg', 'DirectRB_Q31_Q32.svg', 'DirectRB_Q17_Q30.svg', 'DirectRB_Q11_Q12.svg', 'DirectRB_Q9_Q10.svg', 'DirectRB_Q8_Q7.svg', 'DirectRB_Q6_Q5.svg', 'DirectRB_Q4_Q3.svg', 'DirectRB_Q1_Q2.svg', 'DirectRB_Q0_Q14.svg', 'DirectRB_Q18_Q19.svg', 'DirectRB_Q21_Q20.svg', 'DirectRB_Q22_Q23.svg', 'DirectRB_Q25_Q24.svg', 'DirectRB_Q27_Q26.svg', 'DirectRB_Q28_Q35.svg', 'DirectRB_Q47_Q46.svg', 'DirectRB_Q44_Q45.svg', 'DirectRB_Q43_Q42.svg', 'DirectRB_Q40_Q41.svg', 'DirectRB_Q38_Q39.svg', 'DirectRB_Q52_Q37.svg', 'DirectRB_Q112.svg', 'DirectRB_Q56.svg'])"
-      ]
-     },
-     "execution_count": 16,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "%%time\n",
     "exp_data.block_for_results()"
@@ -557,7 +324,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": null,
    "id": "fabulous-platinum",
    "metadata": {},
    "outputs": [],
@@ -568,158 +335,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": null,
    "id": "a3d31324",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>name</th>\n",
-       "      <th>experiment</th>\n",
-       "      <th>components</th>\n",
-       "      <th>value</th>\n",
-       "      <th>quality</th>\n",
-       "      <th>backend</th>\n",
-       "      <th>run_time</th>\n",
-       "      <th>qubits</th>\n",
-       "      <th>reason</th>\n",
-       "      <th>a</th>\n",
-       "      <th>alpha</th>\n",
-       "      <th>b</th>\n",
-       "      <th>chisq</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>41b501b0</th>\n",
-       "      <td>LF</td>\n",
-       "      <td>LayerFidelity</td>\n",
-       "      <td>[Q112, Q108, Q107, Q106, Q105, Q104, Q111, Q12...</td>\n",
-       "      <td>0.08425551354634768+/-nan</td>\n",
-       "      <td>bad</td>\n",
-       "      <td>ibm_kyoto</td>\n",
-       "      <td>2024-03-21 16:29:46.643000+09:00</td>\n",
-       "      <td>None</td>\n",
-       "      <td>None</td>\n",
-       "      <td>None</td>\n",
-       "      <td>None</td>\n",
-       "      <td>None</td>\n",
-       "      <td>None</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>9b098b6b</th>\n",
-       "      <td>EPLG</td>\n",
-       "      <td>LayerFidelity</td>\n",
-       "      <td>[Q112, Q108, Q107, Q106, Q105, Q104, Q111, Q12...</td>\n",
-       "      <td>0.024679263638514026+/-nan</td>\n",
-       "      <td>bad</td>\n",
-       "      <td>ibm_kyoto</td>\n",
-       "      <td>2024-03-21 16:29:46.643000+09:00</td>\n",
-       "      <td>None</td>\n",
-       "      <td>None</td>\n",
-       "      <td>None</td>\n",
-       "      <td>None</td>\n",
-       "      <td>None</td>\n",
-       "      <td>None</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5efada9c</th>\n",
-       "      <td>SingleLF</td>\n",
-       "      <td>LayerFidelity</td>\n",
-       "      <td>[Q112, Q108, Q106, Q107, Q105, Q104, Q122, Q11...</td>\n",
-       "      <td>0.2888253412891042+/-nan</td>\n",
-       "      <td>bad</td>\n",
-       "      <td>ibm_kyoto</td>\n",
-       "      <td>2024-03-21 16:29:46.643000+09:00</td>\n",
-       "      <td>[112, 108, 106, 107, 105, 104, 122, 111, 121, ...</td>\n",
-       "      <td>None</td>\n",
-       "      <td>None</td>\n",
-       "      <td>None</td>\n",
-       "      <td>None</td>\n",
-       "      <td>None</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>9770d221</th>\n",
-       "      <td>SingleLF</td>\n",
-       "      <td>LayerFidelity</td>\n",
-       "      <td>[Q107, Q108, Q105, Q106, Q104, Q111, Q122, Q12...</td>\n",
-       "      <td>0.292+/-0.011</td>\n",
-       "      <td>bad</td>\n",
-       "      <td>ibm_kyoto</td>\n",
-       "      <td>2024-03-21 16:29:46.643000+09:00</td>\n",
-       "      <td>[107, 108, 105, 106, 104, 111, 122, 121, 120, ...</td>\n",
-       "      <td>None</td>\n",
-       "      <td>None</td>\n",
-       "      <td>None</td>\n",
-       "      <td>None</td>\n",
-       "      <td>None</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "              name     experiment  \\\n",
-       "41b501b0        LF  LayerFidelity   \n",
-       "9b098b6b      EPLG  LayerFidelity   \n",
-       "5efada9c  SingleLF  LayerFidelity   \n",
-       "9770d221  SingleLF  LayerFidelity   \n",
-       "\n",
-       "                                                 components  \\\n",
-       "41b501b0  [Q112, Q108, Q107, Q106, Q105, Q104, Q111, Q12...   \n",
-       "9b098b6b  [Q112, Q108, Q107, Q106, Q105, Q104, Q111, Q12...   \n",
-       "5efada9c  [Q112, Q108, Q106, Q107, Q105, Q104, Q122, Q11...   \n",
-       "9770d221  [Q107, Q108, Q105, Q106, Q104, Q111, Q122, Q12...   \n",
-       "\n",
-       "                               value quality    backend  \\\n",
-       "41b501b0   0.08425551354634768+/-nan     bad  ibm_kyoto   \n",
-       "9b098b6b  0.024679263638514026+/-nan     bad  ibm_kyoto   \n",
-       "5efada9c    0.2888253412891042+/-nan     bad  ibm_kyoto   \n",
-       "9770d221               0.292+/-0.011     bad  ibm_kyoto   \n",
-       "\n",
-       "                                  run_time  \\\n",
-       "41b501b0  2024-03-21 16:29:46.643000+09:00   \n",
-       "9b098b6b  2024-03-21 16:29:46.643000+09:00   \n",
-       "5efada9c  2024-03-21 16:29:46.643000+09:00   \n",
-       "9770d221  2024-03-21 16:29:46.643000+09:00   \n",
-       "\n",
-       "                                                     qubits reason     a  \\\n",
-       "41b501b0                                               None   None  None   \n",
-       "9b098b6b                                               None   None  None   \n",
-       "5efada9c  [112, 108, 106, 107, 105, 104, 122, 111, 121, ...   None  None   \n",
-       "9770d221  [107, 108, 105, 106, 104, 111, 122, 121, 120, ...   None  None   \n",
-       "\n",
-       "         alpha     b chisq  \n",
-       "41b501b0  None  None  None  \n",
-       "9b098b6b  None  None  None  \n",
-       "5efada9c  None  None  None  \n",
-       "9770d221  None  None  None  "
-      ]
-     },
-     "execution_count": 18,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "# LF and LF of each disjoing set\n",
     "df[(df.name == \"LF\") | (df.name == \"EPLG\") | (df.name == \"SingleLF\")]"
@@ -727,166 +346,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": null,
    "id": "d80948c0",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>name</th>\n",
-       "      <th>experiment</th>\n",
-       "      <th>components</th>\n",
-       "      <th>value</th>\n",
-       "      <th>quality</th>\n",
-       "      <th>backend</th>\n",
-       "      <th>run_time</th>\n",
-       "      <th>qubits</th>\n",
-       "      <th>reason</th>\n",
-       "      <th>a</th>\n",
-       "      <th>alpha</th>\n",
-       "      <th>b</th>\n",
-       "      <th>chisq</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>315b252c</th>\n",
-       "      <td>ProcessFidelity</td>\n",
-       "      <td>LayerFidelity</td>\n",
-       "      <td>[Q112, Q108]</td>\n",
-       "      <td>0.9741+/-0.0018</td>\n",
-       "      <td>bad</td>\n",
-       "      <td>ibm_kyoto</td>\n",
-       "      <td>2024-03-21 16:29:46.643000+09:00</td>\n",
-       "      <td>(112, 108)</td>\n",
-       "      <td>large_spam</td>\n",
-       "      <td>0.403517</td>\n",
-       "      <td>0.972388</td>\n",
-       "      <td>0.253707</td>\n",
-       "      <td>1.211738</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>8ba264a8</th>\n",
-       "      <td>ProcessFidelity</td>\n",
-       "      <td>LayerFidelity</td>\n",
-       "      <td>[Q106, Q107]</td>\n",
-       "      <td>0.9750+/-0.0031</td>\n",
-       "      <td>bad</td>\n",
-       "      <td>ibm_kyoto</td>\n",
-       "      <td>2024-03-21 16:29:46.643000+09:00</td>\n",
-       "      <td>(106, 107)</td>\n",
-       "      <td>large_spam</td>\n",
-       "      <td>0.192179</td>\n",
-       "      <td>0.973366</td>\n",
-       "      <td>0.212858</td>\n",
-       "      <td>2.313055</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>fd70efcf</th>\n",
-       "      <td>ProcessFidelity</td>\n",
-       "      <td>LayerFidelity</td>\n",
-       "      <td>[Q105, Q104]</td>\n",
-       "      <td>0.9999999999999999+/-nan</td>\n",
-       "      <td>bad</td>\n",
-       "      <td>ibm_kyoto</td>\n",
-       "      <td>2024-03-21 16:29:46.643000+09:00</td>\n",
-       "      <td>(105, 104)</td>\n",
-       "      <td>large_spam</td>\n",
-       "      <td>0.003853</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>0.25289</td>\n",
-       "      <td>0.97619</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>329c0c1a</th>\n",
-       "      <td>ProcessFidelity</td>\n",
-       "      <td>LayerFidelity</td>\n",
-       "      <td>[Q122, Q111]</td>\n",
-       "      <td>0.939+/-0.008</td>\n",
-       "      <td>bad</td>\n",
-       "      <td>ibm_kyoto</td>\n",
-       "      <td>2024-03-21 16:29:46.643000+09:00</td>\n",
-       "      <td>(122, 111)</td>\n",
-       "      <td>large_spam</td>\n",
-       "      <td>0.433793</td>\n",
-       "      <td>0.934506</td>\n",
-       "      <td>0.24728</td>\n",
-       "      <td>3.67749</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>b1455dcc</th>\n",
-       "      <td>ProcessFidelity</td>\n",
-       "      <td>LayerFidelity</td>\n",
-       "      <td>[Q121, Q120]</td>\n",
-       "      <td>0.9858+/-0.0005</td>\n",
-       "      <td>bad</td>\n",
-       "      <td>ibm_kyoto</td>\n",
-       "      <td>2024-03-21 16:29:46.643000+09:00</td>\n",
-       "      <td>(121, 120)</td>\n",
-       "      <td>large_chisq</td>\n",
-       "      <td>0.652881</td>\n",
-       "      <td>0.984827</td>\n",
-       "      <td>0.239197</td>\n",
-       "      <td>3.405308</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                     name     experiment    components  \\\n",
-       "315b252c  ProcessFidelity  LayerFidelity  [Q112, Q108]   \n",
-       "8ba264a8  ProcessFidelity  LayerFidelity  [Q106, Q107]   \n",
-       "fd70efcf  ProcessFidelity  LayerFidelity  [Q105, Q104]   \n",
-       "329c0c1a  ProcessFidelity  LayerFidelity  [Q122, Q111]   \n",
-       "b1455dcc  ProcessFidelity  LayerFidelity  [Q121, Q120]   \n",
-       "\n",
-       "                             value quality    backend  \\\n",
-       "315b252c           0.9741+/-0.0018     bad  ibm_kyoto   \n",
-       "8ba264a8           0.9750+/-0.0031     bad  ibm_kyoto   \n",
-       "fd70efcf  0.9999999999999999+/-nan     bad  ibm_kyoto   \n",
-       "329c0c1a             0.939+/-0.008     bad  ibm_kyoto   \n",
-       "b1455dcc           0.9858+/-0.0005     bad  ibm_kyoto   \n",
-       "\n",
-       "                                  run_time      qubits       reason         a  \\\n",
-       "315b252c  2024-03-21 16:29:46.643000+09:00  (112, 108)   large_spam  0.403517   \n",
-       "8ba264a8  2024-03-21 16:29:46.643000+09:00  (106, 107)   large_spam  0.192179   \n",
-       "fd70efcf  2024-03-21 16:29:46.643000+09:00  (105, 104)   large_spam  0.003853   \n",
-       "329c0c1a  2024-03-21 16:29:46.643000+09:00  (122, 111)   large_spam  0.433793   \n",
-       "b1455dcc  2024-03-21 16:29:46.643000+09:00  (121, 120)  large_chisq  0.652881   \n",
-       "\n",
-       "             alpha         b     chisq  \n",
-       "315b252c  0.972388  0.253707  1.211738  \n",
-       "8ba264a8  0.973366  0.212858  2.313055  \n",
-       "fd70efcf       1.0   0.25289   0.97619  \n",
-       "329c0c1a  0.934506   0.24728   3.67749  \n",
-       "b1455dcc  0.984827  0.239197  3.405308  "
-      ]
-     },
-     "execution_count": 19,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "# 1Q/2Q process fidelities\n",
     "df[(df.name == \"ProcessFidelity\")].head()"
@@ -902,61 +365,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": null,
    "id": "unlike-python",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<qiskit_experiments.framework.containers.figure_data.FigureData at 0x353a44d00>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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+bT2D1Ra5i7Te2/Q9yU22axfY27dgBS7NWP9sb6bnhDpzsYaCtdnl+DJwYZby/gBW1XoIK8h4N71HH/yX/Ttos38fG4HP5fC7XMXhHvdJ+/j1WB+GC7Kk76usb+nj/GdifSi12Pe4C+sD68yMdJcCr9j32G7//N6MMtiM1Scha6/1zDySZUIerBoJA3ww4/ea/mgA/gFckOPfw+lYH1yddtk/BZw20Hs5rfxX5Xid47GGxNZjTSpl7PfWmVnSOrCGFO7Gel8/gRWY9jX64ATgabv8D2IFO+kT+Syj9+iDeVjDDoP2vvS/l1eAuwd4v/V4ZEn7KayOmBGs2VOv76NcTrPzHsR6//6B3n+nn7Wvc3wO5Zw1f5m/J6xakh/Z5RW273lZH+e8zX7v+ge49nbgB7m8H8b7Q+wbVkoplQdiTQV8J1bwN1CNxoiyv5nfBkwyvcfyjzgReQBreOVFo52XvojI6VjNaccba4TXUU2DAqWUyjMR+U+s5oorjTG5dAgdEXb7+kbgV8aYH4yB/OzDmj74xdHOS19E5PdAizHmYwMmPgpoUKCUUscQu5/KycaY20c7L2OdPdPhv2NNZDUWFpAbdhoUKKWUUgrQIYlKKaWUsh3zQxKrq6vN9OnT83Kurq4uioqKBk6o+qRlmB9ajkOnZZgfWo5DNxxluGbNmkZjTE3m9mM+KJg+fTqvvfZaXs61atUqli1blpdzHau0DPNDy3HotAzzQ8tx6IajDEVkd7bt2nyglFJKKUCDAqWUUkrZNChQSimlFKBBgVJKKaVsGhQopZRSCtCgQCmllFI2DQqUUkopBWhQoJRSSimbBgXDIBAIsG7dOgKBwGhnRSmllMrZMT+jYb4FAgFWrlxJIpHA6XSyYsUK/H7/aGdLKaWUGpDWFORZfX09iUSCwsJCEokE9fX1o50lpZRSKiejEhSIyPUislNEwiKyRkTO7SftMhExWR7zMtJdIiKbRSRiP39w+O+kt9raWpxOJ8FgEKfTSW1t7WhkQymllBq0EW8+EJHLgNuA64Hn7ee/iMh8Y8yefg5dADSnvT6Uds4zgYeBm4DfAR8CHhGRs40xr+T5Fvrl9/tZsWIF9fX11NbWatOBUkqpcWM0+hR8EbjHGHOX/foGEbkQ+Azw1X6OazDGNPax7/PA08aY79qvvysiy+3tVww9y4Pj9/s1GFBKKTXujGjzgYh4gFOAJzN2PQmcNcDhr4nIARF5yv7AT3dmlnM+kcM5lVJKKWUb6ZqCasAJZI7VCwDv6uOYA1i1CK8CHuCjwFMistQY85ydZmIf55yY7YQich1wHVjf6letWjW4u+hDZ2dn3s51rNIyzA8tx6HTMswPLcehG8kyHPNDEo0xW4GtaZteEpHpwJeB57IeNPA57wTuBFiyZIlZtmzZEHNpWbVqFfk617FKyzA/tByHTsswP7Qch24ky3CkRx80Agkgs8HdDxwcxHleAerSXh/MwzmVUkqpY9qIBgXGmCiwBjg/Y9f5wIuDONUirGaFlJfycE6llFLqmDYazQc/Au4TkdXAC8CngVrgDgARuRfAGHO1/frzwC7gDaw+BR8BPgBcknbO24BnReRG4A/AB4HlwDnDfC9KKaXUUWPEgwJjzMMiUgV8HZgEbAIuMsbstpNMzTjEA3wfmAKEsIKDi40xf04754sicjlwC/Bt4G3gspGeo0AppZQaz0alo6Ex5nbg9j72Lct4/d/Af+dwzt8Cv81H/kZKIBDQSY6UUkqNGWN+9MHRShdOUkopNdbogkijRBdOUkopNdZoUDBKdOEkpZRSY402H4wSXThJKaXUWKNBwSjShZOUUkqNJdp8MAwCgQDr1q0jEMhcjkEppZQau7SmIM90VIFSSqnxSmsK8kxHFSillBqvNCjIMx1VoJRSarzS5oM801EFSimlxisNCoaBjipQSik1HmnzgVJKKaUADQryKh6P61BEpZRS45Y2H+RJIBCgubmZTZs26VBEpZRS45LWFORJauihDkVUSik1XmlQkCepoYc6FFEppdR4lXPzgYhcA1wBTAUKMnYbY8ysfGZsvPH7/VRWVlJXV6dDEZVSSo1LOQUFIvIN4FvAJmA9EBnGPI1bLpeLxYsXj3Y2lFJKqSOSa03Bx4HbjDFfGM7MKKWUUmr05NqnoAp4bDgzopRSSqnRlWtQ8AywcDgzopRSSqnRlWtQ8Hngn0XkahGpFhFH5mMwFxWR60Vkp4iERWSNiJyb43HniEhcRDZlbL9WREyWR2aHSKWUUkr1Idc+Bdvs57v72G9yPZeIXAbcBlwPPG8//0VE5htj9vRzXAVwL/AUMDlLkiDQYwSEMSacS57yLRAI6IJISimlxp1cg4JvY33w58MXgXuMMXfZr28QkQuBzwBf7ee4XwG/BgS4NMt+Y4w5mKc8HrFAIMDKlStJJBI6s6FSSqlxJaegwBhzcz4uJiIe4BTgBxm7ngTO6ue46wE/cAvwjT6S+URkN+DEGjb5DWPMuqHmebDq6+tJJBIUFhYSDAapr6/XoEAppdS4IMYMrgJARIqBCqDFGNM5yGNrgf3AUmPMs2nbvwlcZYyZm+WYE4G/A2cYY3aKyM3ApcaYE9LSnAnMAV4HSoB/BS4CFhpjtmc553XAdQB+v/+Uhx56aDC30aeOjg5EhGAw2L2tsrISl0uXmMhVZ2cnxcXFo52NcU/Lcei0DPNDy3HohqMMly9fvsYYsyRz+2BmNLwA+C6wCKsK34jIWuBrxpi/5SujGdf0Ag8D/2aM2dlXOmPMS8BLace9iFVbcAPwuSzp7wTuBFiyZIlZtmzZkPMaCAR45plnqK+vxxjDySefzLx587SWYJBWrVpFPn4fxzotx6HTMswPLcehG8kyzGnUgB0Q/AkoBr6D1TnwFqxv5X8WkfNzvF4jkMBqCkjnB7L1B5gEHA/cbY86iAPfBBbYr9+d7SLGmATwGlCXY76GLH1BJBGhtLRUAwKllFLjSq41BTdjtfu/1xiTTG0UkW8Dj2NNgTxgbYExJioia4DzgUfSdp0PPJrlkP3AiRnbrrfTfxDYle06IiLASVjNCSOitraW7du364JISimlxq1cg4KFwIfTAwIAY0xSRG4HVg7imj8C7hOR1cALwKeBWuAOABG51z731caYGNZ6C91EpAGIGGM2pW27CXgZ2A6UYjUZnIQ1omFE6IJISimlxrtcg4II1odtNiUMYoEkY8zDIlIFfB2reWATcJExZredZGqu50pTjtVHYCLQBqwDzjPGrD6Ccx0xXRBJKaXUeJZrULAK+I6IvJze4U9EpmI1LTw9mIsaY24Hbu9j37IBjr3Zvmb6ti8AuliTUkopNQS5BgVfwarq3yoiLwMHsL6VnwG02vuVUkopNY7lNPrAGLMNq43+J4AXOBkowJqueFG2uQCUUkopNb7kPE+BMeYA8G/DmBellFJKjaJBrW6olFJKqaNXnzUFIvIP4HpjzBb75/4YY8w785u18Scej7Nu3TodkqiUUmpc6q/5QNJ+dtD/KonSz75jQiAQoLm5mU2bNunqiEoppcalPoMCY8zytJ+XjUhuxrH0aY47Ojp4+eWXOeOMMzQwUEopNW7kuvbB1faEQ9n2VYrI1fnN1viTmta4ubmZYDDIW2+9xcqVKwkEAqOcM6WUUio3uXY0vBuY1ce+GfZ+BSSTSYwx+Hw+EolEdw2CUkopNdblGhT012egCIjnIS/jWmrJZK/XC1jrX+vCSEoppcaT/kYfLMKapCjlfSJyQkYyH3A51kJExzSv10symaSrqwuABQsWsGTJEu1ToJRSatzob/TB+4Gb7J8N8LU+0jUBH89npsaTB17Zw4Or9xAMBjnQXkwyuZASR4xnNrkp3LGdK06LcOXpR7LGk1JKKTWy+gsKfgzcg9V0sAP4ENbqg+kiQMAY099wxaPaladP5crTp7J582ZuenwrbW1tnFuwl0vedwnz588f7ewppZRSOetvSGIb1jLEiMgM4IAxJjpSGRtvIpEIIoLH46GwsJBIJOfVpJVSSqkxIdcFkXZrQNC/VIfChIGkq6D7dSAQYN26dTo0USml1JiX84JIInId8BlgLtZKiT0YY5x5zNe44/f7iTs9RAp8VJ+0EL/fTyAQYOXKlSQSCZ3lUCml1JiX8+RFwE+BV7GWTL4buB9oB94Gvj1cGRwv3jzQzpvNhvrOBLe/UM+bB9qpr68nkUhQWFiocxYopZQa83Kdp+DzwH9i1RQA3G6MuQaYCYSwRiAc0zbubyNpwIEhHIuzbk8LtbW1OJ1OgsGgzlmglFJqzMu1+aAOeBZI2g8PgDGmRUS+C3wX+Nmw5HCcqC2II8YQSSRxJ+OUOqL4/dNYsWIF9fX1unKiUkqpMS/XoCAEOIwxRkQOYtUQvGzv6wSO+a/ARbFWZpfECXREqXGFiXc2A1ZfAw0GlFJKjQe5BgUbgdnA34HngP8QkZ1Y0xvfDGwZltyNI6FQCJ/TUGgi+JJB6ltDo50lpZRSalByDQruxKodAPgGVnDwvP26A/hAfrM1/hw6dAgAEcEtSfYcaieZNDgc/S0boZRSSo0duc5T8LAx5j/tn98CFgAXAB8EZhtjVg3moiJyvYjsFJGwiKwRkXNzPO4cEYmLyKYs+y4Rkc0iErGfPziYPA1VXV0dAMYYHAjVNRPoiBzz60QppZQaR3KepyCdMaYLq7Zg0ETkMuA24Hqs2obrgb+IyHxjzJ5+jqsA7gWeAiZn7DsTeBhrrYbfYU3J/IiInG2MeeVI8pmr1NoHzV1RDrV7SJgKdscqeemVJu5a83eqi71UFnm44rSpugaCUkqpMa2/VRIH9QnW3wd6hi8C9xhj7rJf3yAiF2INd/xqP8f9Cvg11loMl2bs+zzwtDHmu/br74rIcnv7FTnm64ik1j4AWLVqFYGiGdz17E4qizz8+4XzWDK9cjgvr5RSSuVNfzUFu7BWR8zVgDMaiogHOAX4QcauJ4Gz+jnuesAP3ILVpyHTmViTK6V7AviXgfKUT+FwmOffeJ3Gdhf+0gL2t4ZYMpIZUEoppYagv6DgYwwuKMhFNVbwkLkQQAB4V7YDRORErGaBM4wxCZGsHfcm9nHOiX2c8zrgOrCGDK5atSrH7PctHA4TDAaJRZN0RYWmpibWbOjA17QNj1M7G+aqs7MzL7+PY52W49BpGeaHluPQjWQZ9rdK4j0jkoN+iIgXq6/AvxljdubrvMaYO7FGVLBkyRKzbNmyIZ/zj3/8I5FIhP3NIeKmmnA8yZw5c1g038+E0oIhn/9YsWrVKvLx+zjWaTkOnZZhfmg5Dt1IlmGu0xznSyOQwGoKSOcHDmZJPwk4HrjbHnUQB74JLLBfv9tOd3AQ5xwWdXV1/N++AjbG/BgE4/LgEGjs1CWUlVJKjQ85jT4Qkf83QBJjjPn4QOcxxkRFZA1wPvBI2q7zgUezHLIfODFj2/V2+g9i9XsAeMne9v2Mc744UJ7yZf78+cx+ZRcvNRnAQTjhoNDt4kBbmPm1ZSOVDaWUUuqI5Tok8R307l9QCZQArfYjVz8C7hOR1cALwKexpkm+A0BE7gUwxlxtjIkBPeYkEJEGIGKMSd9+G/CsiNwI/AErYFgOnDOIfA3ZookF3LfVmsmwK5LA5RQC7WGdxEgppdS4kFNQYIyZnm27iJyH9WF+Va4XNMY8LCJVwNexmgc2ARcZY3bbSQY9mN8Y86KIXI41OuHbWMs5XzbccxRkqvI5KPe5aA3FSRhDoCOM1+WgPRyjvNAzkllRSimlBm1IfQqMMc8C/0Pv4YADHXe7MWa6McZrjDnFPk9q3zJjzLJ+jr3ZGHNClu2/NcbMM8Z4jDHHG2N+N5g85cvM6iIA4knD7qYgxkBbKDYaWVFKKaUGJR8dDXcAi/NwnqPCzBo7KEgY1u9pxed2sq8lOMq5UkoppQZ2RNMcp4iIC7gW2JeX3BwFygu9gNUBY/OBNg51RognDYmkwan9CpRSSo1huY4++EeWzR5gDlCF1VlQAemf+8kkvFHfzhkzKmkJRqku9o5expRSSqkB5Np84MBacyD90YG1+NA709YxOOadMq28++ckgAGHwxqFoJRSSo1luY4+WDbM+ThqzJpQQmWRm+auGE6BQEeYEq+bHYe6WKDzFSillBrDRnpGw2OCv8RqJkgYqG8NIw5oDUbpisRHOWdKKaVU33IOCkSkTkR+LSLbRKTLfr5HRGYPZwbHo0KPC5fduSAUi7O/NQQiHOrQJgSllFJjV05BgYgsA14H3gu8DNxuP78P2CgiS4cpf+NSLBbDZzfMhKIJdh7qotDtZE9TaHQzppRSSvUj1yGJPwTWARcYYzpTG0WkBHjS3r8k/9kbf5oam2hsbMSTFKCAUCzJxv1tLJ9bw762IPFEEpdTW22UUkqNPbl+Os0H/is9IAAwxnQA/wUsyHfGxquGhgYMhiI3OKzxBzR1RmgJxkgkDM1d0VHOoVJKKZVdrkHBPqx5CbLxYK1mqAC/348gkExQ6EgA0BGKsmFXAy6HgwNt2oSglFJqbMo1KPgv4FsiUpu+UUQmAzcBt+Y7Y+PV9Ml+qqqrqayo4KTJpYDV2fDZTTtJRrrY2ahTHiullBqbcu1TsBQoBXaIyMtAAPADZ9g/L7M7IwIYY8w1ec7nuFFV7KWwwIPbUcDxZTFe3BskiZO2uJvGpmaSHh/t4RilBe7RzqpSSinVQ65BwTlAHDgATLMf2K8Bzk1La/KTtfHJ6RA8JGls7WLSceV4aCGKk5gROsRHNUJjR0SDAqWUUmNOrjMazhjujIx3D7yyhztfDFGw9mmamlsIJYXbnu0kiROAqHGwtSHIgqkTeLuhk5k1xaOcY6WUUqqnIa2SqA678vSp1IZ2UFZWxqpVr7MjWUUk2Elr0sPqkJ8kwrZAJx6Xg4PtYTojcbpam6ivr6e2tha/39/v+QOBQM5plVJKqSORc1AgIoXAx7D6F1QCzcDTwN3GGO1Sb6utrcXlclIc6iDo8FLtSuIKJYnjJJoU9raEKPG6WL99L+uefpxEIoHT6WTFihV9ftgHAgFWrlyZU1qllFLqSOU6o+FEYC3wE6xJigrt558Ba0VEP6Fsfr+fFStW8J7zTufkU0/lHUvPY2q5tRZCOJZk64F2ynxu1rxVTzyeoLCwkEQiQX19fZ/nrK+vJ5HILa1SSil1pHIdkvjfQAVwrjFmhjHmTLufwTlAOdaQRZWmxOugrMCFt7CEixZZ/TJD8SSb6tvwuhy4C0uJiptgMIjT6aS2trbPc9XW1uJ0OnNKq5RSSh2pXJsP3gN8xRjzQvpGY8yLIvJ14Ht5z9k4lV7V35woYNJJZzO/thqvy0EknqQ1GONAW5iKsjLqll5INe0D9hNI1T5onwKllFLDKdeagmKgrzrrffZ+hVXVH41aUxn7kiFaWlpxO4WplT4AuqIJtgU6qCh00xRzc+JJC3P6kPf7/SxevFgDAqWUUsMm16BgK/DRPvZ9BNgymIuKyPUislNEwiKyRkTO7SftUhF5UUSaRCQkIltE5N8y0lwrIibLo2Aw+coHr9dLMBikvb2deLiTmpICuqIJzq2rAaxVE9ftbcHpFGKJJIGOyEhnUSmllMoq1+aDHwD32h0KH8CatGgicDnwLvoOGHoRkcuA24Drgeft57+IyHxjzJ4sh3RidXDcCASBs4FfiEjQGHN7WrogMCv9QGNMONd85UskEqGwsBCn00kikWBioaEtEmfx1HLcLwmxpKGxI0J9a5hir4u3Ap1MLveNdDaVUkqpXnKqKTDG3A98GjgB+CXwJ+BXwEnAp40xDwziml8E7jHG3GWMedMYcwNWkPGZPq69xhjzkDHmDWPMTjsvT9BzFkU7qTmY/hhEnvKmtrYWh8NBNBrF4XBw0qwpYKDA5WRSuVVx0RVJsmFfKyUFLva2BAlFE6ORVaWUUqqHXJsPMMbcCdRiLZN8rv082RhzV67nEBEPcArwZMauJ4GzcjzHYjvtMxm7fCKyW0T2icjjdrpRISLdz13trQSb9rO/oZmzZ1UDEIzGeX1vK0ljMMawv1UXSVJKKTX6xJi+lyoQkWuBzwOzgVbgYeCrxpjoEV3MWmVxP7DUGPNs2vZvAlcZY+b2c+w+oAaryeNbxphvp+07E5gDvA6UAP8KXAQsNMZsz3Ku64DrAPx+/ykPPfTQkdxOL52dnTidTjo6OnA4HCQSVg1AU8TB9g4nVaVF3L05TsJAuQfOn+6mqsAKIE6f5OoOJo5lnZ2dFBdrv9Wh0nIcOi3D/NByHLrhKMPly5evMcYsydzeZ58CEbkS+H/AW1jNBTOBL2AtePRvfR03jM7FGuVwBvBfIrLTGHMfgDHmJeClVEIReRFYD9wAfC7zRHatx50AS5YsMcuWLctLBletWsXxxx/fPSQxHo8jIhQUldDW6mLe1IlMqYiwuzlIDCcNiSLqSh3E3EUsOGUOE0pHvF/koIzEVMurVq0iX7+PY5mW49BpGeaHluPQjWQZ9tfR8F+B3wMrjDEJABG5CbhRRL6S2jZIjUACa9nldH6g3z4Axpid9o8b7Q6PNwP39ZE2ISKvAXVHkMchSZ9TwOv18tRTTxELBynzFOMpLuO8OUnue3kPXdEEr+9qoLCxCSNOasu8fPCMPitKRp1OtayUUke//voUzAHuyvjwvx3wAlOP5GJ2s8Ma4PyMXecDLw7iVA47H1mJVQ9/EoeXdh5RqTkF5s+fz4oVK1i6dClXv//duAqKOHlqBV6XVeyRpIN2KcJt4ry5p4G2YKzPcwYCAdatW0cgEBip2+hBp1pWSqmjX381BWVYix6lS72uAHZyZH4E3Cciq4EXsEY11AJ3AIjIvQDGmKvt1zfY19pqH38eVvNF93BEuwbjZWA7UIrVZHASfYxoGEl+vx+/3084lmBzyz6KvC6mVvrY3tBFzAi7u5zUlDiorqxge0MHS6ZX9jpHIBDggQceIBaL4Xa7ufLKK4/oW/pQqv91qmWLrlaplDqaDTRPgUNE0msTnH1sxxiTzOWCxpiHRaQK+DowCdgEXGSM2W0nyayFcGKtrTAdiANvAzdiBxG2cqw+AhOBNmAdcJ4xZnUueRoJBW4nU8oLeez1elq6rBqBOE72x4qIx8pZu6aBe14N8Kmls7jmrOk9jn3zzTfp7OwErHkQ3nzzzUF/IA21+l+nWtYmFKXU0W+goOCFPra/kvHa5HCuw4mtSYdu72PfsozXPwZ+PMD5voDVCXJMm1lTxKKp5SybV8O//3YDwWgCt9PBe06czNmzqwm0hzl5Wnmv49KHOBpjjmiUQnr1fzAYpL6+ftAfaKlaj2NVPspQKaXGsv4+yL81Yrk4Rkwo9eIQ8DgczJ1Ywro9rcQTSV7b3cxZs6ooL3SzaX87dRNKcDkPV8TMmzePdevWEY1G8Xg8zJs3b9DX1ur/odMyVEod7foMCowxGhTkmdfl5LjKQgJtYd4xdwLr9rSSBBq7ouxs6mJmdTHNXVHqW0NMrSrqPs7v93PFFVcMqepeq/+HTstQKXW0y7nKX+XHzJpidjd2UecvxuWAeBI6Q1FefruJmdXFlBa42bCvjSkVhTgch5sJ8lF1f6xX/+eDlqFS6mimQcEweOCVPTy42lrbaX+LNYXx5IpCAIwxzJ5QwplTCvASI46bjkicrQfbaA5GqSz0UN8W4kB7WBdKUkopNaI0KMijeDzOunXrWFTi5fhziqitreXHL1jzCvznh07qTvfyjiaeX/cmJY4YXUkX4KArHGft7hbedbyf0gI3a3e3MKm0oEdtgVJKKTWcNCjIk0AgQHNzM6+//jrBYJDCwkI8Hg/BitMoLOz5jX9GdRFrS8rwyH5cJInjJJwwrNndzHlzaij2ujjQFmJ/a4jjKgvzmkdtD1dKKdWXnFdJVP1LzfDndDoxxuB0OkkkEgSDvVdArC724nEKBqHIEQUM4bihK5pgy4F2AMp8btbuaSGZ7HvBqsFIjbF/5plnWLly5ajNjKiUUmrs0qAgT1LD0xKJBCLSPcGN0+mksbGpx4ew0yEUJ7sIJx04HC5cWPM+BSNxnn+rEYOh0OOiLRhjX0t+llXWaYqVUkoNJOfmAxGZDHwJa5rhSuCfjDGbROTzwEvGmMwJjY4pfr+fyspK6urq8Hq9RCIRvF4vT/72dYwxrFy5qccMeCUVlTQnrEAhVRfQForT2BlhX0uI4yoKKS90s3ZPK5MrCnEOsW+BjrFXSik1kJyCAhFZADyHtcLhS8BiwGPvngacBlw5HBkcT1wuF4sXL+5+vW7dOowxuFyu7m/nqaCgw3hxOJw4BVxOB+G4IZ40BCMJVu9o5rhTCin0WH0L9jR1MaNmaGtp6xh7pZRSA8m1+eCHwJvADOBDQPrX1heBM/Kcr6NCbW0tYePkUFhoTvp6fDtfOKUcl9NBAsHndVHqcwPQEYnz5sF2WoJRACoKPazba818OFSp1Rs1IFBKKZVNrkHBOcD3jDGdHK7tTglgLUSkMjQnfRxIltKULGJVdBbNycOjEI6fVMqZs6qYXF7A9ctms3xuDQ4gEk8SiSd48a0mwFpIqSsSZ3tDxyjdhVJKqWNFrkFBf19Tq4FQHvJy1Nm4v41E0uCUJJFYnI3723rsn1DiZVK5j+oiL0umVVJcYLXmBKMJ1u9rpT1sraZYXexl3Z5WgtH4iN+D6ikQCLBu3TodvaGUOirlGhSsBv65j30r6Hs1xWNaUaSJRDxGJJ4kFOyiKNLUY38oFIZwF/sONVFT4mXWBKvfQFsoTiyR5OUdVnq304EIbNrfPuL3oA7TYZ1KqaNdrkHBd4D3iciTwEexmhDeJSK/Bj4IfHeY8jeuxRt3U+top9oZ4h3eHcQbd3fvCwQC7Nixg9amBl57bQ0tLS0sm1tDocf6lYSicV7b1UJXxKodqCrysuVAO81d0VG5F6XDOpVSR7+cggJjzDPAB7A6Gv4/rI6G3wPOBT5wrA9HzJSqYq6pqaHAkaSUEFXOMHV1dd1p6uvrSSaTOAUKTJh9DU3UTSihusgLQHNXjHgyySu7mgHYv28fW9/YwB+es4Y4qpGnwzqVUke7nOcpMMb8CfiTiMwGJgBNxpitw5azcSpVxZyavKh28jzCoTCXXHga8+fP707n9XqJx+MYYyiijZhx4nE6OHN2NQ3r9hOOJwlFE7z8dhPHeUKsXf0yxhh27N3PjAoPy09fOIp3eWzSYZ1KqaNdTjUFIvJNEakFMMa8ZYx5MRUQiMgkEfnmcGZyPKmvrycatar4o9EoLqeL6dOn9QgIACKRCE6nA5fLSUWBgIkTTyZZfFw5ZfbwxOauKPFkkpe2HeyeOtlr4jy1cTexPAxRVIOnwzqVUkezXPsU3ARM6WNfrb1fYdUABINB2tvbu6uZ+0qXSCSJxxPEoxFmVvloDcaoLvayoLYUj1NIGIjEE+wKe4njIJFI4HYYyqpq2FzflvW8anjp6AOl1NEs1+aD/ubYrQAiecjLUSESiVBYWNi9IFIikegzncvlQkQoLCyk2mvYm7S+/Z9TV83G+jYOdUQJtEdxO4V6MxMnhsoiN1u3hfj9lrX889kz+OR5M0fy9o5pmU1D6dNWK6XU0aDPoEBElgHvSNv0KRF5b0YyH3Ax8EbeczZO1dbWsiVWxestJYgIFQIHI+2876fPd6e54rSpvHN6LQ7HFowxeDwe5s2YQvvBJMFonOMqC5lRVURLV4x40lBTUkB7KMqC2jI+ea4VBLSFYhR6nNY8CENcF0HlJn30QTAY7DFttVJKHQ36qylYCnzd/tmQfZ6CKLAZ+NxgLioi1wNfBiZhBRSfN8Y810fapcB/AnOBQmA38EtjzA8y0l2CNXRyFvA28DVjzO8Hk6988Pv93PLP7+nujAb06Jj2wCt7eHD1Hh5cDSFfNZFwhD+Ga3jykbcIx+LMn1TG+xbWsnTuBN4+1EVTV5TGzghuh1DfeniOqDKfm/rWEG83dDJnYslI3+YxSUcfKKWOdn0GBcaYbwHfAhCRJHCGMWb1UC8oIpcBtwHXA8/bz38RkfnGmD1ZDukEfgJsBILA2cAvRCRojLndPueZwMNYfRt+h7U+wyMicvZoDJf0+/34/f6s1c1Xnj6VK0+fenif+/C+sspqfrtmH0ljmF1TxKSyAtpCUaLxJC63g5auKAfaQkwqs6ZLrinx8truZiaVF1BS4B7p2zzm6OgDpdTRLtd5Chz5CAhsXwTuMcbcZYx50xhzA3AA+Ewf115jjHnIGPOGMWanMeZ+4AmsORJSPg88bYz5rn3O7wKr7O2jpr/JbrLtK3A7mVZVSFsohtPhYOmcGsp91mKUoZi1JsLDq3dh7OUn3E4HLofw2q4WnbtghOjoA6XU0SzX0QfdRGSCiEzNfOR4rAc4BXgyY9eTwFk5nmOxnfaZtM1nZjnnE7mec7j0V93c1766CSWEY1bnxAWTy6go9uByWO03saRh84EO/r5xb/d5Kgo97GkOsrOxa0TvTSml1NFHcvmGKSIO4BbgU0B5tjTGmOxj73qepxbYDyw1xjybtv2bwFXGmLn9HLsPqMFq8viWMebbafuiwCeMMfembbsauMsY481yruuA6wD8fv8pDz300EBZz0lnZyfFxcU9tsXjcWKxGG63G5fLNeC+pDG8VB/H5QCPU9jcmOD5/XFCCQCDA6gsEK6Y58FhdzCMJw1dUThtkotC9/judJitDNXgaTkOnZZhfmg5Dt1wlOHy5cvXGGOWZG7PdUji54HPAv+FFRx8F2vlxKvs5+/lJ5v9OhcoBs4A/ktEdhpj7juSExlj7gTuBFiyZIlZtmxZXjK4atUqBnOuQCCQtX36uEAHL+9oYlKZj1mxOJse30yoIwIIBoPD5SZcMpnTZ1R1H9MSjOIocHHu/InjejTCYMtQZaflOHRahvmh5Th0I1mGuTYf/DPwbaygAOD3xpibgOOxvvnn1HwANAIJILNB1g8c7O9Auz/BRmPMXcCPgJvTdh88knOOpv5W3DuushCnQ4gnkxS6XZw1owynvXq1AYo8Dp56s4G20OHFkSoKPRzqiLC5XldSVEopdWRyDQpmAq8ZYxJAHGt+AowxMeDHwMdyOYkxJgqsAc7P2HU+8GKOeQEr3+nNAi/l4Zwjqr9OiAVuJ/MmltJir4g4vSiGU8BBEhAaO6MkjeHPmw52dzoEazTCur0tHOrIPpeUzsanlFKqP7kGBW1Agf1zPdacASkuoHIQ1/wRcK2IfEJEjheR27CmSr4DQETuFZH0vgE3iMh7RaTOfnwc+Dfg/rRz3ga8Q0RuFJF5IvJVYDlWwDImDTTmfba/mEQSjDFMqqmm1BHHgQEMHVGDyyFsD3SwLdDZfYzL4aDU6+aFtxqJxHvOpNhfzYRSSikFufcpWAfMx+rR/wTwLREJYdUafBdYm+sFjTEPi0gV1sRIk4BNwEXGmN12ksymCCdWs8V0+3pvAzdiBxH2OV8Ukcux+jt8205z2Vhe0nmgMe+lBW6mVPpo7IhQXl7OjNpq3jzYidPloC2UYH9riOMqC3l8Qz3Tls+mwGX18ywucNHQHubVXS2cPasKEat/gc7Gp5RSaiC5BgU/xmpCAGuCoJOB39ivdwP/MpiL2pMO3d7HvmUZr39MDt/4jTG/BX47mHyMhM2bN7N9+3bq6up6rZSYmuSoL1sOdnDvi7twO60KnQKvm7ZgDLDmLQi0hyn3eXhmawMXLJjUfVxNiZe3GzqoLvIwb1IpQHdNRGtrKx6PR2fjU0op1UtOQYEx5m9pPx8UkdOwphMuBN60+xaoDJs3b+bRRx/FGMOGDRsAegUG/fnUeTOZUu4jaQyFHheJZJL/ffptGjsj1LeF6QjHmVjqY/XOFhZMLmNKeSEAIoK/pIDVu5qpLPYwocRq+UkNP9WJjpRSSmWTU58CEbnarvIHwFjeMsZsAErsOQFUhu3bt2OMweVyYYxh+/btPfYP1PFPRDhxShltoTgAToeD8xf4cTsdlHidJA0cbA/hcQmPrtlHOK0fgcvpoNzn5pmthwhG49TX15NMJvF4PCSTyR4dG5VSSinIvaPh3Vg1A9nMsPerDHV1dYgI8XgcEaGurq57X64d/2rLffjcDhoam9mxYwf+ggQTywqoKPIgQFsoTixp6AjHeTJjNEKhx0XSGF7Y3ojL7aGrq4v29na6urrwenvN6aSUUuoYl2ufgv5mwynC6gCoMqSaCrL1Kci145/b6WByYYJf/u0ViiSGw+HgvIWnsfL1MBNKPAQ6ouxvCTFnQjHr97VS5y/heLsfAUBVkZeDbWF2HjyIMWD3O+TQoUPDe/NKKaXGnT6DAhFZhNWhMOV9InJCRjIfcDmwHZXV/Pnzs/YjGMwyvK5QMyRixJ2CIx7HHetkyfQKXtvVgtcVJxJPcqA9zITiAv7v9XomV/goTVs1cUKpl2e2JukyPqocYUD7FSillOqtv5qC92ONNABrIr2v9ZGuCfh4PjN1LBjMMrxFvgLK4y3UR4opdsRxu90snzeBTfvbmFRawK7mIM1dse5A4A/r9vPRM6YhIjyz7RDPbjtELG5oCM/FgaHcFeeFtYYfbnyeK06zlnJWSiml+gsKfgzcg9V0sAP4ENZ8BekiQMDo184jMtCQxJRIJMLEYicdUS8O4yIWi1HgcnLRiZP43dp9TCz1crA9wt6WIHMmlLCrqYvn3mrkvLoals6xHgB3/GMLnaEIP7pkPvNnThl0fvtaq0EppdTRoc+gwBjThjWTISIyA6jXoYejo7a2lgKvh0kmzL54EZWV1gSS82tLWbO7iL0tQYo8TrqiCfa2hJhW6WPV1gZqywqYPaGk+zxOh+BywAs7W5lS6+/RxDCQVMfIRCKB0+lkxYoVGhgopdRRJqfRB8aY3amAQES8InK9iPxMRP7DXg5ZDaNUU8P73nEm555xKt5C64NeEC46cRLGwOQKHw6BzkicllAMn9vJb9fso7HLWgehtbWVgwcDtLe1subVV/ndS9voiuTeP7S/tRqUUkodHfoMCkTk2yLyRsY2L/AK8FPgeqxphdfZNQlqGPn9fpaccjLLT5pOS9rqiNXFXpbOqSEcSzClwgdAfWuYpIGkgYdW7yUcT9Dc3IzB4HQ68RCnsaWVv78ZIBjNLTCora3FGENrayvGGJ0RUSmljkL91RS8C/hzxrbPAicB3wfKgDOAGNY6BmoETKkopMznpjPtW/5Zs6qYUlGI0+GgstBqEtjV2EWB20FLV5Q/rqunvLICQUgkEjgcDqZPrCYcS/CPLQ2EY4m+LtdDMpkkHo+TTCaH5d4CgQChUEgXa1JKqVHSX1AwC1idse0DwAHgq8aYDmPMaqwA4Z3Dkz2VyekQTp9RRVso2j2s0Olw8KGTJ+MUa90Dn9tBLGnY3RSkuMDJ1kA76w9G8U/0U1lRwTnnnEN5eTlVRV46I3Ge3tLQa1XFTFu2bCEYDJJMJgkGg2zZsiWv95Xqs9DR0aGrOCql1CjpLygoA7r/M4uIBzgNeDpjtMHrWKsdqmGUPiXyxLICZlYX09R1uBmh3OfhfQtrCcUSTK0qwuUQuqIJDrZFKClw8fxbjTR0xQnipsN4uo+rLvLSGorx9AA1BpnrJuR7wEmqz4LD4dA+C0opZRvpGtT+goL9WMsVp5wOeIAXM9K5ga78ZkulyzYl8uJpFSSNIZY4XJU/v7aUEyaXEYklmFZlLY7U1BWlLRjH5XCwuynEjkNd3LHqbfa2BLuPqyn20hqM9tvH4Pjjj6e4uBiv10txcTHHH398Xu8xNZlTMpkccDInpZQ6FoxGDWp/QcFzwOdFpFxEBPgckAT+lJFuEbBveLKnIHvP/2Kvi8VTy2nsjHSnS41GKPa6cABTyq2Oh/taQ7SHYxggkYRgNMbupmCPa1QXFxCMxPnb5gAd4d4jT/1+PxdccAFz587lggsuyPtwxNQIi5KSEh3uqJRSjE4Nan9BwbewagoCQCtwCXCnMWZ3RrrLgeeHI3PKkvrW3Nra2uN13YQSSjM6HRa4nFx26nEkDJQUuKgqspoKmjqjgMFg6IrEMbFwr+tUFnmJxpM8+UaAtmDPwCAQCPDUU0+xc+dOnnrqqWGJWP1+Pz6fTwMCNeq006saC0ajBrXPoMAYsxOrFuC/gHuBa4wx16enEZGJWM0JukriMMvWlu9yOnp1OgSYVObj/Ytr6YrGmVjqpczn6l470UmSIqI8uTnA/tZQr+tUFFpBxF/eOEBD++HAQecpUMcK7fSqxorRqEHtd5VEY8we4Jv97D8I3JDvTB1N8jE1cH19PSJCeXl5rxUVJ5YVUDehhN3NQWqKDy+HvGBSGYG6CM9vP8RxFT6i0Q5CCSGBA7ckEeDel3bxkTOmcVxFYY/rlfncBKNxnngjQFNXhKfebCAWi7G7cSbGGMpcCf76dCvu53XtBHX0yVZlq7VXarSMdA1qrksnqyOQr6mBB1pR8eRpFRxoC9MViVPkPfwrXTanhkPtEbYFOpha5WNbQxcGB22mgCpfAeIQ7ntpN1edMZVplUU9zlnoceF2Okgaw7ffv4CFU8r5wgOrCQaD3PL++fpPUh21tNOrOpblNM2xOjL5qnJPVSEtXbo0a2BR4HZybl01beEYieThZgSHCB9YXEtVsYdY0kFJgRsHkMDB7tYITofgdgq/eXkPOxp7DyBxOx1MKi1g4742ntt+CG9BAdXVVRoQqKOa3+/nne98J16vl3e+8536flfHFA0KhtFA3/AHw+/3s3jx4j7/QU0oLWDhlDIaOnp2IPS6nFx1+jQKvU6MgVKfC6/LQTSe5K2GTsAKDB54ZTfr97T0Oq/DIUwqK6C+LcRbB1qpDzRqG6s6qqU61UYikWHrVKvUWKVBwTAa6Bt+vi2oLaPannMgXZnPzdVnTsfpEBIGZtcUWbMeJgxvH+rEAD6Pk//bUM8/tjb0mphIRHDHwzQ0NLCtvonb7/89Bw8eHNZ7UWq06ERa6lg2KkGBvcriThEJi8gaETm3n7QfEpEnReSQiHSIyCsi8k8Zaa4VEZPlUTD8d9O/gb7h55PL6eCs2dVE4kmi8Z7rE1QWeqjzl+AQiMaTzKwppsjjJJ40vNXQSTSetGY+3H6IR9fuI5roeXxzczMJA1Hj5o2uQv68bmfOiykpNZ5onwJ1LBvxoEBELgNuA24FFmMNafyLiPTVhX0p8A/gYjv9n4HfZwkkgljTLXc/jDG9B+OPsPTpiUdCmc/N2bOrOdQZJpE0PLPtEN95fDPfeXwzDe0RijwudreE2N7QSSJpKHA5SBp4+1AXbaE4ZT43mw+0c+9Lu2hLW40x7CriUNxLa9zB5lAp+8NeHnv9AHuauvI+5bEaGh1jPzQ6kZYaS0b677nP0Qci8o9BnMcYY3JdFOmLwD3GmLvs1zeIyIXAZ4CvZjnxv2Zs+paIXIy1ONNzGXkYU3Xa+Rp9MFjTq4toC1Xw+t5WzqurZumcmh7761tD3P/KbhJJQ6HHSX1rmKauKPtaQoSjCSaWeQm0R7jjmR1ccspkZteU0Bx14nC6cAp43U4STi9FHidPb21gZk0xS6ZV4vM4h/3eVP9S7zm/38/KlSv1Q+0I6URaaiwYjb/n/oYkOoD0r4BzgYnALqxZDv1YMx4eALbmcjF7UaVTgB9k7HoSOCuXc9hKgMxecT4R2Q04gfXAN4wx6wZxzrxLH32QOb/AcDtxchmdkTi7Grvwl/ZsRakt9/Gxs2dw38u76IwkmFzuo8DtZH9riMauKOF4gmmVhcSThgde2cM5s6uZNaEYp0NIGvC4nEyrKqTA7aS2zMf+liD1rSGWTKtkRnURDoccUZ43b95MW1sbmzdvZv78+fkohrx44JU9PLh6DwD77TUjJqfN7TCW5mrQMfZKHT1G4+9Zcqn6FZEPYFX5rzDGvJK2/XTgYeBfjTF/zOE8tVgLLS01xjybtv2bwFXGmLk5nOOzwPeAE1JTLovImcAcrBUbS4B/BS4CFhpjtmc5x3XAdQB+v/+Uhx56aKDL5qSzs5Pi4uLu1/F4nObm5u7XlZWVuFwjNzVEPGlY35AgGDOUeHt/UAdjhqf3xmmPJCl0CZEkBLoMScApMKFQcDsgFIdqnxCMGeJJWDbVTbWv5/liSUNHBEq9wpwKJ2VZrtefcDhMS0sLXq+XSCRCRUUFBQWj3iWkl7s3WWtN/PMJ3gFSjo7Ue87tdhOLxUb8PXc0yfx7VkdGy/HIDeff8/Lly9cYY5Zkbs/17N/B+ub9SvpGY8wrInIzcAswYFAwVCJyCfB94LL0NRiMMS8BL6WlexGrtuAGrIWcejDG3AncCbBkyRKzbNmyvORv1apVZJ4rHzMaDuU8Z0Xj/HXTQTBQ6nP32n/SCXEefnUve5uDVBe4KSsx7G7qIhRLcqDLMKHEi7/CQ1c0SWMkRJXPwcLZx1FZUZH1eh3hGK3hOBP8xZwwuYySgt7XzOaPf/wj27dvZ86cOWzfvp2FCxdy4YUX5nyfI+WJ5g0ALFt20ijnpG+BQIC1a9dy1llnaS3BEGT7e1aDp+U4NCP995xrR8M64FAf+xqA2TmepxFIYDU9pPMD/fYHEJFLgfuAq40xj/WX1hiTAF7Dyveoysfog2xLJ+eq0OPinfP8JIyhPdR79UOf28VVZ0xj8dQK2sIxRGD2hGJqSqw1EBo6Irx9qAsnSeLRKAfaIvzsiY3sOtCY9XolBW4mlRWwuynIH9fVs2Z3c06jFOrqrF9VquYq9XqsCQZDNDY2jelOfNoertTRY6T/nnMNCnYCn+pj36ew+hkMyBgTBdYA52fsOh9rFEJWIrICKyC41hjz24GuYy/1fBJWf4dxb6gzI5YVunn3/Il9BgZuh4OLT5rEPy2cTDiWJBRNMKnUx8zqIlwOIRRL8lZjiASCS6A97uRXL+3l2e2Heg1dBGsmxepiLzUlXrYFOvj92v28vrel3+CgqqqKoqIiRISioiKqqqoGdY8jIRAIsGPHDg4cOKAL5Siljkq5BgXfAt4nIptE5GYR+Yz9vAlrqODNg7jmj4BrReQTInK8iNwG1AJ3AIjIvSJybyqxiFwO/Aa4EXhWRCbaj8q0NDeJyAUiMlNEFgG/wgoK7hhEvsasfMyMmAoMksbQliUwEIRFx5XzsbOnU+B20h6OUeR1MtdfTLnPamWK4yRsHDgcUFLg4Zmth/jff2xny8F2DL37pjgdQk1xAVXFHt6ob+d3a/fzys6mXpMrAd2BjsPh6PF6LKmvr8cYg8vlGtOT2uiQxKHTMlTHqpyCAmPMQ8AFQBvWsMH/tZ9bgQuMMQ/nekE77eeBr2O1+58DXJTWR2Cq/Uj5NFbfhx9jffNPPX6XlqYcq4/Am1gjGSYD5xljVuear7EsXzMjlhW6OX/BRIwxWT+YwVp2+bqlM5k3sYS2UIzGziiReBKP0+o4aBCa417ebgpxsD1MY2eER17by69f2p11KWYAl8PBhJICJhR72dXYxWOv1/P0lgAH2kLdazV4vV66urpIJBJ0dXXh9Y69jny1tbWICPF4fMxOahMIBHjwwQdpb2/nwQcf1A+1I6BLJ6tjWc7dGI0xfwf+LiIOoBpoNMb0rjvO7Vy3A7f3sW9Zf6/7OOYLwBeOJC/jhd/vz0ubUpnPzbtPmMjTWxo41Bmmprh3D/9Ct4tLTpnCggMdPLahHp/Hic9paGpupTPpIJh0kTBCMp4EhGKvi/qWEP/v+Z3MmlDM8nk1TCr19TqvwyFUFXkxxtDcFePvmwMUuJ3Mm1jK3v09//E2NDSMqWGJYP0OZs6cSTAYZMX7x+ZCOVu2bKGjowO/309HRwdbtmwZk/kcy3RYpzqWDXpGQ2NM0hjTcKQBgRp9pQVuLlgwEX9pAQfaQiSzDEsVhOMnlXL9stnUTSihpStK3ECZK0mVM0qRWzBAoCPClkAH4XiC4gInOxs7+eWzO1n52l4OtIWyNiuICGU+N5PKfBR5XGzc38rLB+LsipfREHYQMw6sbiFjTwQn7cZLc7J30DMWGGO6O2um/6xyp9Mcq2NZzjUFIjITWIFVtZ/59dIYYz6ez4yp4VXgdrJ0zgTW7Wnhjfo2JpQU4Hb2jhGLvS5WnDqFukoXv31lB8G44HEkmVrpI5oU6ltDhGJJ6lvDBNoj1BR7qCzysL2hg22BDiaX+zinrppZNcU4snzQe1xW00J4Qjm7tzvY0emgIV7J1GAhkwMd1JQUUOpzjYkg4c0D7bzwVhPJpOH636zl9qtO5vhJpaOdrR4mTJjQ72s1sFRz3dq1a1m6dKnWEqhjSk5BgT150UqsmoUGIJKRRL+OjENOh7BkeiXlhW5e3tGEz+2iLMtcBoJw8qxJHFfh5YWtATY2xgnFodjrpG6C1fegoSNMKJbkYHuEho4I1cUeKgs9HGwL8/CreyktcHPGrEoWTCqjyNv7bRePxynxeajwOogXOAmGo7yysxkDFLgcTK8uYlK5j4pCN4We0ZmMZ+P+NpJJg9flIBZPsHF/25gLCiKRCD6fD4fDgc/nIxLJ/FNVudBhnepYNZjJi1ZhzTrY13wFapyaPaGEqiIvL7zVyIH2MP5ib9apit0OByf5vZwys4Z1ByNs2NcGQEmBizJfCR2ROA3tYbqiCRo6ojR0RCktcFFd7CUST/DkpgBPvhFg3sQSTplWybSqQp7b3siz2w4RT8RpaJ+EdAo+JrB+SxDX9h2cN6eGs2ZV8XZDJ1sOdmCMoajAxXEVPvwlPkp9LkoK3DiPcGrlwThxchkOhxCJJykr9HDi5LJhv+Zgeb1ewuEwyWSScDg8JjtsKqVylz4SZkRW280x3UzgSxoQHL0qijxccMJENu5rY+P+Nsp97h7f6FtbW3n++edJJpM4HA7OO+cczpldw3PbD7FxvxUcFHtdzKoppisap7EzQlsoTnvYenicDioK3ZQVutl6sIMtBzvwup0snFLGx86ZQSFhfvrX1/F6C5jnPMTS8+ZTXl7eff2q4sMfbpF4gp2Hgmw52IkAAlQWe/CXFFBZ5KHI66LY66LAnd++CcdPKuXs2VW0dEW5+Z9OGHO1BGDVFBQWFuJ0OiksLNSaAqXGsbG2IFK6LcDYm01G5ZXb6eDkaRVMrvDx4tuNHGwLU1Xswe100NzcTDKZ7F6boLm5mZkzy3n/osmcU1fDC28dsqvXodDrZFplEbFkkubOKE1dUaKJJIGOCIGOCEUeJ+WFHjwuw+odzaze2QyJKO1xN6VuwTiF5ubmHkFBOq/Lidd1eEXGpDGEYwm2BzqIJQ0iYAy4nVaHxrJCDxU+N8UFLgrcTgrcTrwuR9Y+FAPxkqBUIlQ6QsDYCwpqa2txOBwYY3A4HNpJTqlxbDRGwuQaFPw78GMRecUYs2M4M6RGn7+0gPeeVMv2QAfr97biEKGiogKHw0EkEsHhcFBZ2T13FFVFHv5p4WSWz/Ozbk8Lr+xooi0aw+MQ/KVeJpR66YjEae2K0haK0xVN0BW15jQo8jgp9bnxOpx0GRehkPBCpJqO+iRd3jYmV1j9CIS+v/E7RCj0uHr1NUgkDZF4gv3NQXbEkxiwAwYDCB6ngyKvk0KPiyKvkyKvdQ6XQ/DYQYPbKbgcDlxOoelQAzt27MAYw8qVm8bsssSp2pGx0DlTKXXkRmMkTK5Bwc1YNQVvish2oDljvzHGLM1nxtTocjsdzK8tY2pVEev3trLjUJITTzmDeLCNqqqqrN/iS7wuzqur4cxZVWw72MHLO5o50GZ9+PvcTkori0iYJG2hOG3BGB2RVICQAMAhToyBwsJi9rRF2b1+v3Wsx8nsmmJm1hQzqayAqiJPTh94TkcqWMi+P55MEk9YEzk1dhriCUPSJDGkPlQBQ3cwsWfPXpqjLpwuB292FvCHV99mzmzB5RQ7gLCCB7dDcDscOJ2CQ+yHw/qQdogVxIhYHTgdYm0X+xrSva/ndrDSp992+vbU6z379pMwgMNJ0sDeffuprjk8AqGvUhts/NBf+Q/HMMjhHlmZ7fRJY7on1xqWax4jw0UTSUM8y3ToR+rYKDVLZXUNH7rkUtatX8eKERoJk2tQkAC2DmdG1PA7kpUWi70uzpldzfETS9iwr429LUUYtxNjTJ8fDG6HgwW1ZSyoLaO5K8qG/a2s2dVCRySGMVbHxMpCDwmTpCMcpy0UoyMcJ2kgCexvjwJRCj1OijxOksawwe7rkPpgnVzuY0Z1ERPLCqgp8VLm6782IRuXw4HLYQ3PzIWnthrnhgOYRAKHw0lhSTmtwShJY/2DTxrrg8TYz5D6B2bVTKRyZzA9Psi7PxukO2mv/3y5/iPsaHezOVzOvgahI1xOQZObLav3ZDlD7mWVba6JoRrs7+rIDC3f2/bGqV+9O22L1rwciW374hx4de9oZ2PcOnDgAI2H4lRNbBg7QUEuswqqsS3VYSWRSOB0Ogdd9V1V7GX5vAk0dUbYsL+Nvc1Bu/Ogp9+e/5VFHpbNmcB5dTXsbw2xub6NDfvb6YjESCatGoRyn5tQLMFbDV09/o0HowmCdi0CWEMTC71Wf4KdTV3saurCabefOx3ChJICassLmFhaQKU9JLK4wJW3D6CKigomTfITiURYdvrCPvs8jKaJpQXUFJ/J22+/zdmLTxiTeRwvDhUIE7PMzKkGxyrH3jOnqoHt3buXbetXU1Rdy/899jhlBc5hn+l1dAZ8qxGXvtJiMBg84g4rVcVels+dQEtXlLcaOtne0EHSGMp9nn6/cTtEOK6ikOMqCjl//kT2t4bY3tDJ5vp2WoNROsLWCooCuBxCTakXj9NBZyROVyROKJYkHLcecHhBpwKXA5/HidfpYE8syN6WIE4RnA4haddmlPncVBd7mFBaQIXPTYnPTWmBixK74+Fgggav14vX6x3TH7bl5eV4vJ4xnUel1MBSi7AhgsGwffv2sREUiMh5A6Uxxjw79Oyo4ZKPlRYzmx9OnVHJiVPK2NMUZNOBNlrbo7gcDsp9blz99OxPDxDeMXcCbaEoq3c189s1+0gkrUpfY6zgwF/ixVnmI2kMoZhVcxCKxglGE0QTJi1QSD8/eF0Oe5SCEI4laOyIsPVgB06R7jkYksaAWE0kJV43FYV2wOBzU+h2Uuh14nM5eX1fK6t3Nnf3AwD4zuObu6933pwals6pGXR5KqVUf2pra9m1axcYq8mxrq5u2K+Za03BKgZuoMutYVaNitTUrYPtU5DSV/NDgdvJnIklzJ5QTHMwyu6mLrYHOoklk3idTkp9LlyO/of+lfk8nH/8RLYHOjnY1MqHTptJLGF4q6GTPc1BYtHDNQPWZEgeBCFhkoRjSUKxBOFYgnAsSTiWIGkgFEsSivXu3CRYUyt7nA7cLqtTYCSWoKUrxt5mK6DA7gyYPi1zqubB4xI8Lic+t9NaKMrtpC0U5W9vBij0HB7q6LFHLlidD3uOYnDZPzsFnAOUTa6e2XaIZ7dZ04g0dUaIJxL4d2ngotR4dtxxx3HWWWfx5q79XPzei0dkkbhcg4LlWbZVAe8FlgL/krccHUOOpOPfUAxlpcWBmh8cDqG62Et1sZeFU8pp6IiwpznIrsYu4gmrzb+0wIW3nyaGYq+LigIHi46rAODU6ZUYDC1dMerbQuxtDrKnOcihjggiYlWrGWtYY4XP3f0BG09awUI0niSSSFjPsSTRRJKkgUg8SSSe7D1ZdxqXQ3A7BafDgcshuByC0wGRWJxkIoHL6cJt34sIiBGSdtycCiYy13ow9gN7oaJU90OnwwoUnA77Z4cDhwPc4sDRvQ+c4rD3W+kc9s8OO7g4u64alwgv7Wwk2NnFuxf4u0c6OATW7mnpHtWQ6gdijXiwRzZAd0DUPRrC3oY9UiJVV9Jz3+EueIIc3pZWs9K9P7OlJvP4tAS9k/bcMuCIiczRGQMkz0zQGTU0py0xfiQ9U4Y6KvRo6NrYFTW0hbIv1a4GVlrtx+xvYk7dnBG5Xq4dDZ/pY9fvROR/gPcBf8lbro4BQ+34N9IG0/zgcjqoLfdRW+7j1OmVNHVF2N8SYmdTFy3tMYwx3XMDDFSLIAiVRdYiSyfUWtMKx5NJmrqiNLRHONgeZn9LiEB7iFg0gUOEpD2MzOdxUOJ04U59/QcSJkk0bojFk0STSWKJJLG4sZ4ThnjSChziSUM8abDGQ2TT+5+cQ+hunnA6rJoGp4MewxIdWPtTr8GQMIZk3PowdSBAovsDN5VzkxoeaXpW2aWCi9TGJIamzijxBDzxxsFeZQk9P8y796UuIqZ7Z/pgiPFmoLEW2ao9O0Ix2u2+LfFk0poA681N3ftLC1yUpq0NMvDYhqGO2hiPJd9bMBjjqYNvj3Y2xpUe78VEkiTw6i9eoqTAev9dcdpUrjx96rBcOx8dDf8EPARcn4dzHTPy1fFvpBxp80NqVMCEkgIWT62gIxyjqTPC3pYQ9S0hookkCPhczu4P84G4HA78JQX4Swq61x8wGDojCVq6IrQEYwTawxxsC9NkT5jkcFgfiUn7W7rL4aDE7er+1p3+DziJNWdBPJHsDg4SiSShSIxwNIpBSNpzAVg/p4Yj2v0UhnFsuyN9DoPUN/zubdb2iN2E0tgZxenI+GafSp8+90Hq5JKRLrVZ0p+l57a0cpOMH9IDm0yZR2ULVHqeS7Jt7lPWU0n/+8sL3ZQXWv90D7aFSCSS+MvcuJyurAcN/JE9Gh/qY69qwiXgcuZ+4qMjFBqaymIPlcXWBCsH28OEI3G+fMEcLj5p8rBfOx9BwVz6/jql+pCPjn8jbSjNDyklBW5KCtxMry4mmTR0hOM0d0XY3xYilkwSSVh/BE4RCu02+2yLM2UShBKvixKvi6mVPffFk0naw9aESe3hGC1dUQ51RmjuitIesiZRSn2bN0AyaTBYgYPbKRS4nDgdkCxw0NzcRdIYHCJUVlXhdh3+5phIJkmYw5PeJJLGChiSVqCRSFjnTb1OJg8HE9Y8B8b+2Q5eSM15cPheun/untyg7wAkfTinOjK7miP0286kctPVOdo5GPfePNDJxScN/3VyHX1wdZbNHuAE4OPA7/KZqWPBUDv+HQ0cDuFPGw/w4Oo9gPXBGE0Y7ntptz3bYJLFUytYMr0SjFXrUOB24HM7+x3dkMnlcFBZaM1bkE08maQrEqczkrCf47SHY91BREfYmnmxM5ognHSCPedhVySBI364Cj8VWDgdVp8Ej9PRPWOho8+vwrlLYvdFMIdHaKSundre0hWhKRizr2QNFS31ubtDh6TdsSE1qVLqPNgBCByON0x3qp4xSM9wJC0oMRlps6fqbu7oFc70OWFT2k+DqITpL2l/swkmk0miCbt/CElcTieOIXYIHYnJC8fqTH+pRdTUkYnZfaF87pEpw1xrCu7pY3sEeBj417zk5hiTj2/egzHSHRtzceXph9vGAoEAa9eu5eST53bnL5k0dEbjtIditHTFONQZobEjQjSRQLC+2XucDgrc1hDEI1lC2eVwUObzUDbAPDVv7XibDRt34XAX0BWJcdzUaqr8E4nEkoSiCTojMboiCYL2aIhQNEkkniAaSxBPJhFS/Qh6Voanf6inPohTzQKHOy2mTYOcvt+R6iBoNQT4nIc/hB1Asdsa3aFy1xmKsKM5ZL8SJpcVUOzTJaiPVHtHB6UlJaOdjXFr56EOQrEkyVhwRK6Xa1AwI8u2sDEmkM/MqOGTj46NmzdvZvv27dTV1eV9aExfS4Q6HEJpgZvSAjdTKg6nD0UTdERiBCMJmrqiNHZGaA1GiSWsb/FJwCXWwkZelwOPy9FrNMBgVVdW4XE6SMbDlLkdnDTdT3l5WU7HJo3VmTEatzo0RhIJ4nFD1F5/werwaPVhiNrpovEEETu91RHS6iSZSJrudRviSesYq8kCjMOJiyjgwOdIYBwO2sPWkM7uhZJyvF+T9kO2qY4zaxR6HJi1/T3LNFHZ2vmlj7R96Ctdf7/urLvsjZ2xpH19gxEHXbEkTlf8iHLR3/3mw3hof48lrIXJ1OB1haN02M2Adzyzk8WTSznnhGwfx/mT6+iD3cOaCzXshtqxcfPmzTz66KMYY9iwYQNAXgODwS4R6vNY8wRQAtOriwDr23YkbjUFBKMJ2kIxWoJRWoMxGjsjJE3PHvWp+Qo8LmvY4UCLLJWXl3POOefQ3NxMZWXloGYMdIj0WvJ5uNy5aivt7R1cffZciktLMUlI2B0sE0nsvgvWqIf0fan+CwmrXaJnfwe7eSFVm5FMZjRdYPeFAPvYw+l7nJ/0WhG770TycO1Gqs9E9zntdob0JofDP6eu0TMsyWwGSUlmabPodSzgcwY51BEGBIdJ4C92U1Hi696frZ7eZNvcV+KMfPZI3g8zwPnyIR9n79VUEg1S3teqZKpfHV0hwOBAMEnDy1vrx0ZQkCIiqXkJKrFWSlxljPnTYC8qItcDXwYmAW8AnzfGPNdH2g8BnwYWAwXAZuC7xpj/y0h3CfAdYBbwNvA1Y8zvB5u3o9VQOzZu377d6rXvchGPx/M+3WZtba31oZVIYIw5oo6XIkKB20mB20kVcFzavmTSEI4nCEUThGIJuiIJWoPR7j4DqU55hxcoMjjlcMCQWgGxvLx8zE8fXOD10uXsYkJV5cCJVS87duwg0HAIXF7qnA2cN6uGmTOnj3a2xq2NmzZy4gkzRzsb49LmPQF+smoXBnA74Yy5Y2TpZBEpAR4HzgXiQBPW5EVfFJHngPcaY3LqXioilwG3YQ1hfN5+/ouIzDfG7MlyyFLgH8DXsQKRq4Dfi8iyVCAhImdi9W24CavT44eAR0TkbGPMK7nk62g31I6NdXV1bNiwgXg8jsjwTLfZXb091BlfsnB0L6Oc/S2fChrCMasfQDiWpDMcpzMSo9PuhNgainfXNqSkah1cDsHltCYXcjsOT0Q0HPeSTfqMhgChkNGpmI9QJBrBaWIUud14o51Eojr6QI2O+VP9zJvUTKCli5suPmHYawkg95qCW4GTgY8CDxljEiLiBC4Hfm7v/1yO5/oicI8x5i779Q0iciHwGeCrmYmNMZmdGL8lIhcDHwBStQufB542xnzXfv1dEVlub78ix3wd9YbSsTFVKzBcfQrq6+sBcDqd3a9HsjPk4aCh7zTGGPbVH2TP/nqqavyUVVZ1z5bYZa/HEIpanQw7I/HuNRlSYUEqgEhVlgv2jITdEx2JPfnR4UmQcu0HsTTjQ9/6djb8U6Iejdrb2vt9rdRIqiz2EQmFOG3ecQMnzoNcg4JLgK8bY36T2mCMSQC/EZFq4N/JISgQEQ9wCvCDjF1PAmflmBeAEqAl7fWZwE8z0jyBTr+cV/Pnzx+2ubdTzRvJZHLMztvQ0NDAb1c+RCwWw+12c+WVVzKtn8DFGNM9S2IsboildSqMJw3ReJJQNE4sYdVSWJ0LremYQ/EEsYTV7p/SPaqxZ3SRMehP6OzooLEjws76Q5SVlfUYFtk92ZGkT2d8eFvPyZDGQze2/EnVtkRjXjpNjHDUwcHYcWyu9/Lk45u1tkUdE6S/8brdiUQiWE0Ef8uy73zgMWPMgAtmi0gtsB9Ymr6qooh8E7jKGDM3h3N8FvgecEKqA6SIRIFPGGPuTUt3NXCXMabXWCIRuQ64DsDv95/y0EMPDXTZnHR2dlJcXJyXcx2L4vE4wWCQwsJCXK6xt6p3Z2cnHR0d3a9LSkqG/ffdc7ZESJjU5Eb2PuixPxqL09LahsPpJhKPU1RSgjicJJPYEysd7kyYtDv+JZOHO/lZHQGzT8poxSEZQQqpOo/sUyIbeteUpP+cfhnJOsahj5P1o8e4Benx1Ef63uKxGPF4HJfLhcvjHjD9YM49FOMxTouEI3gLdEjnYGxpTrCl6fCIjVjSUOKx+jcBLDvOxbLj3H0dnpPly5evMcYsydye63/enViLH/UKCoCL7P3Dzu5M+H3gsqGMiDDG3AncCbBkyRKzbNmyvORv1apV5Otcx6qxXIbPPPMM69at616M6bzzzmPp0qWjna0e1q1bxzO7NjNpyhT27dvHGYuWsnjx4iM6lzWzI4dHEmSMQDg886LdOTM1MsEcnhwomT6cMTV6IG3b4YmYDr9Of7YOS5tYKbUtbdRA9+iFtMEFydT50o5NP966v7Sf0ydIss+3Zu1aTjn55F7H9SijXiMIeo9w6LE/vTyy7c9IO6C0RPkal9DfeY5kEqbX169n4aJFR5od67pDOnrk5fJluz+Z/1U2bXidq9+7jCLv8H9ZyvUKvwB+KCLFwG+AA8BErD4Fn8DqJ5CLRiABZNa5+oGDvZMfJiKXAvcCVxtjHsvYffBIzqnUYMybN49169YRjUbxeDzMmzdvtLPUSz6bYVLTSzvHxWj4/Gvd4WTR1IqBE6p+xfa5OHt29WhnY1xL7HeNSEAA1qRnAzLG/A9Wlf1VWLUFm4CngGuA7xljbsvxPFFgDXB+xq7zgRf7Ok5EVgD3AdcaY36bJclLgz2nUoPl9/t597vfzdy5c3n3u989ZmaFTJcaZVJSUjLmV94cyzZv3kxbWxubN28eOLFSR5FchySWAd/Gqro/g8PzFLxsjGnp79gsfgTcJyKrgRew5iCoBe6wr3UvgDHmavv15VgBwb8Bz4rIRPs8UWNMs/3zbfa+G4E/AB8ElgPnDDJvahQFAgFCoRCBQGBMfpgFAgGeeOIJYrEYO3bsoKqqakzm0+/34/P5xmTexoPURF2zZ8/m0UcfBfI7UZdSg5EeoI7E+3DAmgIRcWHNS3C+MabFGPMXY8xv7OfBBgQYYx7GGir4dWA91gf3RWl9BKbaj5RPYwUvP8Zqtkg9uhdhMsa8iNWUcS2wAbgaq9+BzlEwTqSmOe7o6GDlypUEAmNvBu0333yTzs5OIpEInZ2dvPnmm6OdJTUMNmzYQNLucJBMJrtn8FRqpKUC1GAwyKOPPjoiNVcD1hQYY+IiEsDqC5AXxpjbgdv72Lesv9f9nPO3QLamBTUODHaa49GQ6mCY/lodfTJHlOiIIjVaUjPJgtV5Md8zyWaT61qM92N1KFRqWIyHeQpqamp6zLpYUzM2x6ynN8OowTv11FMpKipCRCgqKuLUU08d7SypY1RNTU2PoGAk/ufk2p1xF3CliLwK/BGr+r7HmAtjzP/Lb9bUsSTVQW7t2rUsXbp0zNUSAEQiEQoLC3E6nSQSCSKRsTf9bV+rTarc+f1+PvrRj7J27VqWL1+u5adGjc/nw+fz4XA4un8ebrkGBf9rP0/GmpEwkwE0KFBDMtY7yNXW1uLxeEgkEng8njFZm1FfX080GgUgGo2OyWaY8WCsvxfVsaG2thafz4eI4PP5RuR/Tq5BwfCvwqDUGDfURaVGgtfrJRgMkkgkCAaDeL06k5xS49Vo1KDmFBQMZfZApY4mQ1lUaiSkN3EUFhaOySYOpVTuRrrWKteOht1ExJHx0C7YSo0RqSYOYMw2cSilcjfSE2n1GRSIyEQR+ZO9sFBqmxOIZTxaRWTsfnVS6hiiMxoqdfQYjXkK+qspuB44GXgkY7sAv8Sa4fA7QD3WBENKKXVU0GGdaixIzVOQmiNl+/btw37N/oKCC7GWHg5lbDfAL4wx3zLG3Az8DGulRKXUKBsPM0OOdYFAgAceeID29nYeeOABLUM1aurq6roDAhGhrq5u2K/ZX1Awl+wLCmX2Idhmp1VKjbJsM0OqwUlNZ51MJnU6azWqqqqqegxJrKqqGvZr9hcUFACd6RuMMQlgEvB62uawnVYpNcpSHQsTiUSP1yp3mX2ntS+1Gi319fW4XK7ux0gE+f0FBQ3AzMyNxpiAHRykzAAO5TtjSqkjkz4tqhq8efPmUVJSgsPhoKSkhHnz5o12ltQxajSmf+8vKHge+GgO57gaawlkpdQoq6+vR0RwOp2IiDYfHAG/388VV1xBaWkpV1xxhY7gUKNmNEYT9RcU/AR4h4j8wF4+uQcRcYnIj4BlwG3DlD+l1CCMh4WlxgOd5liNFSP9XuxzRkNjzEsi8u/AfwMfEZG/AXvs3VOB84Fq4KvGmJeGPadKqQGNh4WllFJjV7/THBtjfigia4GvAJdwuENhGHgW+G9jzD+GN4tKqcHQb7lKqSM14NoHxpingaft2QxT4yGaMjobKqWUUmqcy3WVxNRwxIZhzIsaZoFAYEyv8KeUUmp05RwUqPEtNdNdIpHA6XTqvPhKKaV6GfQqiWp8Ss10V1hYqDPdKaWUykqDgmNEaqhaMBjUoWpKKaWy0uaDY0RqqJr2KVBKKdWXUakpEJHrRWSniIRFZI2InNtP2kki8oCIbBGRhIjckyXNtSJisjx0TYY0fr+fxYsXa0CglFIqqxEPCkTkMqwZEG8FFmOtxPgXEZnaxyFeoBH4HvBKP6cOYi3W1P0wxoTzlW+llFLqaDcaNQVfBO4xxtxljHnTGHMDcAD4TLbExphdxpjPGWPuAZr7Oa8xxhxMf+Q/60oppdTRa0SDAhHxAKcAT2bsehI4a4in94nIbhHZJyKPi8jiIZ5PKaWUOqbISC6vKiK1wH5gqTHm2bTt3wSuMsbMHeD4x4FGY8y1GdvPBOYArwMlwL8CFwELjTHbs5znOuA6AL/ff8pDDz00lNvq1tnZSXFxcV7OdazSMswPLceh0zLMDy3HoRuOMly+fPkaY8ySzO1HxegDe0Gm7kWZRORFYD1wA/C5LOnvBO4EWLJkiVm2bFle8rFq1Sryda5jlZZhfmg5Dp2WYX5oOQ7dSJbhSPcpaAQSQGb3dz+Qtz4A9pTMrwF1+TqnUkopdbQb0aDAGBMF1mAtu5zufKxRCHkhIgKchNWBUSmllFI5GI3mgx8B94nIauAF4NNALXAHgIjcC2CMuTp1gIgssn8sBZL266gxZrO9/ybgZWC7neZzWEFB1hENSimllOptxIMCY8zDIlIFfB1rPoFNwEXGmN12kmzzFazLeP0+YDcw3X5djtVHYCLQZqc/zxizOq+ZV0oppY5io9LR0BhzO3B7H/uWZdkmA5zvC8AX8pI5pZRS6hilCyIppZRSCtCgQCmllFI2DQqUUkopBWhQoJRSSimbBgVKKaWUAjQoUEoppZRNgwI1ZgQCAUKhEIFAYLSzopRSxyQNCtSYEAgEWLlyJR0dHaxcuVIDA6WUGgUaFKgxob6+nkQigcPhIJFIUF9fP9pZUkqpY44GBWpMqK2txel0kkwmcTqd1NbWjnaWlFLqmKNBgRoT/H4/K1asoKSkhBUrVuD3Z66urZRSarhpUKDGDL/fj8/n04BAKaVGiQYFSimllAI0KFBKKaWUTYMCpZRSSgEaFCillFLKpkGBUkoppQANCpRSSill06BAKaWUUoAGBUoppZSyiTFmtPMwqkTkELA7T6erBhrzdK5jlZZhfmg5Dp2WYX5oOQ7dcJThNGNMTebGYz4oyCcRec0Ys2S08zGeaRnmh5bj0GkZ5oeW49CNZBlq84FSSimlAA0KlFJKKWXToCC/7hztDBwFtAzzQ8tx6LQM80PLcehGrAy1T4FSSimlAK0pUEoppZRNgwKllFJKARoUKKWUUsqmQUEeiMj1IrJTRMIiskZEzh3tPI0lInKeiPyfiOwXESMi12bsFxG5WUTqRSQkIqtEZEFGmgoRuU9E2uzHfSJSPpL3MZpE5Ksi8qqItIvIIRF5TEROyEij5dgPEfmsiGywy7BdRF4SkYvT9mv5DZL9vjQi8rO0bVqOA7DLx2Q8DqbtH7Uy1KBgiETkMuA24FZgMfAi8BcRmTqqGRtbioFNwL8CoSz7/x34EnADcCrQAPxNRErS0jwAnAxcaD9OBu4bxjyPNcuA24GzgHcAceDvIlKZlkbLsX/7gK9g3fMS4B/AH0TkJHu/lt8giMgZwHXAhoxdWo652QpMSnucmLZv9MrQGKOPITyAV4C7MrZtB/5ztPM2Fh9AJ3Bt2msBDgBfS9vmAzqAT9mvjwcMcHZamnPsbXNH+55GqRyLgQTwPi3HIZVjM/ApLb9Bl1sZ8DawHFgF/MzeruWYW/ndDGzqY9+olqHWFAyBiHiAU4AnM3Y9ifWNTg1sBjCRtDI0xoSAZzlchmdiBRMvph33AtDFsVvOJVg1fS32ay3HQRARp4hcjhVcvYiW32DdCfzWGPN0xnYtx9zNtJsHdorIQyIy094+qmWoQcHQVANOIJCxPYD1S1UDS5VTf2U4EThk7HAYwP65gWO3nG8D1gMv2a+1HHMgIieKSCcQAe4APmiM2YiWX85E5JPAbODrWXZrOebmFeBarGr/T2Ld94siUsUol6FrKAcrpUaeiPwIq6rwHGNMYrTzM85sBRZhVX9fCvxaRJaNYn7GFRGZi9V/6hxjTGy08zNeGWP+kv5aRF4GdgDXAC+PSqZsWlMwNI1Y7br+jO1+4GDv5CqLVDn1V4YHgRoRkdRO++cJHGPlLCL/A1wBvMMYsyNtl5ZjDowxUWPMW8aYNcaYr2LVtnwBLb9cnYlVQ/qGiMRFJA4sBa63f26y02k5DoIxphN4A6hjlN+LGhQMgTEmCqwBzs/YdT4923pU33ZivYm7y1BECoBzOVyGL2G1/Z6ZdtyZQBHHUDmLyG0cDgi2ZOzWcjwyDsCLll+u/oDVS35R2uM14CH7521oOQ6aXUbzsDoYju57cbR7YY73B3AZEAU+gdUj9DasDiDTRjtvY+Vhv3kX2Y8g8E3756n2/q8AbcCHgBOw/sHUAyVp5/gLsNF+459p//zYaN/bCJbh/wLtWMMRJ6Y9itPSaDn2X4bfw/rHOh3rg+0/gSTwHi2/IZXrKuzRB1qOOZfZD7BqWGYApwOP23/f00a7DEe9cI6GB3A9sAur89Ia4LzRztNYemCNsTdZHvfY+wVriM4BIAw8A5yQcY4K4H77D6fd/rl8tO9tBMswW/kZ4Oa0NFqO/ZfhPcBu+++0Afg7cIGW35DLNTMo0HIcuMxSH/JRYD/wKDB/LJShrpKolFJKKUD7FCillFLKpkGBUkoppQANCpRSSill06BAKaWUUoAGBUoppZSyaVCglFJKKUCDAqVUFiJyrYgYEZk92nlRSo0cDQqUUkopBWhQoJQax0TEO9p5UOpookGBUmrQRORUEfmtiOwTkZCIbBWRW0XEl5bmpyISEBF3xrElItIhIt9L21YjIneIyH4RiYjIFhG5LuO4VJPGeSLyiIi0Yq1Ln8rP30Skyc7PDhG5fXhLQamjj2u0M6CUGpemYi07fA/QASzAWuhqJnC5nebnwL8AHwRWph17JdZqbr8AEJFS4HnAhzXf+07gAuDnIuI1xvw049q/AR4ELgVcIlIMPAGsBq618zMdOCsvd6rUMUTXPlBK9SIi1wJ3A3XGmLcGSCuAEysYuBeoMcY02ftWAQljzDvT0q8FGowxF9qvvwF8DTjRGLM9Ld1dWAHFRGNMPC1PPzbGfCEt3RLgVWChMWbDEG9dqWOaNh8opQZNREpF5L9E5G2sVQdjwH1Yq7vVpSW9HVguInX2cacCi7FrCWwXYjUD7BQRV+qB9e2/CpifcfnfZ7zeDrQCvxCRj4jIcfm4R6WORRoUKKWOxN3Ap4GfAOcDpwKftfcVpKX7PXAQ+JT9+tNYS8Y+lpZmAnAeVmCR/njE3l+Vce0D6S+MMW3Acvu8twN7RGSTiFxyhPem1DFL+xQopQZFRAqA9wM3G2NuS9t+YmZaY0xMRH4JXC8i/43VxPBDY0w8LVkT0AD8ax+X3Jp52izXWQ9cYtcwLAG+CqwUkYXGmE0535xSxzitKVBKDZYXqw9BLGP7tX2k/wVQjvXN3wvclbH/r8A8YI8x5rUsj45cM2aMiRtjXga+gfX/7fhcj1VKaU2BUqp/F4rIwYxtbcDLwJdE5ADQCHwMmJztBMaY/SLyf1idBh8zxuzNSPI/wGXAcyLyP1g1A0VYgcK5xpj395dBEXkvcB3wB6yRC0XA57BGIbyU430qpdCgQCnVv8zhgABvAO/FGnL4v0AIa8jhvwKP93GeR7CCgl9k7jDGtInIWVhDGr+CFVy0YgUHj+aQx+12Hr4BTMIKBl4FzjfG7MvheKWUTYckKqWGnYj8BjgbmGmMSY52fpRS2WlNgVJq2IjIGcAirOaBL2pAoNTYpjUFSqlhIyIG6MRqXvhUxqgDpdQYo0GBUkoppQAdkqiUUkopmwYFSimllAI0KFBKKaWUTYMCpZRSSgEaFCillFLKpkGBUkoppQANCpRSSill06BAKaWUUoAGBUoppZSyaVCglFJKKUCDAqWUUkrZNChQSimlFKBBgVJKKaVsGhQopZRSCtCgQCmllFI2DQqUUkopBWhQoJRSSimbBgVKKaWUAjQoUEoppZRNgwKllFJKARoUKKWUUsqmQYFSSimlAA0KlFJKKWXToEAppZRSgAYFSimllLJpUKCUUkopAFyjnQGlclVTU/Os0+mcOtr5UEpll0gk9hw6dOi80c6HOnIaFKhxw+l0Tj148GDjaOcD4Cc/+UnVr3/96+o1a9ZszWdapcaziRMnatA+zmnzgVKKUCgkH/7wh6cXFxcvrq6uXnjzzTf7+0v78Y9//LgJEyacVFpauugjH/nI1EgkIqn9hYWFi9MfTqfzlGuuueY4gDVr1hSccMIJx5eWli4qLS1ddNZZZ81Zs2ZNQeY1wuGwzJw5c4Hf7z9peO64p29961sTqqurFxYXFy/+8Ic/PD0UCklfaf/4xz+WzJgxY4HP51t8+umnz9m2bZsnta+/chzo3r/xjW/46+rqFhQVFS2ePHnyid/4xje6j92+fbsns1xF5JSbbrrJD3DjjTdOTN9XUFBwssPhOOXAgQP6xU8NigYFSin+7d/+rXbHjh3enTt3bnjyySe3/uxnP5v429/+tjRb2q9//esT169fX7hx48Y3tmzZsmnjxo2FN95446TU/mAwuC71OHDgwOterzd5+eWXtwBMmzYt9uijj77d2tq6vrm5ef1FF13UesUVV8zMvMZNN93kr6ysjA/mHiZPnnzi1q1bPQOn7OnRRx8t/clPfjLpr3/969adO3du2L17t/dLX/pSbba0Bw4ccH3kIx+Z9Y1vfKO+qalp/aJFi4If/vCHu/PfXzkOdO/GGO6+++6dra2t6/785z9v+9WvfjXhzjvvrACoq6uLppfr2rVr33A4HFx55ZUtAN/73vcOpu+//vrrD5522mkdkyZNGlQZKqVBgVJ9+I//+I+Jxx133AlFRUWLZ82ateDee+8t7yutiJxyyy23TJgyZcqJFRUVCz/1qU9NSSQSPdJcd911U0pLSxdNnjz5xJUrV3Z/4N52221VM2fOXFBUVLR4ypQpJ37/+9+vHr67yu6RRx6p+trXvnagpqYmcfLJJ4evuuqqQ/fcc0/WfDzxxBPln/3sZxv8fn+itrY2/pnPfKbhwQcfzJr23nvvraisrIxfcMEFnQDV1dWJuXPnRh0OB8YYnE6n2bt3rzf9mC1btngeeeSRqn//938/kP877e2ee+6puuKKKxqXLFkSrqmpSXzta1+rX7lyZdb7+c1vflM+e/bs8Mc+9rGWwsJC8/3vf79+69athevWrSuA/stxoHu/5ZZbAuecc07Q7XazcOHCyLvf/e7WF154oThbPu66666qJUuWdMydOzeauS+ZTLJy5cqqq666qik/JaSOJRoUKNWH2bNnR5577rmt7e3t62688cb6T33qUzN2797t7iv9Y489Vr5mzZrNq1evfvOJJ54ov+2227o/WF5//fWiuXPnhpubm9d/7nOfO/gv//Iv05PJJAB+vz/+2GOPvdXR0bHuF7/4xc5vfvObxz3//POF2a7xxBNPFJeUlCzq6/HEE09k/RDpz6FDh5yHDh1yn3rqqcHUtkWLFoW2bdvWq1o/JZV3sL7hBgIBd1NTkzMz3W9+85uqFStWNDkcPf/VlJSULPL5fKd87Wtfm3rDDTccTN/3mc98ZurNN9+8v7Cw0Az2Xo7Etm3bfAsXLuy+99NOOy3U1NTkOnjwYK/7eeONN3zz58/vTltaWpo87rjjIq+//npBruXY372nJJNJXnnllZIFCxaEs+3r70P/iSeeKG5ubnZdffXVLbmVgFKHaVCgVB8+9rGPtUyfPj3mdDr55Cc/2TJt2rTIc889V9RX+i9/+csH/X5/oq6uLvrpT3868Mgjj1Sm9tXW1ka/9KUvNbpcLq6//vqmQ4cOufft2+cCuPzyy9sWLFgQcTgcXHzxxZ1nn312+9NPP531w/2CCy7o7OjoWN/XI/WNfDDa2tocAFVVVd1VG+Xl5Ymurq5eH4oA73jHO9puv/12f319vWvPnj2un//85xMAOjs7e/w/2bZtm+fVV18tue6663p1Du3o6Fjf3Ny87tZbb91z8sknd3+I3nvvveWJREKuvvrq1oHyvX37dk96QHTgwAHPKaecsiD1+o477qgc6BwAwWDQUVFR0X3vlZWVCYC2trZe99/V1eUoKyvrUQVUUlKSaG9vd+Zajn3de7ovfelLtclkkhtuuKFX2T355JPFTU1N7muuuSbrh/7dd99d9Z73vKelrKwsmW2/Uv3RTihK9eFnP/tZ1c9+9jP//v37PQChUMh56NChPv9mpk+fHk3/ORAIdNcq1NTUxFI/l5SUJAHa29udQHzlypWl3/3ud2t37dpVkEwmCYfDjgULFoSG5aaySH14tLS0OAsLC+NgBQpFRUWJbOlvvfXWA9dff71r0aJF8z0ej/noRz966M033yycMmVKLD3dL3/5y6qTTz65c968eb2quMH6lv3lL3/5UFVV1aJzzz13U0lJSfIb3/jGlMcff3x7Lvmuq6uLdnR0rE+9njx58on/+Mc/tmarUu9PYWFhsrW1tfuDu6WlxQGQ+eEPUFRUlLR/b906OzsdpaWlicGUY+a9T548ubvt/9Zbb61ZuXJl1bPPPrvF5/P1qi25++67qy688MKsH/odHR2OP//5z5UPPvjgW4MpA6VStKZAqSy2bdvm+eIXvzjttttu29PS0rK+o6Nj/ezZs0PG9F2jvWvXru5Obrt37/b4/f5Yn4ltoVBIrrnmmlmf//znAw0NDa93dHSsX7p0aVtf1/nrX/9anNkLPf3x17/+ddDNBzU1NYmamprY6tWru5ss1q9fXzhnzpxeVdcAxcXF5t57793T0NCwYd++fRurqqriCxYsCDqdPb9Y21Xc/Q4hTQVBu3btcm/atMm7f/9+z/Lly+dVV1cvvOqqq2Y1Nja6q6urFx5JB8JczZkzJ/T666933/vq1asLq6qq4hMnTuz1Yb5gwYLQ5s2bu9O2t7c79u7d6124cGF4sOWYfu+pbT/+8Y+rbrvttkl/+9vfts6aNavX+6ezs1P+/Oc/V1x77bVZmw7uv//+8rKysvjFF1/ckXsJKHWYBgVKZdHR0eEQESZOnBgDqzPgW2+95evvmB/+8IcTDx065Hzrrbfcd9xxx4RLLrmkeaDrhMNhiUajjgkTJsTcbrdZuXJl6QsvvJC11z/AhRde2JneyzzzceGFFw66+QDg0ksvbbr11lsnHTp0yLlu3bqC3/zmN9XXXntt1g/0nTt3unft2uVOJpM89dRTRT/4wQ9qv/nNb9anp/nb3/5WFAgEelVx//73vy994YUXfPF4nObmZscnP/nJ40pLS+OLFy8On3rqqaEdO3ZsWLt27Rtr16594yc/+cmuqqqq2Nq1a9+YNWvWgN/+9+/fv3GwtQQA11xzTdODDz5YvWbNmoLGxkbnrbfeOmnFihVZ7/3KK69s3b59e8E999xTHgwG5Stf+cqkOXPmhBYvXhyG/suxv3sH+PnPf155yy23TPnLX/6ybf78+Vnv4/77768oLS1NvPe97836oX///fdXffjDH+7Vh0OpXOk7R6ksTjnllPB1110XOO+8846vqalZuHHjRt/ixYv7/cC9+OKLWxcvXjx/yZIlC971rne1ff7znx9woqWKiorkLbfcsufqq6+eVVZWtuiBBx6oeuc739mWvzvJzQ9/+MP66dOnR2bMmHHSu971rrmf/exnA5deemk7HB4jv337dg/Ali1bvGefffa8oqKixR//+Mdn3HTTTfs+9KEPtaef7+67766+4IILWisqKnpUcbe0tDg/8pGPzCwtLV08a9asE3ft2uV97LHHthcWFhq3283UqVPjqUdVVVVCRJg6dWrc5erdapNt7H764+c//3lOfQouvfTS9n/5l385+O53v3vu9OnTT5oyZUr0hz/8YXeQM3v27AWpc9XW1sbvu+++t7/1rW9NrqysXLxmzZrilStX7silHPu7d4Bbbrllcmtrq/Occ845PnUPV155ZY/JgO6///6qSy+9NOuH/s6dO92vvPJK6Sc+8QkddaCOmPRXHarUWDJx4sRdY2VGw0wicsrGjRs3nXDCCZHRzotSo2XixInVBw8enD7a+VBHTmsKlFJKKQVoUKCUUkopmw5JVCoPjDFrRjsPSik1VFpToJRSSilAgwKlxoQbb7xx4mWXXTatr/2TJ08+8Q9/+EPJQOfZunWrR0ROicWsIe7nnXde3U9/+tOqPGZVKXUU06BAjVuTJ08+saCg4OTCwsLFVVVVCy+55JLpqalmx6qf/OQnVU6n85T0oXNXX3311O9973sHH3744d35vt6zzz67/YYbbmhKXfuUU06Zm+9rDNXWrVs9p59++hyfz7d4xowZC/oLfnbu3Ol+5zvfOausrGyR3+8/6b//+79rUvuyTewkIqfcc8895ZC97B9//PEBA62huuOOOypra2tP9Pl8i9/1rnfNCgQCWaePBnjxxRd9CxYsON7n8y1esGDB8S+++GL33BjJZJLPfOYzk8vLyxeVl5cv+sxnPjM5tQbFgQMHXCeffPK88vLyRSUlJYsWLVo078knn+wxJXdfy0MPtCzzY489VjJnzpz5JSUli8rLyxedf/75s3bu3NnnGiBqfBvT/0CVGshDDz20PRgMrnvttdc2b9iwoeirX/3qpMw0qW/NY8WiRYt6TEB077337hntPI2myy67bOaJJ54YPHTo0Pqbbrpp/0c/+tFZ9fX1Wfs7XX755TOmTZsWbWhoeP0Pf/jD9u9+97uTH3vssRLoPbHTypUr3yosLEymz6GQWfZ9TQKU6bTTTpt7JAHEa6+9VvClL31p2q9+9audBw8efN3n8yU//vGPZ60RCofDcumll85esWJFU3Nz8/orrrii6dJLL50dDocF4Ic//GH1X/7yl4o1a9a8sW7dujeefPLJ8h/84Ac1YE3JfPfdd+9sampa39bWtv5LX/rSwQ9/+MN1qfd+f8tDD7Qs86JFi0JPPvnk9o6OjvUHDhx4fdasWZG+7kGNfxoUqKPCjBkzYu94xzva3nzzTR9Y8wb853/+Z820adNOmD59+olg/VOdOnXqCWVlZYve8Y53zE6fXva1114rOOuss+rKysoWVVVVLbzxxhsnAiQSie4llMvLyxdddNFFM1Pf9ILBoLz//e+fkfp2dsIJJxy/d+9eF1jfSqdMmXJiUVHR4smTJ5840EQ6X/ziF2vf//73z0i9/t///d/K2traE8vLyxd95StfmZietr88ZTrttNPm/uhHP6peu3ZtwZe//OVp69evLy4sLFxcUlKy6JlnnimsqqpaGI93T7vPr3/96/K5c+fOH2TxH7ENGzZ4N2/eXPj973+/vri42Fx77bWtc+bMCd1///0VmWnb2tocq1evLrnl/7d350FNnP8fwD+bA3KTgJhAuMItGBKOap1pnW+p4oB1aosFBa1lxhlrvf5oO4hoZ9pRemi12ipeTEFTrHiM2nqLqFXbwSICagWi5QxXIZiEhJDr94duJkqA2P6sUD+vGWas2d3n2ajdzz7Ps/tet67N3d3dNmXKFENKSoq6oKDAacxxYWGhV3JysprH4z23YKDCwkKvxMTE3uTkZJ2Hh4f1iy++UJ05c4ZP5is4OnnyJNdsNhNr167tZDKZtjVr1nTabDYgi5Hi4uJxy5Ytaw8JCTFJJBLTsmXLOn744QcvAAAWi2WTyWRGKpVqj2XWaDTUzs5OGtkPV+Ohn4xl9vf3NwcFBdkrayqVamtsbHR3ti8a+7AoQP8JSqWSXlpa6hETE2NPnfvpp5/45eXlf9TW1t46fvw4d926deLi4uL77e3tVf7+/sY5c+YEAzwMwElOTg6fPn26pq2trUqpVNYkJSVpAQDy8vLGnzhxgn/x4sXatra2Kj6fb1m0aFEAAMC2bdu8tFottbm5uVqtVt/Mz89vfBSYQ1m9enXAiRMn6vr6+iqvXbt21zFOdyQVFRWMjz/+OLCgoODPtra2qu7ublpHR4f93f/D9WkocXFx/Rs2bGgk75QfZSzo+Xy++ciRI/bXKisUCq+5c+c6fSPe6tWrRcPFNrt6fo5u3rzJ9PPzMzq++TA6Otpw+/btQbHN5FC54wvXbDYb1NbWDnr9tEajoZw6dWpQRsCdO3dYAoFAFhQUNPHjjz/2edajSH/88QcjJibGHm4VHR1tpNPptlu3bg06v5qaGkZkZKTB8W2FkZGRhpqaGiYAgFKpZMTFxdmPFR8fr3/y1dvh4eFRDAYjLjMzMzQ9Pf0vMmjJ1XjooWKZyURKNpsdt3PnTuHKlSudRj6jsQ8fSURjWkZGRiiVSrVxuVxLYmLig/Xr17eRn61atapdKBRaAAAUCoVnenp69yuvvKIHANi6dWurl5eXvLa21u3ixYuccePGmT/99NMOgId3XYmJiX0AAN9//7335s2bm8hwms8//1wVEhIiNZlMf9LpdJtarabduXPHffLkyYZXX31VD/DwgkQQhK2yspIZEhIyEBgYaAoMDLRffaqqqjiOF9GjR48+lgq4f/9+QWJi4oPk5GQdAMCmTZtURUVF48nPh+vT035/c+fO7VYoFF5paWmajo4O6pUrV3h79uxxOp2Rl5fXnpeX9/96MdBqtRQul/tY8JCHh4dFpVINmrMWCATWuLg43erVq33y8/Nbbty4wTx16pRAIBCYn9x23759fD6fb05JSbFPD0ybNk1bWVl5OywsbKCiooI5b968YBqNZvv888+dnpPjn5HBYKCmpaWFUqlUGwDA8uXLXfou9Ho99cm0RQ6HY3EWy6zT6ag8Hm9QLLNWqyVHpqiOEc8CgcCi1+spVqsVyEKirq7ujl6vJ/bt2ycYGBggHPoxZDy0Y/DTULHMZCJlR0cHdevWrd7R0dFOQ57Q2IdFARrTiouLlbNnz3Y6LyyRSOyhMu3t7W6xsbG95H97eHhY+Xy+pbGxkd7c3EwPDAx0+j+5trY2t8zMzFCCIOy3p1QqFVpaWuhLlizpaW5udsvIyAjWarXUt99+u2fLli2tPB7PWlhYeP/rr78WLl++PCg+Pl63efPmFjL4RiaT6SoqKmod2zlx4oQH+WuVSkUXi8X2vvN4PCufz7df+Ibrk4tfm92iRYu6pVLpRI1GQykqKvKMj4/XORYwzxqXy7XqdLrHLpAajYbC4XCcxjbv37//z8WLFweIxeIYf39/41tvvdXtbKTghx9+GJeWlvZYRoBjyNCkSZMM2dnZqi1btoiGKgocY5knTZoU8cknn6hcXYNAYrFYFo1G89iIbF9f36BCAeBhsaDVah/bVqfTUcmiicViWXp7e+2f9/b2UlkslvXJHAQWi2VbvHhxT3BwcHRCQoJ+ypQpBlfjoYeLZQYAEAqFlsWLF/8VGxsb3d7eXkWn43rD/xqcPkD/WQRhv1ECkUg04DgPqtFoKL29vdTAwECTv7+/qampyekcqVAoNB05cqROq9XeJH+MRuMNiURicnd3t3399ddt9+7du/3LL7/cPXfunMf27du9AABSU1M1165dq29ra6sKCwvrX7RokcsLs3x8fEytra326QKtVkvp7e21F/DD9cnV74MkkUhMcrlct2/fPv6PP/7olZGRMWSYzqpVq0TDBRC5en6O5HK5oaWlxd1xjv327dusoe5Ew8PDB8rKypRqtbqqurr6bnd3Ny0uLq7PcRulUkkvLy/njhQMRBAEPOvslwkTJvRXV1fbo5Tv3LnjNjAwQEycOHHQ+Uml0v67d++yyGkSAIC7d+8ypVKpAQAgNDS0/8aNG/ZjVVRUMENDQw1PHodkNpuJ+vp6dwDX4qFHimUmmUwmoqenh9bT0zPkUxRo7MKiAL0QMjIyeg4cOOB17do1psFgIFauXCmWyWR9ERERA2lpab1dXV30zz77bLzBYCDUajXlwoULbACArKyszjVr1vjV1dW5AQCoVCqaQqHgAzx8VKu8vJxpNpuBz+dbaDSajUKh2Jqbm2kKhYKv0WgoTCbTxuFwBt3NDWfevHnqCxcueJw5c4bT399PfPjhh742m81+RR+uT8Px8fExtbe3u5Gr2UmZmZndW7ZsEdXV1TEXLFjQO9T+X3zxRftwsc0un6CDmJgYY2RkpD47O9tXr9cTe/fu5dfW1jLnz5+vdrb9jRs3GGq1mtLf309s377d88qVK7zc3NwOx2327NnjJZfLddHR0Y+FU5WUlPDIhaCVlZWMr776yjc5OXnI83VUXl5e+7SjBAAA7733XveFCxf4p0+f5mg0GkpOTo7YWXokAEBKSoqWSqXa1q9fP95gMBB5eXneAABku/Pmzevetm2bkIyu/u6770Tk3H9paSmb/Pui0+mI3NxcUXd3N33q1Kl9AK7FQw8Vy1xUVMSvqqpyt1gsoFKpaMuXL/efMGGCnpyaQ/8tWBSgF8Ls2bO1OTk5qvT09BCRSCRraGhwJyNvBQKB9eTJk3WnTp3iC4VCWVhYmPT8+fNcAIA1a9Z0pqSk9CYlJYWz2ezYyZMnR/72229sgIfD/GlpaSFcLjc2Kipq4pQpU7QffPBBt9VqJbZs2SIUi8UxfD5ffvXqVe6OHTtcfgdBQkJC/5dfftn03nvvSUQikUwgEJiFQqF96Hu4Pg3njTfe0IaFhRmEQqFMIBDIyN+fP39+r0qlck9KSlJzudx/faV+SUnJ/Zs3b7I9PT1jP/nkE799+/bd8/X1NQMA5Ofne4aGhkaT2/7888+84OBgqUAgkBcUFHgfO3asntyWdODAgUEL5QAAzp8/z5PL5dFMJjN21qxZYTNnzlQPty5guFER8umUkSQkJPRv3LixMSsrSyIUCmU6nY5SUFBg/7swderUMPJYDAbDdvDgQeWPP/7oJRAIYhUKxbiDBw8qGQyGDQDgo48+6kpKSnogl8ujZTJZ9LRp0x589NFHXQAPH2dcsWJFgJeXl1wsFsvOnj3rcejQoXryqYGR4qEBho5lbmlpcUtJSQnncDixMTEx0RQKBY4ePXrPlfNHYw9GJ6MxYzRHJ491/v7+E7/99tvGodZnIOQKjE4e+3CkAKEXXGFhIZ8gCJg1axYWBAi94PDpA4ReYJMmTYpQKpWM3bt3/0ml4roxhF50WBQg9AIrLy+vHXkrhNCLAqcPEEIIIQQAWBQghBBC6BEsChBCz01ZWRlLLpdHJiQkRMyaNUtiNBoHv2EJIfSvwaIAIfTcBAcHm65cuVL7+++/1wYEBAwUFxfzn3efEHqRYVGA0H9Iampq0IoVK3zHSpuBgYEmDodjAwBwc3OzUSgUfHEKQs8RFgUIoeeurq7OraysjDd37twHz6oNg8FApKWlBfr6+krZbHZsZGRkVElJCW+o7d98802Jt7d3DIfDiQ0KCpq4adOmcY6f5+XleU+cOHGCm5tbXGpqatDT7o/QaISPJCI0SplMJngRUuh6enooGRkZkqKioj/d3d2f2UiByWQi/Pz8TGVlZbWhoaEDJSUlHllZWSEymex2RETEwJPbr127ti06OrqByWTaKisrGdOnT4946aWX9GREtlgsNmVnZ7edPn2a19/fP+gGa6T9ERqNcKQAoVFELBZLc3NzReHh4VFsNjuuvr7ebcaMGSECgUAmFoul69atG++4/dWrV5lRUVET2Gx27MyZM4ONRqP93zRBEPG3bt2ypz8+OcyvVCrpSUlJIQKBQMbn8+XvvvtuAPlZQ0MDfah2h2vTGYlEEh0eHh7V1NREAwC4fv06g8vlyisqKhgmkwneeuut4LVr16pkMplxuOP8Uzwez7pp0yZVRETEAJVKhXnz5j0Qi8XGX3/9leVs+4SEhH4mk2kDAKBQKDaCIGy1tbX273PhwoW9CxYs6PXy8jL/nf0RGo2wKEBolDl8+LDnyZMn6//666+bs2fPDpFKpfq2trbqc+fO1e7YsUN4+PBhHsDDEJx33nknND09vbunp+fmnDlz1KdPn+a70obZbIaZM2eG+fv7DzQ2NtaoVKqqzMzMHgAAi8UCM2fODHXW7t9ps6am5g6LxbIePHiQbzQaiYULF0pWrFjRHh8f379r1y7Pqqoqzvr1630nTZoUsXv3boGzY7z22muhXC5X7uzntddeC326b/ih5uZmWmNjI0MmkzmNaQYAmD9/fgCTyYyVy+UTvb29Te+8885TTW/80/0R+rfh9AFCo8z777/fERoaarpw4QK7p6eHtnHjxjYAgKioqIEFCxZ07d+/3zM1NVVTVlbGNpvNxNq1azspFApkZWWpt27dKnSljYsXL7I7OzvpO3bsaCanKGbMmKEDALh06dKQ7bJYLOvTtsnhcGyJiYmampoaZk5Ojg+NRrN9+umn7QAAS5cu7Vm6dGnPSP0tKytTunJerjIajUR6enpwampqd2xs7JBFgUKhaCosLGwqLS3llJaWcsnEQlf90/0R+rfhSAFCo0xgYKAJAOD+/ftuXV1dbo53xVu3bvXp6uqiAQA0NzfTx48fb3KMuvXz83NpCL6hocFNLBYPOFuzMFy7I7WZn5/vScYLT506NYz8falUajh//jx/586dwsLCwgYa7fndj1gsFkhNTZXQ6XTr999/3zTS9jQaDWbMmKFraWmhb9iwwftp2/un+yP0b8KRAoRGGYIgbAAAQUFBA2Kx2NjY2HjL2XZisdjU2dlJt1qtQF6kW1tb3SUSiREAgMFgWPv6+uxX787OTrpYLB4gj61SqdycLWYcrt0TJ05whmtzyZIlPUuWLBl05x8TE2NobW11y83NbYmLixvyznwoU6dODfv99985zj5LSEjQXb58ud6V41itVkhPTw/q6uqilZaW1j/NwkaLxULcu3fvb68J+Kf7I/RvwJEChEap//3vf31sNtuSm5sr0ul0hNlshuvXrzMuXbrEAgB4/fXX+6hUqm39+vXjjUYjUVRUxK+urrYvmpswYYKhqKjI02w2w6FDh3jXr1/nOh7b29vbtHTpUj+NRkPR6/XE2bNn2SO1O1KbQykoKPACAPjggw+6/853cfny5Xq9Xl/p7MfVggDg4Rx/fX094+zZs0ry/QjOtLa20nbt2iV48OABxWw2w+HDh3nHjx/3nDZtmobcxmQygV6vJywWC2GxWAi9Xk+YTCaX90doNMKiAKFRikajwcmTJ5XV1dXMoKCgGE9PT/miRYuC1Go1FQCAwWDYDhw4cK+4uHicp6envKSkxHPGjBm95P7ffPNN09mzZ/keHh6xCoXCa/r06WrHY//888/K+/fvuwcEBMSIxeKY4uJiz5HaHalNZ8rKylh79+4dLxQKTVVVVYxn822NrK6uzm3//v3ed+/eZfn4+MjIaY78/HxPgIejEatWrRIBABAEAbt37x7v5+cXw+fzY3NycvzWrVvXnJmZaV8omJ2d7ctms+O2b98uOnbsmCebzY7Lzs72dXV/hEYjwmbDdS9obBCJRA3t7e1/Pe9+INcZDAZCKpVGZWVldV2/fp09efJkXU5OTtfz7hd6NkQi0bj29vag590P9PfhSAFC6Jn58MMPfceNG2fKycnplEql+tOnT3v09/dj6BFCoxQWBQihZ6KsrIylUCi89+7d20ChUCAzM1Pd2NjIePnllyOed98QQs7h9AEaM3D6AKHRDacPxj4cKUAIIYQQAGBRgBBCCKFHsChACCGEEABgUYAQQgihR7AoQAghhBAAYFGAEEIIoUcwEAmNGRaLpUkkEgU8734ghJyzWCwjpk6i0Q3fU4AQQgghAMDpA4QQQgg9gkUBQgghhAAA4P8AX7nmjCn3dsYAAAAASUVORK5CYII=",
-      "text/plain": [
-       "<qiskit_experiments.framework.containers.figure_data.FigureData at 0x353a479d0>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<qiskit_experiments.framework.containers.figure_data.FigureData at 0x353a44d30>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
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",
-      "text/plain": [
-       "<qiskit_experiments.framework.containers.figure_data.FigureData at 0x333780c10>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<qiskit_experiments.framework.containers.figure_data.FigureData at 0x333781e70>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "# Plot 1Q/2Q simultaneous direct RB curves\n",
     "for i in range(0, 5):\n",
@@ -973,83 +385,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": null,
    "id": "3207af8e",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>name</th>\n",
-       "      <th>experiment</th>\n",
-       "      <th>components</th>\n",
-       "      <th>value</th>\n",
-       "      <th>quality</th>\n",
-       "      <th>backend</th>\n",
-       "      <th>run_time</th>\n",
-       "      <th>qubits</th>\n",
-       "      <th>reason</th>\n",
-       "      <th>a</th>\n",
-       "      <th>alpha</th>\n",
-       "      <th>b</th>\n",
-       "      <th>chisq</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>4a949ae1</th>\n",
-       "      <td>ProcessFidelity</td>\n",
-       "      <td>LayerFidelity</td>\n",
-       "      <td>[Q7, Q6]</td>\n",
-       "      <td>0.795+/-0.034</td>\n",
-       "      <td>bad</td>\n",
-       "      <td>ibm_kyoto</td>\n",
-       "      <td>2024-03-21 16:29:46.643000+09:00</td>\n",
-       "      <td>(7, 6)</td>\n",
-       "      <td>large_spam</td>\n",
-       "      <td>0.203485</td>\n",
-       "      <td>0.780861</td>\n",
-       "      <td>0.225911</td>\n",
-       "      <td>6.248698</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                     name     experiment components          value quality  \\\n",
-       "4a949ae1  ProcessFidelity  LayerFidelity   [Q7, Q6]  0.795+/-0.034     bad   \n",
-       "\n",
-       "            backend                          run_time  qubits      reason  \\\n",
-       "4a949ae1  ibm_kyoto  2024-03-21 16:29:46.643000+09:00  (7, 6)  large_spam   \n",
-       "\n",
-       "                 a     alpha         b     chisq  \n",
-       "4a949ae1  0.203485  0.780861  0.225911  6.248698  "
-      ]
-     },
-     "execution_count": 21,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "pfdf = df[(df.name == \"ProcessFidelity\")]\n",
     "pfdf[pfdf.value < 0.8]"
@@ -1065,846 +404,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": null,
    "id": "a3924a1b",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>name</th>\n",
-       "      <th>experiment</th>\n",
-       "      <th>components</th>\n",
-       "      <th>value</th>\n",
-       "      <th>quality</th>\n",
-       "      <th>backend</th>\n",
-       "      <th>run_time</th>\n",
-       "      <th>qubits</th>\n",
-       "      <th>reason</th>\n",
-       "      <th>a</th>\n",
-       "      <th>alpha</th>\n",
-       "      <th>b</th>\n",
-       "      <th>chisq</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>315b252c</th>\n",
-       "      <td>ProcessFidelity</td>\n",
-       "      <td>LayerFidelity</td>\n",
-       "      <td>[Q112, Q108]</td>\n",
-       "      <td>0.9741+/-0.0018</td>\n",
-       "      <td>bad</td>\n",
-       "      <td>ibm_kyoto</td>\n",
-       "      <td>2024-03-21 16:29:46.643000+09:00</td>\n",
-       "      <td>(112, 108)</td>\n",
-       "      <td>large_spam</td>\n",
-       "      <td>0.403517</td>\n",
-       "      <td>0.972388</td>\n",
-       "      <td>0.253707</td>\n",
-       "      <td>1.211738</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>8ba264a8</th>\n",
-       "      <td>ProcessFidelity</td>\n",
-       "      <td>LayerFidelity</td>\n",
-       "      <td>[Q106, Q107]</td>\n",
-       "      <td>0.9750+/-0.0031</td>\n",
-       "      <td>bad</td>\n",
-       "      <td>ibm_kyoto</td>\n",
-       "      <td>2024-03-21 16:29:46.643000+09:00</td>\n",
-       "      <td>(106, 107)</td>\n",
-       "      <td>large_spam</td>\n",
-       "      <td>0.192179</td>\n",
-       "      <td>0.973366</td>\n",
-       "      <td>0.212858</td>\n",
-       "      <td>2.313055</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>fd70efcf</th>\n",
-       "      <td>ProcessFidelity</td>\n",
-       "      <td>LayerFidelity</td>\n",
-       "      <td>[Q105, Q104]</td>\n",
-       "      <td>0.9999999999999999+/-nan</td>\n",
-       "      <td>bad</td>\n",
-       "      <td>ibm_kyoto</td>\n",
-       "      <td>2024-03-21 16:29:46.643000+09:00</td>\n",
-       "      <td>(105, 104)</td>\n",
-       "      <td>large_spam</td>\n",
-       "      <td>0.003853</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>0.25289</td>\n",
-       "      <td>0.97619</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>329c0c1a</th>\n",
-       "      <td>ProcessFidelity</td>\n",
-       "      <td>LayerFidelity</td>\n",
-       "      <td>[Q122, Q111]</td>\n",
-       "      <td>0.939+/-0.008</td>\n",
-       "      <td>bad</td>\n",
-       "      <td>ibm_kyoto</td>\n",
-       "      <td>2024-03-21 16:29:46.643000+09:00</td>\n",
-       "      <td>(122, 111)</td>\n",
-       "      <td>large_spam</td>\n",
-       "      <td>0.433793</td>\n",
-       "      <td>0.934506</td>\n",
-       "      <td>0.24728</td>\n",
-       "      <td>3.67749</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>b1455dcc</th>\n",
-       "      <td>ProcessFidelity</td>\n",
-       "      <td>LayerFidelity</td>\n",
-       "      <td>[Q121, Q120]</td>\n",
-       "      <td>0.9858+/-0.0005</td>\n",
-       "      <td>bad</td>\n",
-       "      <td>ibm_kyoto</td>\n",
-       "      <td>2024-03-21 16:29:46.643000+09:00</td>\n",
-       "      <td>(121, 120)</td>\n",
-       "      <td>large_chisq</td>\n",
-       "      <td>0.652881</td>\n",
-       "      <td>0.984827</td>\n",
-       "      <td>0.239197</td>\n",
-       "      <td>3.405308</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>ca0ff259</th>\n",
-       "      <td>ProcessFidelity</td>\n",
-       "      <td>LayerFidelity</td>\n",
-       "      <td>[Q119, Q118]</td>\n",
-       "      <td>0.9922+/-0.0004</td>\n",
-       "      <td>bad</td>\n",
-       "      <td>ibm_kyoto</td>\n",
-       "      <td>2024-03-21 16:29:46.643000+09:00</td>\n",
-       "      <td>(119, 118)</td>\n",
-       "      <td>large_chisq</td>\n",
-       "      <td>0.570897</td>\n",
-       "      <td>0.991682</td>\n",
-       "      <td>0.25806</td>\n",
-       "      <td>3.158789</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>156ce79e</th>\n",
-       "      <td>ProcessFidelity</td>\n",
-       "      <td>LayerFidelity</td>\n",
-       "      <td>[Q82, Q81]</td>\n",
-       "      <td>0.9786+/-0.0008</td>\n",
-       "      <td>bad</td>\n",
-       "      <td>ibm_kyoto</td>\n",
-       "      <td>2024-03-21 16:29:46.643000+09:00</td>\n",
-       "      <td>(82, 81)</td>\n",
-       "      <td>large_chisq</td>\n",
-       "      <td>0.612284</td>\n",
-       "      <td>0.977174</td>\n",
-       "      <td>0.256304</td>\n",
-       "      <td>4.146358</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>df2acdc7</th>\n",
-       "      <td>ProcessFidelity</td>\n",
-       "      <td>LayerFidelity</td>\n",
-       "      <td>[Q80, Q79]</td>\n",
-       "      <td>0.9818+/-0.0006</td>\n",
-       "      <td>bad</td>\n",
-       "      <td>ibm_kyoto</td>\n",
-       "      <td>2024-03-21 16:29:46.643000+09:00</td>\n",
-       "      <td>(80, 79)</td>\n",
-       "      <td>large_chisq</td>\n",
-       "      <td>0.652751</td>\n",
-       "      <td>0.980545</td>\n",
-       "      <td>0.253304</td>\n",
-       "      <td>3.196182</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>e40cc5a4</th>\n",
-       "      <td>ProcessFidelity</td>\n",
-       "      <td>LayerFidelity</td>\n",
-       "      <td>[Q98, Q91]</td>\n",
-       "      <td>0.9756+/-0.0011</td>\n",
-       "      <td>bad</td>\n",
-       "      <td>ibm_kyoto</td>\n",
-       "      <td>2024-03-21 16:29:46.643000+09:00</td>\n",
-       "      <td>(98, 91)</td>\n",
-       "      <td>large_chisq</td>\n",
-       "      <td>0.580621</td>\n",
-       "      <td>0.974021</td>\n",
-       "      <td>0.222527</td>\n",
-       "      <td>3.307072</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>91fa4b5e</th>\n",
-       "      <td>ProcessFidelity</td>\n",
-       "      <td>LayerFidelity</td>\n",
-       "      <td>[Q76, Q77]</td>\n",
-       "      <td>0.906+/-0.014</td>\n",
-       "      <td>bad</td>\n",
-       "      <td>ibm_kyoto</td>\n",
-       "      <td>2024-03-21 16:29:46.643000+09:00</td>\n",
-       "      <td>(76, 77)</td>\n",
-       "      <td>large_spam</td>\n",
-       "      <td>0.250542</td>\n",
-       "      <td>0.899955</td>\n",
-       "      <td>0.194955</td>\n",
-       "      <td>2.241609</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>15ed0950</th>\n",
-       "      <td>ProcessFidelity</td>\n",
-       "      <td>LayerFidelity</td>\n",
-       "      <td>[Q62, Q61]</td>\n",
-       "      <td>0.9833+/-0.0009</td>\n",
-       "      <td>bad</td>\n",
-       "      <td>ibm_kyoto</td>\n",
-       "      <td>2024-03-21 16:29:46.643000+09:00</td>\n",
-       "      <td>(62, 61)</td>\n",
-       "      <td>large_chisq</td>\n",
-       "      <td>0.585973</td>\n",
-       "      <td>0.982237</td>\n",
-       "      <td>0.245123</td>\n",
-       "      <td>3.005546</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>435599c8</th>\n",
-       "      <td>ProcessFidelity</td>\n",
-       "      <td>LayerFidelity</td>\n",
-       "      <td>[Q63, Q64]</td>\n",
-       "      <td>0.9822+/-0.0007</td>\n",
-       "      <td>bad</td>\n",
-       "      <td>ibm_kyoto</td>\n",
-       "      <td>2024-03-21 16:29:46.643000+09:00</td>\n",
-       "      <td>(63, 64)</td>\n",
-       "      <td>large_chisq</td>\n",
-       "      <td>0.669473</td>\n",
-       "      <td>0.981054</td>\n",
-       "      <td>0.247685</td>\n",
-       "      <td>4.958896</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>f0c016b0</th>\n",
-       "      <td>ProcessFidelity</td>\n",
-       "      <td>LayerFidelity</td>\n",
-       "      <td>[Q65, Q66]</td>\n",
-       "      <td>0.9910+/-0.0004</td>\n",
-       "      <td>bad</td>\n",
-       "      <td>ibm_kyoto</td>\n",
-       "      <td>2024-03-21 16:29:46.643000+09:00</td>\n",
-       "      <td>(65, 66)</td>\n",
-       "      <td>large_chisq</td>\n",
-       "      <td>0.660338</td>\n",
-       "      <td>0.990358</td>\n",
-       "      <td>0.267629</td>\n",
-       "      <td>3.898633</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>79279b21</th>\n",
-       "      <td>ProcessFidelity</td>\n",
-       "      <td>LayerFidelity</td>\n",
-       "      <td>[Q32, Q36]</td>\n",
-       "      <td>0.9889+/-0.0007</td>\n",
-       "      <td>bad</td>\n",
-       "      <td>ibm_kyoto</td>\n",
-       "      <td>2024-03-21 16:29:46.643000+09:00</td>\n",
-       "      <td>(32, 36)</td>\n",
-       "      <td>large_chisq</td>\n",
-       "      <td>0.675131</td>\n",
-       "      <td>0.9882</td>\n",
-       "      <td>0.253997</td>\n",
-       "      <td>4.471692</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>bccc0de8</th>\n",
-       "      <td>ProcessFidelity</td>\n",
-       "      <td>LayerFidelity</td>\n",
-       "      <td>[Q8, Q9]</td>\n",
-       "      <td>0.9643+/-0.0027</td>\n",
-       "      <td>bad</td>\n",
-       "      <td>ibm_kyoto</td>\n",
-       "      <td>2024-03-21 16:29:46.643000+09:00</td>\n",
-       "      <td>(8, 9)</td>\n",
-       "      <td>large_chisq</td>\n",
-       "      <td>0.548188</td>\n",
-       "      <td>0.961901</td>\n",
-       "      <td>0.257352</td>\n",
-       "      <td>4.591003</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4a949ae1</th>\n",
-       "      <td>ProcessFidelity</td>\n",
-       "      <td>LayerFidelity</td>\n",
-       "      <td>[Q7, Q6]</td>\n",
-       "      <td>0.795+/-0.034</td>\n",
-       "      <td>bad</td>\n",
-       "      <td>ibm_kyoto</td>\n",
-       "      <td>2024-03-21 16:29:46.643000+09:00</td>\n",
-       "      <td>(7, 6)</td>\n",
-       "      <td>large_spam</td>\n",
-       "      <td>0.203485</td>\n",
-       "      <td>0.780861</td>\n",
-       "      <td>0.225911</td>\n",
-       "      <td>6.248698</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2ee122bb</th>\n",
-       "      <td>ProcessFidelity</td>\n",
-       "      <td>LayerFidelity</td>\n",
-       "      <td>[Q1, Q0]</td>\n",
-       "      <td>0.9821+/-0.0007</td>\n",
-       "      <td>bad</td>\n",
-       "      <td>ibm_kyoto</td>\n",
-       "      <td>2024-03-21 16:29:46.643000+09:00</td>\n",
-       "      <td>(1, 0)</td>\n",
-       "      <td>large_spam</td>\n",
-       "      <td>0.442856</td>\n",
-       "      <td>0.9809</td>\n",
-       "      <td>0.230101</td>\n",
-       "      <td>0.208882</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>d12da5c7</th>\n",
-       "      <td>ProcessFidelity</td>\n",
-       "      <td>LayerFidelity</td>\n",
-       "      <td>[Q18, Q14]</td>\n",
-       "      <td>0.988+/-0.009</td>\n",
-       "      <td>bad</td>\n",
-       "      <td>ibm_kyoto</td>\n",
-       "      <td>2024-03-21 16:29:46.643000+09:00</td>\n",
-       "      <td>(18, 14)</td>\n",
-       "      <td>large_spam</td>\n",
-       "      <td>0.034896</td>\n",
-       "      <td>0.987474</td>\n",
-       "      <td>0.258215</td>\n",
-       "      <td>1.719684</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>81cea09a</th>\n",
-       "      <td>ProcessFidelity</td>\n",
-       "      <td>LayerFidelity</td>\n",
-       "      <td>[Q22, Q21]</td>\n",
-       "      <td>0.9720+/-0.0008</td>\n",
-       "      <td>bad</td>\n",
-       "      <td>ibm_kyoto</td>\n",
-       "      <td>2024-03-21 16:29:46.643000+09:00</td>\n",
-       "      <td>(22, 21)</td>\n",
-       "      <td>large_chisq</td>\n",
-       "      <td>0.589132</td>\n",
-       "      <td>0.970173</td>\n",
-       "      <td>0.243312</td>\n",
-       "      <td>3.683361</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>6e24abbe</th>\n",
-       "      <td>ProcessFidelity</td>\n",
-       "      <td>LayerFidelity</td>\n",
-       "      <td>[Q25, Q26]</td>\n",
-       "      <td>0.9918+/-0.0004</td>\n",
-       "      <td>bad</td>\n",
-       "      <td>ibm_kyoto</td>\n",
-       "      <td>2024-03-21 16:29:46.643000+09:00</td>\n",
-       "      <td>(25, 26)</td>\n",
-       "      <td>large_chisq</td>\n",
-       "      <td>0.61865</td>\n",
-       "      <td>0.991259</td>\n",
-       "      <td>0.259028</td>\n",
-       "      <td>3.356521</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>36f100f1</th>\n",
-       "      <td>ProcessFidelity</td>\n",
-       "      <td>LayerFidelity</td>\n",
-       "      <td>[Q37, Q38]</td>\n",
-       "      <td>0.9828+/-0.0008</td>\n",
-       "      <td>bad</td>\n",
-       "      <td>ibm_kyoto</td>\n",
-       "      <td>2024-03-21 16:29:46.643000+09:00</td>\n",
-       "      <td>(37, 38)</td>\n",
-       "      <td>large_spam</td>\n",
-       "      <td>0.432599</td>\n",
-       "      <td>0.981673</td>\n",
-       "      <td>0.21247</td>\n",
-       "      <td>1.74983</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>cd138baa</th>\n",
-       "      <td>ProcessFidelity</td>\n",
-       "      <td>LayerFidelity</td>\n",
-       "      <td>[Q107, Q108]</td>\n",
-       "      <td>0.962+/-0.004</td>\n",
-       "      <td>bad</td>\n",
-       "      <td>ibm_kyoto</td>\n",
-       "      <td>2024-03-21 16:29:46.643000+09:00</td>\n",
-       "      <td>(107, 108)</td>\n",
-       "      <td>large_spam</td>\n",
-       "      <td>0.14405</td>\n",
-       "      <td>0.95956</td>\n",
-       "      <td>0.22854</td>\n",
-       "      <td>0.819488</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2fa9a210</th>\n",
-       "      <td>ProcessFidelity</td>\n",
-       "      <td>LayerFidelity</td>\n",
-       "      <td>[Q105, Q106]</td>\n",
-       "      <td>0.9997+/-0.0029</td>\n",
-       "      <td>bad</td>\n",
-       "      <td>ibm_kyoto</td>\n",
-       "      <td>2024-03-21 16:29:46.643000+09:00</td>\n",
-       "      <td>(105, 106)</td>\n",
-       "      <td>large_spam</td>\n",
-       "      <td>0.264829</td>\n",
-       "      <td>0.999635</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>1.298543</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>e8470f6b</th>\n",
-       "      <td>ProcessFidelity</td>\n",
-       "      <td>LayerFidelity</td>\n",
-       "      <td>[Q104, Q111]</td>\n",
-       "      <td>0.9799+/-0.0019</td>\n",
-       "      <td>bad</td>\n",
-       "      <td>ibm_kyoto</td>\n",
-       "      <td>2024-03-21 16:29:46.643000+09:00</td>\n",
-       "      <td>(104, 111)</td>\n",
-       "      <td>large_chisq</td>\n",
-       "      <td>0.505645</td>\n",
-       "      <td>0.978537</td>\n",
-       "      <td>0.253888</td>\n",
-       "      <td>3.324584</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4cb99579</th>\n",
-       "      <td>ProcessFidelity</td>\n",
-       "      <td>LayerFidelity</td>\n",
-       "      <td>[Q102, Q92]</td>\n",
-       "      <td>0.9736+/-0.0008</td>\n",
-       "      <td>bad</td>\n",
-       "      <td>ibm_kyoto</td>\n",
-       "      <td>2024-03-21 16:29:46.643000+09:00</td>\n",
-       "      <td>(102, 92)</td>\n",
-       "      <td>large_chisq</td>\n",
-       "      <td>0.632969</td>\n",
-       "      <td>0.971815</td>\n",
-       "      <td>0.254668</td>\n",
-       "      <td>3.131533</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1e0f2c3f</th>\n",
-       "      <td>ProcessFidelity</td>\n",
-       "      <td>LayerFidelity</td>\n",
-       "      <td>[Q96, Q95]</td>\n",
-       "      <td>0.9834+/-0.0012</td>\n",
-       "      <td>bad</td>\n",
-       "      <td>ibm_kyoto</td>\n",
-       "      <td>2024-03-21 16:29:46.643000+09:00</td>\n",
-       "      <td>(96, 95)</td>\n",
-       "      <td>large_spam</td>\n",
-       "      <td>0.438003</td>\n",
-       "      <td>0.98226</td>\n",
-       "      <td>0.238188</td>\n",
-       "      <td>3.790113</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>b339523a</th>\n",
-       "      <td>ProcessFidelity</td>\n",
-       "      <td>LayerFidelity</td>\n",
-       "      <td>[Q94, Q90]</td>\n",
-       "      <td>0.9773+/-0.0009</td>\n",
-       "      <td>bad</td>\n",
-       "      <td>ibm_kyoto</td>\n",
-       "      <td>2024-03-21 16:29:46.643000+09:00</td>\n",
-       "      <td>(94, 90)</td>\n",
-       "      <td>large_chisq</td>\n",
-       "      <td>0.558095</td>\n",
-       "      <td>0.975839</td>\n",
-       "      <td>0.236842</td>\n",
-       "      <td>3.869758</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>de45c7fd</th>\n",
-       "      <td>ProcessFidelity</td>\n",
-       "      <td>LayerFidelity</td>\n",
-       "      <td>[Q76, Q75]</td>\n",
-       "      <td>0.960+/-0.004</td>\n",
-       "      <td>bad</td>\n",
-       "      <td>ibm_kyoto</td>\n",
-       "      <td>2024-03-21 16:29:46.643000+09:00</td>\n",
-       "      <td>(76, 75)</td>\n",
-       "      <td>large_spam</td>\n",
-       "      <td>0.422947</td>\n",
-       "      <td>0.956955</td>\n",
-       "      <td>0.238076</td>\n",
-       "      <td>3.385566</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>517c46b6</th>\n",
-       "      <td>ProcessFidelity</td>\n",
-       "      <td>LayerFidelity</td>\n",
-       "      <td>[Q77, Q71]</td>\n",
-       "      <td>0.9769+/-0.0018</td>\n",
-       "      <td>bad</td>\n",
-       "      <td>ibm_kyoto</td>\n",
-       "      <td>2024-03-21 16:29:46.643000+09:00</td>\n",
-       "      <td>(77, 71)</td>\n",
-       "      <td>large_spam</td>\n",
-       "      <td>0.432125</td>\n",
-       "      <td>0.975324</td>\n",
-       "      <td>0.234107</td>\n",
-       "      <td>2.477181</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>580222cf</th>\n",
-       "      <td>ProcessFidelity</td>\n",
-       "      <td>LayerFidelity</td>\n",
-       "      <td>[Q73, Q66]</td>\n",
-       "      <td>0.9900+/-0.0004</td>\n",
-       "      <td>bad</td>\n",
-       "      <td>ibm_kyoto</td>\n",
-       "      <td>2024-03-21 16:29:46.643000+09:00</td>\n",
-       "      <td>(73, 66)</td>\n",
-       "      <td>large_chisq</td>\n",
-       "      <td>0.649445</td>\n",
-       "      <td>0.989288</td>\n",
-       "      <td>0.264381</td>\n",
-       "      <td>3.445938</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>f24d523d</th>\n",
-       "      <td>ProcessFidelity</td>\n",
-       "      <td>LayerFidelity</td>\n",
-       "      <td>[Q88, Q87]</td>\n",
-       "      <td>0.9865+/-0.0006</td>\n",
-       "      <td>bad</td>\n",
-       "      <td>ibm_kyoto</td>\n",
-       "      <td>2024-03-21 16:29:46.643000+09:00</td>\n",
-       "      <td>(88, 87)</td>\n",
-       "      <td>large_chisq</td>\n",
-       "      <td>0.623668</td>\n",
-       "      <td>0.985601</td>\n",
-       "      <td>0.236159</td>\n",
-       "      <td>3.406165</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>635751a0</th>\n",
-       "      <td>ProcessFidelity</td>\n",
-       "      <td>LayerFidelity</td>\n",
-       "      <td>[Q11, Q12]</td>\n",
-       "      <td>0.9887+/-0.0005</td>\n",
-       "      <td>bad</td>\n",
-       "      <td>ibm_kyoto</td>\n",
-       "      <td>2024-03-21 16:29:46.643000+09:00</td>\n",
-       "      <td>(11, 12)</td>\n",
-       "      <td>large_chisq</td>\n",
-       "      <td>0.573717</td>\n",
-       "      <td>0.987961</td>\n",
-       "      <td>0.25327</td>\n",
-       "      <td>3.093085</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2df324aa</th>\n",
-       "      <td>ProcessFidelity</td>\n",
-       "      <td>LayerFidelity</td>\n",
-       "      <td>[Q9, Q10]</td>\n",
-       "      <td>0.9739+/-0.0012</td>\n",
-       "      <td>bad</td>\n",
-       "      <td>ibm_kyoto</td>\n",
-       "      <td>2024-03-21 16:29:46.643000+09:00</td>\n",
-       "      <td>(9, 10)</td>\n",
-       "      <td>large_chisq</td>\n",
-       "      <td>0.591139</td>\n",
-       "      <td>0.972162</td>\n",
-       "      <td>0.245885</td>\n",
-       "      <td>3.872122</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>d727e840</th>\n",
-       "      <td>ProcessFidelity</td>\n",
-       "      <td>LayerFidelity</td>\n",
-       "      <td>[Q8, Q7]</td>\n",
-       "      <td>0.835+/-0.016</td>\n",
-       "      <td>bad</td>\n",
-       "      <td>ibm_kyoto</td>\n",
-       "      <td>2024-03-21 16:29:46.643000+09:00</td>\n",
-       "      <td>(8, 7)</td>\n",
-       "      <td>large_spam</td>\n",
-       "      <td>0.421773</td>\n",
-       "      <td>0.824446</td>\n",
-       "      <td>0.252432</td>\n",
-       "      <td>0.985535</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>24c43177</th>\n",
-       "      <td>ProcessFidelity</td>\n",
-       "      <td>LayerFidelity</td>\n",
-       "      <td>[Q0, Q14]</td>\n",
-       "      <td>0.9755+/-0.0008</td>\n",
-       "      <td>bad</td>\n",
-       "      <td>ibm_kyoto</td>\n",
-       "      <td>2024-03-21 16:29:46.643000+09:00</td>\n",
-       "      <td>(0, 14)</td>\n",
-       "      <td>large_spam</td>\n",
-       "      <td>0.472265</td>\n",
-       "      <td>0.973889</td>\n",
-       "      <td>0.213734</td>\n",
-       "      <td>3.678971</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>c5d235ae</th>\n",
-       "      <td>ProcessFidelity</td>\n",
-       "      <td>LayerFidelity</td>\n",
-       "      <td>[Q18, Q19]</td>\n",
-       "      <td>1.000+/-0.031</td>\n",
-       "      <td>bad</td>\n",
-       "      <td>ibm_kyoto</td>\n",
-       "      <td>2024-03-21 16:29:46.643000+09:00</td>\n",
-       "      <td>(18, 19)</td>\n",
-       "      <td>large_spam</td>\n",
-       "      <td>0.270738</td>\n",
-       "      <td>0.999941</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.686612</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>57496351</th>\n",
-       "      <td>ProcessFidelity</td>\n",
-       "      <td>LayerFidelity</td>\n",
-       "      <td>[Q22, Q23]</td>\n",
-       "      <td>0.9693+/-0.0010</td>\n",
-       "      <td>bad</td>\n",
-       "      <td>ibm_kyoto</td>\n",
-       "      <td>2024-03-21 16:29:46.643000+09:00</td>\n",
-       "      <td>(22, 23)</td>\n",
-       "      <td>large_chisq</td>\n",
-       "      <td>0.619221</td>\n",
-       "      <td>0.967299</td>\n",
-       "      <td>0.248939</td>\n",
-       "      <td>3.046559</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>b9ecf0db</th>\n",
-       "      <td>ProcessFidelity</td>\n",
-       "      <td>LayerFidelity</td>\n",
-       "      <td>[Q43, Q42]</td>\n",
-       "      <td>0.9770+/-0.0011</td>\n",
-       "      <td>bad</td>\n",
-       "      <td>ibm_kyoto</td>\n",
-       "      <td>2024-03-21 16:29:46.643000+09:00</td>\n",
-       "      <td>(43, 42)</td>\n",
-       "      <td>large_chisq</td>\n",
-       "      <td>0.583279</td>\n",
-       "      <td>0.975414</td>\n",
-       "      <td>0.256407</td>\n",
-       "      <td>4.525672</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>dda89160</th>\n",
-       "      <td>ProcessFidelity</td>\n",
-       "      <td>LayerFidelity</td>\n",
-       "      <td>[Q52, Q37]</td>\n",
-       "      <td>0.9784+/-0.0009</td>\n",
-       "      <td>bad</td>\n",
-       "      <td>ibm_kyoto</td>\n",
-       "      <td>2024-03-21 16:29:46.643000+09:00</td>\n",
-       "      <td>(52, 37)</td>\n",
-       "      <td>large_spam</td>\n",
-       "      <td>0.436053</td>\n",
-       "      <td>0.976985</td>\n",
-       "      <td>0.187152</td>\n",
-       "      <td>0.723898</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                     name     experiment    components  \\\n",
-       "315b252c  ProcessFidelity  LayerFidelity  [Q112, Q108]   \n",
-       "8ba264a8  ProcessFidelity  LayerFidelity  [Q106, Q107]   \n",
-       "fd70efcf  ProcessFidelity  LayerFidelity  [Q105, Q104]   \n",
-       "329c0c1a  ProcessFidelity  LayerFidelity  [Q122, Q111]   \n",
-       "b1455dcc  ProcessFidelity  LayerFidelity  [Q121, Q120]   \n",
-       "ca0ff259  ProcessFidelity  LayerFidelity  [Q119, Q118]   \n",
-       "156ce79e  ProcessFidelity  LayerFidelity    [Q82, Q81]   \n",
-       "df2acdc7  ProcessFidelity  LayerFidelity    [Q80, Q79]   \n",
-       "e40cc5a4  ProcessFidelity  LayerFidelity    [Q98, Q91]   \n",
-       "91fa4b5e  ProcessFidelity  LayerFidelity    [Q76, Q77]   \n",
-       "15ed0950  ProcessFidelity  LayerFidelity    [Q62, Q61]   \n",
-       "435599c8  ProcessFidelity  LayerFidelity    [Q63, Q64]   \n",
-       "f0c016b0  ProcessFidelity  LayerFidelity    [Q65, Q66]   \n",
-       "79279b21  ProcessFidelity  LayerFidelity    [Q32, Q36]   \n",
-       "bccc0de8  ProcessFidelity  LayerFidelity      [Q8, Q9]   \n",
-       "4a949ae1  ProcessFidelity  LayerFidelity      [Q7, Q6]   \n",
-       "2ee122bb  ProcessFidelity  LayerFidelity      [Q1, Q0]   \n",
-       "d12da5c7  ProcessFidelity  LayerFidelity    [Q18, Q14]   \n",
-       "81cea09a  ProcessFidelity  LayerFidelity    [Q22, Q21]   \n",
-       "6e24abbe  ProcessFidelity  LayerFidelity    [Q25, Q26]   \n",
-       "36f100f1  ProcessFidelity  LayerFidelity    [Q37, Q38]   \n",
-       "cd138baa  ProcessFidelity  LayerFidelity  [Q107, Q108]   \n",
-       "2fa9a210  ProcessFidelity  LayerFidelity  [Q105, Q106]   \n",
-       "e8470f6b  ProcessFidelity  LayerFidelity  [Q104, Q111]   \n",
-       "4cb99579  ProcessFidelity  LayerFidelity   [Q102, Q92]   \n",
-       "1e0f2c3f  ProcessFidelity  LayerFidelity    [Q96, Q95]   \n",
-       "b339523a  ProcessFidelity  LayerFidelity    [Q94, Q90]   \n",
-       "de45c7fd  ProcessFidelity  LayerFidelity    [Q76, Q75]   \n",
-       "517c46b6  ProcessFidelity  LayerFidelity    [Q77, Q71]   \n",
-       "580222cf  ProcessFidelity  LayerFidelity    [Q73, Q66]   \n",
-       "f24d523d  ProcessFidelity  LayerFidelity    [Q88, Q87]   \n",
-       "635751a0  ProcessFidelity  LayerFidelity    [Q11, Q12]   \n",
-       "2df324aa  ProcessFidelity  LayerFidelity     [Q9, Q10]   \n",
-       "d727e840  ProcessFidelity  LayerFidelity      [Q8, Q7]   \n",
-       "24c43177  ProcessFidelity  LayerFidelity     [Q0, Q14]   \n",
-       "c5d235ae  ProcessFidelity  LayerFidelity    [Q18, Q19]   \n",
-       "57496351  ProcessFidelity  LayerFidelity    [Q22, Q23]   \n",
-       "b9ecf0db  ProcessFidelity  LayerFidelity    [Q43, Q42]   \n",
-       "dda89160  ProcessFidelity  LayerFidelity    [Q52, Q37]   \n",
-       "\n",
-       "                             value quality    backend  \\\n",
-       "315b252c           0.9741+/-0.0018     bad  ibm_kyoto   \n",
-       "8ba264a8           0.9750+/-0.0031     bad  ibm_kyoto   \n",
-       "fd70efcf  0.9999999999999999+/-nan     bad  ibm_kyoto   \n",
-       "329c0c1a             0.939+/-0.008     bad  ibm_kyoto   \n",
-       "b1455dcc           0.9858+/-0.0005     bad  ibm_kyoto   \n",
-       "ca0ff259           0.9922+/-0.0004     bad  ibm_kyoto   \n",
-       "156ce79e           0.9786+/-0.0008     bad  ibm_kyoto   \n",
-       "df2acdc7           0.9818+/-0.0006     bad  ibm_kyoto   \n",
-       "e40cc5a4           0.9756+/-0.0011     bad  ibm_kyoto   \n",
-       "91fa4b5e             0.906+/-0.014     bad  ibm_kyoto   \n",
-       "15ed0950           0.9833+/-0.0009     bad  ibm_kyoto   \n",
-       "435599c8           0.9822+/-0.0007     bad  ibm_kyoto   \n",
-       "f0c016b0           0.9910+/-0.0004     bad  ibm_kyoto   \n",
-       "79279b21           0.9889+/-0.0007     bad  ibm_kyoto   \n",
-       "bccc0de8           0.9643+/-0.0027     bad  ibm_kyoto   \n",
-       "4a949ae1             0.795+/-0.034     bad  ibm_kyoto   \n",
-       "2ee122bb           0.9821+/-0.0007     bad  ibm_kyoto   \n",
-       "d12da5c7             0.988+/-0.009     bad  ibm_kyoto   \n",
-       "81cea09a           0.9720+/-0.0008     bad  ibm_kyoto   \n",
-       "6e24abbe           0.9918+/-0.0004     bad  ibm_kyoto   \n",
-       "36f100f1           0.9828+/-0.0008     bad  ibm_kyoto   \n",
-       "cd138baa             0.962+/-0.004     bad  ibm_kyoto   \n",
-       "2fa9a210           0.9997+/-0.0029     bad  ibm_kyoto   \n",
-       "e8470f6b           0.9799+/-0.0019     bad  ibm_kyoto   \n",
-       "4cb99579           0.9736+/-0.0008     bad  ibm_kyoto   \n",
-       "1e0f2c3f           0.9834+/-0.0012     bad  ibm_kyoto   \n",
-       "b339523a           0.9773+/-0.0009     bad  ibm_kyoto   \n",
-       "de45c7fd             0.960+/-0.004     bad  ibm_kyoto   \n",
-       "517c46b6           0.9769+/-0.0018     bad  ibm_kyoto   \n",
-       "580222cf           0.9900+/-0.0004     bad  ibm_kyoto   \n",
-       "f24d523d           0.9865+/-0.0006     bad  ibm_kyoto   \n",
-       "635751a0           0.9887+/-0.0005     bad  ibm_kyoto   \n",
-       "2df324aa           0.9739+/-0.0012     bad  ibm_kyoto   \n",
-       "d727e840             0.835+/-0.016     bad  ibm_kyoto   \n",
-       "24c43177           0.9755+/-0.0008     bad  ibm_kyoto   \n",
-       "c5d235ae             1.000+/-0.031     bad  ibm_kyoto   \n",
-       "57496351           0.9693+/-0.0010     bad  ibm_kyoto   \n",
-       "b9ecf0db           0.9770+/-0.0011     bad  ibm_kyoto   \n",
-       "dda89160           0.9784+/-0.0009     bad  ibm_kyoto   \n",
-       "\n",
-       "                                  run_time      qubits       reason         a  \\\n",
-       "315b252c  2024-03-21 16:29:46.643000+09:00  (112, 108)   large_spam  0.403517   \n",
-       "8ba264a8  2024-03-21 16:29:46.643000+09:00  (106, 107)   large_spam  0.192179   \n",
-       "fd70efcf  2024-03-21 16:29:46.643000+09:00  (105, 104)   large_spam  0.003853   \n",
-       "329c0c1a  2024-03-21 16:29:46.643000+09:00  (122, 111)   large_spam  0.433793   \n",
-       "b1455dcc  2024-03-21 16:29:46.643000+09:00  (121, 120)  large_chisq  0.652881   \n",
-       "ca0ff259  2024-03-21 16:29:46.643000+09:00  (119, 118)  large_chisq  0.570897   \n",
-       "156ce79e  2024-03-21 16:29:46.643000+09:00    (82, 81)  large_chisq  0.612284   \n",
-       "df2acdc7  2024-03-21 16:29:46.643000+09:00    (80, 79)  large_chisq  0.652751   \n",
-       "e40cc5a4  2024-03-21 16:29:46.643000+09:00    (98, 91)  large_chisq  0.580621   \n",
-       "91fa4b5e  2024-03-21 16:29:46.643000+09:00    (76, 77)   large_spam  0.250542   \n",
-       "15ed0950  2024-03-21 16:29:46.643000+09:00    (62, 61)  large_chisq  0.585973   \n",
-       "435599c8  2024-03-21 16:29:46.643000+09:00    (63, 64)  large_chisq  0.669473   \n",
-       "f0c016b0  2024-03-21 16:29:46.643000+09:00    (65, 66)  large_chisq  0.660338   \n",
-       "79279b21  2024-03-21 16:29:46.643000+09:00    (32, 36)  large_chisq  0.675131   \n",
-       "bccc0de8  2024-03-21 16:29:46.643000+09:00      (8, 9)  large_chisq  0.548188   \n",
-       "4a949ae1  2024-03-21 16:29:46.643000+09:00      (7, 6)   large_spam  0.203485   \n",
-       "2ee122bb  2024-03-21 16:29:46.643000+09:00      (1, 0)   large_spam  0.442856   \n",
-       "d12da5c7  2024-03-21 16:29:46.643000+09:00    (18, 14)   large_spam  0.034896   \n",
-       "81cea09a  2024-03-21 16:29:46.643000+09:00    (22, 21)  large_chisq  0.589132   \n",
-       "6e24abbe  2024-03-21 16:29:46.643000+09:00    (25, 26)  large_chisq   0.61865   \n",
-       "36f100f1  2024-03-21 16:29:46.643000+09:00    (37, 38)   large_spam  0.432599   \n",
-       "cd138baa  2024-03-21 16:29:46.643000+09:00  (107, 108)   large_spam   0.14405   \n",
-       "2fa9a210  2024-03-21 16:29:46.643000+09:00  (105, 106)   large_spam  0.264829   \n",
-       "e8470f6b  2024-03-21 16:29:46.643000+09:00  (104, 111)  large_chisq  0.505645   \n",
-       "4cb99579  2024-03-21 16:29:46.643000+09:00   (102, 92)  large_chisq  0.632969   \n",
-       "1e0f2c3f  2024-03-21 16:29:46.643000+09:00    (96, 95)   large_spam  0.438003   \n",
-       "b339523a  2024-03-21 16:29:46.643000+09:00    (94, 90)  large_chisq  0.558095   \n",
-       "de45c7fd  2024-03-21 16:29:46.643000+09:00    (76, 75)   large_spam  0.422947   \n",
-       "517c46b6  2024-03-21 16:29:46.643000+09:00    (77, 71)   large_spam  0.432125   \n",
-       "580222cf  2024-03-21 16:29:46.643000+09:00    (73, 66)  large_chisq  0.649445   \n",
-       "f24d523d  2024-03-21 16:29:46.643000+09:00    (88, 87)  large_chisq  0.623668   \n",
-       "635751a0  2024-03-21 16:29:46.643000+09:00    (11, 12)  large_chisq  0.573717   \n",
-       "2df324aa  2024-03-21 16:29:46.643000+09:00     (9, 10)  large_chisq  0.591139   \n",
-       "d727e840  2024-03-21 16:29:46.643000+09:00      (8, 7)   large_spam  0.421773   \n",
-       "24c43177  2024-03-21 16:29:46.643000+09:00     (0, 14)   large_spam  0.472265   \n",
-       "c5d235ae  2024-03-21 16:29:46.643000+09:00    (18, 19)   large_spam  0.270738   \n",
-       "57496351  2024-03-21 16:29:46.643000+09:00    (22, 23)  large_chisq  0.619221   \n",
-       "b9ecf0db  2024-03-21 16:29:46.643000+09:00    (43, 42)  large_chisq  0.583279   \n",
-       "dda89160  2024-03-21 16:29:46.643000+09:00    (52, 37)   large_spam  0.436053   \n",
-       "\n",
-       "             alpha         b     chisq  \n",
-       "315b252c  0.972388  0.253707  1.211738  \n",
-       "8ba264a8  0.973366  0.212858  2.313055  \n",
-       "fd70efcf       1.0   0.25289   0.97619  \n",
-       "329c0c1a  0.934506   0.24728   3.67749  \n",
-       "b1455dcc  0.984827  0.239197  3.405308  \n",
-       "ca0ff259  0.991682   0.25806  3.158789  \n",
-       "156ce79e  0.977174  0.256304  4.146358  \n",
-       "df2acdc7  0.980545  0.253304  3.196182  \n",
-       "e40cc5a4  0.974021  0.222527  3.307072  \n",
-       "91fa4b5e  0.899955  0.194955  2.241609  \n",
-       "15ed0950  0.982237  0.245123  3.005546  \n",
-       "435599c8  0.981054  0.247685  4.958896  \n",
-       "f0c016b0  0.990358  0.267629  3.898633  \n",
-       "79279b21    0.9882  0.253997  4.471692  \n",
-       "bccc0de8  0.961901  0.257352  4.591003  \n",
-       "4a949ae1  0.780861  0.225911  6.248698  \n",
-       "2ee122bb    0.9809  0.230101  0.208882  \n",
-       "d12da5c7  0.987474  0.258215  1.719684  \n",
-       "81cea09a  0.970173  0.243312  3.683361  \n",
-       "6e24abbe  0.991259  0.259028  3.356521  \n",
-       "36f100f1  0.981673   0.21247   1.74983  \n",
-       "cd138baa   0.95956   0.22854  0.819488  \n",
-       "2fa9a210  0.999635       0.0  1.298543  \n",
-       "e8470f6b  0.978537  0.253888  3.324584  \n",
-       "4cb99579  0.971815  0.254668  3.131533  \n",
-       "1e0f2c3f   0.98226  0.238188  3.790113  \n",
-       "b339523a  0.975839  0.236842  3.869758  \n",
-       "de45c7fd  0.956955  0.238076  3.385566  \n",
-       "517c46b6  0.975324  0.234107  2.477181  \n",
-       "580222cf  0.989288  0.264381  3.445938  \n",
-       "f24d523d  0.985601  0.236159  3.406165  \n",
-       "635751a0  0.987961   0.25327  3.093085  \n",
-       "2df324aa  0.972162  0.245885  3.872122  \n",
-       "d727e840  0.824446  0.252432  0.985535  \n",
-       "24c43177  0.973889  0.213734  3.678971  \n",
-       "c5d235ae  0.999941       0.0  0.686612  \n",
-       "57496351  0.967299  0.248939  3.046559  \n",
-       "b9ecf0db  0.975414  0.256407  4.525672  \n",
-       "dda89160  0.976985  0.187152  0.723898  "
-      ]
-     },
-     "execution_count": 22,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "# find bad quality analysis results\n",
     "pfdf[pfdf.quality==\"bad\"]"
@@ -1912,21 +415,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": null,
    "id": "4170c823",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "datetime.datetime(2024, 3, 21, 16, 29, 46, 643000, tzinfo=tzlocal())"
-      ]
-     },
-     "execution_count": 23,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "RUN_TIME = df[(df.name == \"LF\")].run_time.values[0]\n",
     "RUN_TIME"
@@ -1942,7 +434,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": null,
    "id": "530a366a",
    "metadata": {},
    "outputs": [],
@@ -1953,21 +445,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": null,
    "id": "202188f7",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "101"
-      ]
-     },
-     "execution_count": 25,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "# Compute LF by chain length assuming the first layer is full with 2q-gates\n",
     "lf_sets, lf_qubits = two_disjoint_layers, best_qubit_chain\n",
@@ -1980,7 +461,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": null,
    "id": "371d7436",
    "metadata": {},
    "outputs": [],
@@ -1990,41 +471,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": null,
    "id": "80d4c824",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([0.92055217, 0.9741141 , 0.96208789, 0.97503078, 0.99965795,\n",
-       "       1.        , 0.97987882, 0.93859898, 0.99203894, 0.98577528,\n",
-       "       0.99035144, 0.99220233, 0.98818172, 0.98517995, 0.96736989,\n",
-       "       0.9790599 , 0.97357661, 0.97056358, 0.98155472, 0.97860038,\n",
-       "       0.9742577 , 0.98176111, 0.97835567, 0.97564462, 0.9652965 ,\n",
-       "       0.9890947 , 0.98336868, 0.99009675, 0.97734924, 0.93316685,\n",
-       "       0.95964551, 0.90620797, 0.97686608, 0.97599839, 0.97652024,\n",
-       "       0.9850133 , 0.98775448, 0.98334683, 0.98172138, 0.98223841,\n",
-       "       0.98999861, 0.99096077, 0.98995754, 0.99090252, 0.99226826,\n",
-       "       0.99107934, 0.98650061, 0.97199183, 0.98736921, 0.97831851,\n",
-       "       0.98856905, 0.98560185, 0.98954903, 0.98241734, 0.97179765,\n",
-       "       0.97997059, 0.96498726, 0.98893729, 0.98683034, 0.95981566,\n",
-       "       0.98999975, 0.98376994, 0.9887138 , 0.98631117, 0.9739017 ,\n",
-       "       0.96428246, 0.83541798, 0.7945571 , 0.99311771, 0.98481061,\n",
-       "       0.98572814, 0.97602161, 0.9794883 , 0.98209418, 0.97552063,\n",
-       "       0.98825718, 0.99994424, 0.99167623, 0.98821901, 0.97203675,\n",
-       "       0.96934318, 0.98762032, 0.98735948, 0.99180551, 0.98789597,\n",
-       "       0.98846725, 0.98315516, 0.98103707, 0.99263718, 0.98973811,\n",
-       "       0.9851167 , 0.99009538, 0.97695021, 0.98737435, 0.92521576,\n",
-       "       0.98922638, 0.92659756, 0.98281836, 0.97842359, 0.9859088 ,\n",
-       "       0.94850497])"
-      ]
-     },
-     "execution_count": 27,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "pfs = [pfdf.loc[pfdf[pfdf.qubits==qubits].index[0], 'value'] for qubits in full_layer]\n",
     "pfs = list(map(lambda x: x.n if x != 0 else 0, pfs))\n",
@@ -2035,28 +485,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": null,
    "id": "330f6c75",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "101"
-      ]
-     },
-     "execution_count": 28,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "len(pfs)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": null,
    "id": "3eb0afe8",
    "metadata": {},
    "outputs": [],
@@ -2074,23 +513,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": null,
    "id": "c02610b6",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "# Plot LF by chain length\n",
     "plt.title(f\"Backend: {backend.name}, {RUN_TIME.strftime('%Y/%m/%d %H:%M')}\")\n",
@@ -2110,23 +536,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": null,
    "id": "74603316",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "# Plot EPLG by chain length\n",
     "num_2q_gates = [length -1 for length in chain_lens]\n",

From 1767c8d66dfdff4b2d0e9253a4e24eb179f6bee8 Mon Sep 17 00:00:00 2001
From: Toshinari Itoko <itoko@jp.ibm.com>
Date: Fri, 19 Apr 2024 14:26:21 +0900
Subject: [PATCH 4/9] Remove commented out cells

---
 notebooks/layer_fidelity.ipynb | 43 ----------------------------------
 1 file changed, 43 deletions(-)

diff --git a/notebooks/layer_fidelity.ipynb b/notebooks/layer_fidelity.ipynb
index 73d29db..93b4caf 100644
--- a/notebooks/layer_fidelity.ipynb
+++ b/notebooks/layer_fidelity.ipynb
@@ -558,49 +558,6 @@
     "plt.show()"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "id": "062ba49f",
-   "metadata": {},
-   "source": [
-    "### Optional: Load Data from Results DB and rerun analysis"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "dd07a015",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# %%time\n",
-    "# # load results from DB with experiment_id\n",
-    "# from qiskit_experiments.framework import ExperimentData\n",
-    "# experiment_id = \"368f151b-63e9-49db-a36e-66a365948644\"  # REPLACE WITH YOUR OWN EXPERIMENT ID\n",
-    "# exp_data = ExperimentData.load(experiment_id, provider=provider)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "3d07c745",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# %%time\n",
-    "# # rerun analysis\n",
-    "# new_exp = LayerFidelity(\n",
-    "#     physical_qubits=exp_data.metadata[\"physical_qubits\"],\n",
-    "#     two_qubit_layers=[[tuple(qpair) for qpair in layer] for layer in exp_data.metadata[\"two_qubit_layers\"]],\n",
-    "#     lengths=[2, 4, 8, 16, 30, 50, 70, 100, 150, 200, 300, 500],\n",
-    "#     backend=backend,\n",
-    "#     num_samples=12,\n",
-    "#     seed=42,\n",
-    "# )\n",
-    "# new_exp.analysis.run(exp_data, replace_results=True)\n",
-    "# exp_data.block_for_results()"
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": null,

From d42b35704d338ddebc46553e64afafd21dbd63b5 Mon Sep 17 00:00:00 2001
From: Toshinari Itoko <itoko@jp.ibm.com>
Date: Fri, 19 Apr 2024 14:26:55 +0900
Subject: [PATCH 5/9] Set max_circuit option to avoid Internal Server Error

---
 notebooks/layer_fidelity.ipynb | 11 +++++++++++
 1 file changed, 11 insertions(+)

diff --git a/notebooks/layer_fidelity.ipynb b/notebooks/layer_fidelity.ipynb
index 93b4caf..2b2d196 100644
--- a/notebooks/layer_fidelity.ipynb
+++ b/notebooks/layer_fidelity.ipynb
@@ -200,6 +200,17 @@
     ")"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "bc9afcb9",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# set maximum number of circuits per job to avoid errors due to too large payload\n",
+    "lfexp.experiment_options.max_circuits = 144"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,

From 7531adc81bac5a31acd806316a5ace1e6d852f99 Mon Sep 17 00:00:00 2001
From: Toshinari Itoko <itoko@jp.ibm.com>
Date: Fri, 26 Apr 2024 17:31:43 +0900
Subject: [PATCH 6/9] Fix bugs in the best qubit chain selection

---
 notebooks/layer_fidelity.ipynb | 50 ++++++++++++++++++++--------------
 1 file changed, 30 insertions(+), 20 deletions(-)

diff --git a/notebooks/layer_fidelity.ipynb b/notebooks/layer_fidelity.ipynb
index 2b2d196..702bf05 100644
--- a/notebooks/layer_fidelity.ipynb
+++ b/notebooks/layer_fidelity.ipynb
@@ -103,31 +103,43 @@
     "        prev_node = node\n",
     "    return edges\n",
     "    \n",
-    "def path_cost(path, correct_by_duration=True):\n",
-    "    \"\"\"Compute cost of a path. The path is given in node indices form, e.g. [0, 1, 2].\n",
-    "    Use `to_edges` function to obtain edge list, e.g. [(0, 1), (1, 2)].\"\"\"\n",
-    "    max_duration = max(backend.target[twoq_gate][qs].duration for qs in to_edges(path))\n",
+    "def path_fidelity(path, correct_by_duration: bool=True, readout_scale: float=None):\n",
+    "    \"\"\"Compute an estimate of the total fidelity of 2-qubit gates on a path.\n",
+    "    If `correct_by_duration` is true, each gate fidelity is worsen by\n",
+    "    scale = max_duration / duration, i.e. gate_fidelity^scale.\n",
+    "    If `readout_scale` > 0 is supplied, readout_fidelity^(1/readout_scale)\n",
+    "    for each qubit on the path is multiplied to the total fielity.\n",
+    "    The path is given in node indices form, e.g. [0, 1, 2].\n",
+    "    An external function `to_edges` is used to obtain edge list, e.g. [(0, 1), (1, 2)].\"\"\"\n",
+    "    path_edges = to_edges(path)\n",
+    "    max_duration = max(backend.target[twoq_gate][qs].duration for qs in path_edges)\n",
     "    \n",
-    "    def gate_error(qpair):\n",
+    "    def gate_fidelity(qpair):\n",
     "        duration = backend.target[twoq_gate][qpair].duration\n",
-    "        factor = max_duration / duration if correct_by_duration else 1.0\n",
-    "        return factor * backend.target[twoq_gate][qpair].error\n",
+    "        scale = max_duration / duration if correct_by_duration else 1.0\n",
+    "        # 1.25 = (d+1)/d) with d = 4\n",
+    "        return max(0.25, 1 - (1.25 * backend.target[twoq_gate][qpair].error)) ** scale\n",
     "    \n",
-    "    gate_cost = -np.prod([1-0.8*gate_error(qs) for qs in to_edges(path)])\n",
-    "    readout_cost = 0\n",
-    "    return gate_cost + readout_cost\n",
+    "    def readout_fidelity(qubit):\n",
+    "        return max(0.25, 1 - backend.target[\"measure\"][qubit].error)\n",
+    "  \n",
+    "    total_fidelity = np.prod([gate_fidelity(qs) for qs in path_edges])\n",
+    "    if readout_scale:\n",
+    "        total_fidelity *= np.prod([readout_fidelity(q) for q in path]) ** (1/readout_scale)\n",
+    "    return total_fidelity\n",
     "\n",
-    "def flatten(paths):\n",
-    "    return [val for ps in paths.values() for vals in ps.values() for val in vals]\n",
+    "def flatten(paths, cutoff=None):  # cutoff not to make run time too large\n",
+    "    return [val for s, ps in paths.items() for t, vals in ps.items() for val in vals[:cutoff] if s < t]\n",
     "\n",
     "paths = rx.all_pairs_all_simple_paths(G.to_undirected(multigraph=False), min_depth=num_qubits_in_chain, cutoff=num_qubits_in_chain)\n",
-    "if not flatten(paths):\n",
+    "paths = flatten(paths, cutoff=400)\n",
+    "if not paths:\n",
     "    raise Exception(f\"No qubit chain with length={num_qubits_in_chain} exists in {backend.name}. Try smaller num_qubits_in_chain.\")\n",
     "\n",
-    "print(f\"Selecting the best from {len(flatten(paths))} candidate paths (will take a few minutes)\")\n",
-    "best_qubit_chain =  min(flatten(paths), key=path_cost)\n",
+    "print(f\"Selecting the best from {len(paths)} candidate paths (will take a few minutes)\")\n",
+    "best_qubit_chain = max(paths, key=path_fidelity)\n",
     "assert(len(best_qubit_chain) == num_qubits_in_chain)\n",
-    "print(f\"Predicted LF from reported 2q-gate EPGs: {-path_cost(best_qubit_chain)}\")"
+    "print(f\"Predicted LF from reported 2q-gate EPGs: {path_fidelity(best_qubit_chain)}\")"
    ]
   },
   {
@@ -570,11 +582,9 @@
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "adb248bf",
+   "cell_type": "markdown",
+   "id": "d92ed6bd",
    "metadata": {},
-   "outputs": [],
    "source": []
   }
  ],

From 2481c71de979d6c677a244734895e01d4d3d5e95 Mon Sep 17 00:00:00 2001
From: Toshinari Itoko <itoko@jp.ibm.com>
Date: Fri, 26 Apr 2024 18:22:26 +0900
Subject: [PATCH 7/9] Change how to get job date

---
 notebooks/layer_fidelity.ipynb | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

diff --git a/notebooks/layer_fidelity.ipynb b/notebooks/layer_fidelity.ipynb
index 702bf05..2eb0ab5 100644
--- a/notebooks/layer_fidelity.ipynb
+++ b/notebooks/layer_fidelity.ipynb
@@ -443,8 +443,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "RUN_TIME = df[(df.name == \"LF\")].run_time.values[0]\n",
-    "RUN_TIME"
+    "job = service.job(exp_data.job_ids[0])\n",
+    "JOB_DATE = job.creation_date"
    ]
   },
   {
@@ -542,7 +542,7 @@
    "outputs": [],
    "source": [
     "# Plot LF by chain length\n",
-    "plt.title(f\"Backend: {backend.name}, {RUN_TIME.strftime('%Y/%m/%d %H:%M')}\")\n",
+    "plt.title(f\"Backend: {backend.name}, {JOB_DATE.strftime('%Y/%m/%d %H:%M')}\")\n",
     "plt.plot(\n",
     "    chain_lens,\n",
     "    chain_fids,\n",
@@ -567,7 +567,7 @@
     "# Plot EPLG by chain length\n",
     "num_2q_gates = [length -1 for length in chain_lens]\n",
     "chain_eplgs = [1 - (fid ** (1 / num_2q)) for num_2q, fid in zip(num_2q_gates, chain_fids)]\n",
-    "plt.title(f\"Backend: {backend.name}, {RUN_TIME.strftime('%Y/%m/%d %H:%M')}\")\n",
+    "plt.title(f\"Backend: {backend.name}, {JOB_DATE.strftime('%Y/%m/%d %H:%M')}\")\n",
     "plt.plot(\n",
     "    chain_lens,\n",
     "    chain_eplgs,\n",

From 0f2dc73f045435467b30532ee958b19a38a1617f Mon Sep 17 00:00:00 2001
From: Toshinari Itoko <itoko@jp.ibm.com>
Date: Tue, 30 Apr 2024 13:21:45 +0900
Subject: [PATCH 8/9] add qiskit copyright

---
 notebooks/layer_fidelity.ipynb | 71 ++++++++++++++++++++++++++++++----
 1 file changed, 64 insertions(+), 7 deletions(-)

diff --git a/notebooks/layer_fidelity.ipynb b/notebooks/layer_fidelity.ipynb
index 2eb0ab5..e1d994d 100644
--- a/notebooks/layer_fidelity.ipynb
+++ b/notebooks/layer_fidelity.ipynb
@@ -436,15 +436,24 @@
     "pfdf[pfdf.quality==\"bad\"]"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "e760ede5",
+   "metadata": {},
+   "source": [
+    "### Display \"bad\" 1Q/2Q direct RB plots"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "4170c823",
+   "id": "49f56a2d",
    "metadata": {},
    "outputs": [],
    "source": [
-    "job = service.job(exp_data.job_ids[0])\n",
-    "JOB_DATE = job.creation_date"
+    "for qubits in pfdf[pfdf.quality==\"bad\"].qubits:\n",
+    "    figname = \"DirectRB_Q\" + \"_Q\".join(map(str, qubits))\n",
+    "    display(exp_data.figure(figname))"
    ]
   },
   {
@@ -516,6 +525,17 @@
     "len(pfs)"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "3e1b2500",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "job = service.job(exp_data.job_ids[0])\n",
+    "JOB_DATE = job.creation_date"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
@@ -582,10 +602,47 @@
    ]
   },
   {
-   "cell_type": "markdown",
-   "id": "d92ed6bd",
-   "metadata": {},
-   "source": []
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "885f56a7",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div style='width: 100%; background-color:#d5d9e0;padding-left: 10px; padding-bottom: 10px; padding-right: 10px; padding-top: 5px'><p>&copy; Copyright IBM 2017, 2024.</p><p>This code is licensed under the Apache License, Version 2.0. You may<br>obtain a copy of this license in the LICENSE.txt file in the root directory<br> of this source tree or at http://www.apache.org/licenses/LICENSE-2.0.<p>Any modifications or derivative works of this code must retain this<br>copyright notice, and modified files need to carry a notice indicating<br>that they have been altered from the originals.</p></div>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import datetime\n",
+    "from IPython.display import HTML, display\n",
+    "\n",
+    "def qiskit_copyright(line=\"\", cell=None):\n",
+    "    \"\"\"IBM copyright\"\"\"\n",
+    "    now = datetime.datetime.now()\n",
+    "\n",
+    "    html = \"<div style='width: 100%; background-color:#d5d9e0;\"\n",
+    "    html += \"padding-left: 10px; padding-bottom: 10px; padding-right: 10px; padding-top: 5px'>\"\n",
+    "    html += \"<p>&copy; Copyright IBM 2017, %s.</p>\" % now.year\n",
+    "    html += \"<p>This code is licensed under the Apache License, Version 2.0. You may<br>\"\n",
+    "    html += \"obtain a copy of this license in the LICENSE.txt file in the root directory<br> \"\n",
+    "    html += \"of this source tree or at http://www.apache.org/licenses/LICENSE-2.0.\"\n",
+    "\n",
+    "    html += \"<p>Any modifications or derivative works of this code must retain this<br>\"\n",
+    "    html += \"copyright notice, and modified files need to carry a notice indicating<br>\"\n",
+    "    html += \"that they have been altered from the originals.</p>\"\n",
+    "    html += \"</div>\"\n",
+    "    return display(HTML(html))\n",
+    "\n",
+    "qiskit_copyright()"
+   ]
   }
  ],
  "metadata": {

From 263107302a411102ad716d86df7348de302f5642 Mon Sep 17 00:00:00 2001
From: Toshinari Itoko <itoko@jp.ibm.com>
Date: Tue, 30 Apr 2024 13:23:22 +0900
Subject: [PATCH 9/9] comment out lines to plot bad RB curves

---
 notebooks/layer_fidelity.ipynb | 15 ++++-----------
 1 file changed, 4 insertions(+), 11 deletions(-)

diff --git a/notebooks/layer_fidelity.ipynb b/notebooks/layer_fidelity.ipynb
index e1d994d..dcf5358 100644
--- a/notebooks/layer_fidelity.ipynb
+++ b/notebooks/layer_fidelity.ipynb
@@ -436,14 +436,6 @@
     "pfdf[pfdf.quality==\"bad\"]"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "id": "e760ede5",
-   "metadata": {},
-   "source": [
-    "### Display \"bad\" 1Q/2Q direct RB plots"
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": null,
@@ -451,9 +443,10 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "for qubits in pfdf[pfdf.quality==\"bad\"].qubits:\n",
-    "    figname = \"DirectRB_Q\" + \"_Q\".join(map(str, qubits))\n",
-    "    display(exp_data.figure(figname))"
+    "# Display \"bad\" 1Q/2Q direct RB plots\n",
+    "# for qubits in pfdf[pfdf.quality==\"bad\"].qubits:\n",
+    "#     figname = \"DirectRB_Q\" + \"_Q\".join(map(str, qubits))\n",
+    "#     display(exp_data.figure(figname))"
    ]
   },
   {