lcl4_q1-reci2.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "c0c987ec-0371-4da5-b22d-d9b6dd0e3010",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "from qiskit_ibm_runtime import QiskitRuntimeService\n",
    "\n",
    "lcl_token = \"TOKEN HERE\"\n",
    "\n",
    "service = QiskitRuntimeService(\n",
    "    channel=\"ibm_quantum\", instance=\"ibm-q/open/main\", token=lcl_token\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "b225a45b-5a20-4b3f-8ff7-2249782ef7af",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "from pyqsp.angle_sequence import QuantumSignalProcessingPhases\n",
    "from pyqsp.response import PlotQSPResponse\n",
    "from pyqsp.poly import StringPolynomial\n",
    "from numpy.polynomial import Polynomial\n",
    "\n",
    "from qiskit import QuantumCircuit\n",
    "import numpy as np\n",
    "from qiskit_ibm_runtime import Sampler, Options\n",
    "\n",
    "import matplotlib as mpl\n",
    "\n",
    "import json\n",
    "\n",
    "# installed tensorflow, seaborn, pyqsp, qiskit_ibm_runtime onto default env"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "1bcbd8b4-4932-4c4d-ab5e-f26f9674b73d",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "def get_phi(d):\n",
    "    # Note: d is degree\n",
    "    print(f\"Evaluating get_phi({d})\")\n",
    "    # Warning: Time consuming [~ 1 min] since uses more accurate tensorflow to compute phis\n",
    "    epsilon = 0.1\n",
    "    k = 15\n",
    "    b = k * k * np.log(k/epsilon)\n",
    "    print(f\"b = {b}\")\n",
    "    poly_314x_degd = StringPolynomial(f\"(0.05*(1-np.power((1-(x*x)),{b})))/(x)\", d)\n",
    "    ang_seq_314x_degd = QuantumSignalProcessingPhases(poly_314x_degd, signal_operator=\"Wx\", method=\"tf\")\n",
    "    print(f\"Finished evaluating get_phi({d})\")\n",
    "    return ang_seq_314x_degd[:,0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "b0610b4c-b10d-4482-8510-edff4f826cb9",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Evaluating get_phi(1)\n",
      "WARNING:tensorflow:You are casting an input of type complex64 to an incompatible dtype float32.  This will discard the imaginary part and may not be what you intended.\n",
      "WARNING:tensorflow:You are casting an input of type complex64 to an incompatible dtype float32.  This will discard the imaginary part and may not be what you intended.\n",
      "WARNING:tensorflow:You are casting an input of type complex64 to an incompatible dtype float32.  This will discard the imaginary part and may not be what you intended.\n",
      "WARNING:tensorflow:You are casting an input of type complex64 to an incompatible dtype float32.  This will discard the imaginary part and may not be what you intended.\n",
      "Finished evaluating get_phi(1)\n",
      "[-0.30355027  1.2672479 ]\n"
     ]
    }
   ],
   "source": [
    "print(get_phi(1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "057cf327-254a-4f35-a366-4473c3c45d1b",
   "metadata": {},
   "outputs": [],
   "source": [
    "from qiskit_ibm_runtime import Sampler, Options\n",
    "\n",
    "options = Options()\n",
    "options.resilience_level = 1\n",
    "options.optimization_level = 3\n",
    "\n",
    "# Create an Estimator object\n",
    "sampler = Sampler(backend_sim, options=options)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "99998c93-03e0-4fbb-90a3-96932aa5dac3",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6aed679d-8519-4223-93d7-ea59641378e0",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "e9707358-8bd9-4a47-9cf1-6510ee978812",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import json"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "a44c9c31-fdd8-4427-9960-fdd50b5fe98b",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<IBMBackend('ibm_brisbane')>,\n",
       " <IBMBackend('ibm_kyoto')>,\n",
       " <IBMBackend('ibm_osaka')>,\n",
       " <IBMBackend('ibmq_qasm_simulator')>,\n",
       " <IBMBackend('simulator_extended_stabilizer')>,\n",
       " <IBMBackend('simulator_mps')>,\n",
       " <IBMBackend('simulator_statevector')>,\n",
       " <IBMBackend('simulator_stabilizer')>]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "service.backends()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "f3773874-2f51-4acc-b97c-3793cbba541f",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "backend_sim = service.backend(\"ibmq_qasm_simulator\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "39d15600-5fd1-482a-b281-6a936b6be4b2",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<IBMBackend('ibm_brisbane')>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "backend = service.least_busy(simulator=False, operational=True)\n",
    "backend = service.backend(\"ibm_brisbane\")\n",
    "backend"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "7f45a9c9-e7ea-4724-a945-f65180c88096",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 1690.45x200.667 with 1 Axes>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from qiskit import QuantumCircuit\n",
    "import numpy as np\n",
    "from qiskit_ibm_runtime import Sampler, Options\n",
    "\n",
    "\n",
    "qc = QuantumCircuit(1,1)\n",
    "\n",
    "# 2x\n",
    "\n",
    "phi_3 = [0.3434281, 2.249998, 2.2590873, 1.9060472]\n",
    "phi_3.reverse()\n",
    "\n",
    "a = 0.2\n",
    "\n",
    "qc.h(0)\n",
    "\n",
    "qc.barrier(0)\n",
    "\n",
    "for i in range(len(phi_3)):\n",
    "    if(i != 0):\n",
    "        qc.rx(-2*np.arccos(a),0)\n",
    "        qc.barrier(0)\n",
    "    qc.rz(-2*phi_3[i], 0)\n",
    "    qc.barrier(0)\n",
    "\n",
    "qc.h(0)\n",
    "\n",
    "qc.barrier(0)\n",
    "\n",
    "# measure all the qubits\n",
    "qc.measure([0], [0])\n",
    "\n",
    "qc.draw(output=\"mpl\", style=\"iqp\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "a6a03d78-fcff-4788-b647-c1829e359c77",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'backend_sim' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[4], line 8\u001b[0m\n\u001b[1;32m      5\u001b[0m options\u001b[38;5;241m.\u001b[39moptimization_level \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m3\u001b[39m\n\u001b[1;32m      7\u001b[0m \u001b[38;5;66;03m# Create an Estimator object\u001b[39;00m\n\u001b[0;32m----> 8\u001b[0m sampler \u001b[38;5;241m=\u001b[39m Sampler(\u001b[43mbackend_sim\u001b[49m, options\u001b[38;5;241m=\u001b[39moptions)\n\u001b[1;32m     10\u001b[0m \u001b[38;5;66;03m# Submit the circuit to Estimator\u001b[39;00m\n\u001b[1;32m     11\u001b[0m job \u001b[38;5;241m=\u001b[39m sampler\u001b[38;5;241m.\u001b[39mrun(qc, shots\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1000\u001b[39m)\n",
      "\u001b[0;31mNameError\u001b[0m: name 'backend_sim' is not defined"
     ]
    }
   ],
   "source": [
    "from qiskit_ibm_runtime import Sampler, Options\n",
    "\n",
    "options = Options()\n",
    "options.resilience_level = 1\n",
    "options.optimization_level = 3\n",
    "\n",
    "# Create an Estimator object\n",
    "sampler = Sampler(backend_sim, options=options)\n",
    "\n",
    "# Submit the circuit to Estimator\n",
    "job = sampler.run(qc, shots=1000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "075ed36b-e5e3-4473-bed4-c28a0cc86057",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'job' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[9], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m jobid \u001b[38;5;241m=\u001b[39m \u001b[43mjob\u001b[49m\u001b[38;5;241m.\u001b[39mjob_id()\n\u001b[1;32m      2\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m>>> Job ID: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mjob\u001b[38;5;241m.\u001b[39mjob_id()\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m      3\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m>>> Job Status: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mjob\u001b[38;5;241m.\u001b[39mstatus()\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m)\n",
      "\u001b[0;31mNameError\u001b[0m: name 'job' is not defined"
     ]
    }
   ],
   "source": [
    "jobid = job.job_id()\n",
    "print(f\">>> Job ID: {job.job_id()}\")\n",
    "print(f\">>> Job Status: {job.status()}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "8c26cbc3-58d9-4345-9175-5bfea7477871",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "retrieve_job = service.job(jobid)\n",
    "result = retrieve_job.result()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "9f7e479d-84ac-4c49-bab2-6873053d2283",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SamplerResult(quasi_dists=[{0: 0.128, 1: 0.872}], metadata=[{'shots': 1000, 'circuit_metadata': {}, 'readout_mitigation_overhead': 1.0, 'readout_mitigation_time': 0.024896160699427128, 'warning': 'Optimization level clipped from 3 to 1'}])\n"
     ]
    }
   ],
   "source": [
    "from qiskit.visualization import plot_distribution\n",
    "\n",
    "plot_distribution(result.quasi_dists[0].binary_probabilities())\n",
    "print(result)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "24ba049e-64eb-4848-a2d3-3e8152e088a3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.128\n"
     ]
    }
   ],
   "source": [
    "print(result.quasi_dists[0][0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "a1b9ea7a-809d-477e-b11e-98142a63d1dc",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-1.0\n",
      "-0.9\n",
      "-0.8\n",
      "-0.7\n",
      "-0.6\n",
      "-0.5\n",
      "-0.3999999999999999\n",
      "-0.29999999999999993\n",
      "-0.19999999999999996\n",
      "-0.09999999999999998\n",
      "0.0\n",
      "0.10000000000000009\n",
      "0.20000000000000018\n",
      "0.30000000000000004\n",
      "0.40000000000000013\n",
      "0.5\n",
      "0.6000000000000001\n",
      "0.7000000000000002\n",
      "0.8\n",
      "0.9000000000000001\n",
      "1.0\n"
     ]
    }
   ],
   "source": [
    "def gen_qc(a):\n",
    "    qc = QuantumCircuit(1,1)\n",
    "\n",
    "    # 2x\n",
    "\n",
    "    phi_3 = [0.3434281, 2.249998, 2.2590873, 1.9060472]\n",
    "    phi_3.reverse()\n",
    "\n",
    "    qc.h(0)\n",
    "\n",
    "    qc.barrier(0)\n",
    "\n",
    "    for i in range(len(phi_3)):\n",
    "        if(i != 0):\n",
    "            qc.rx(-2*np.arccos(a),0)\n",
    "            qc.barrier(0)\n",
    "        qc.rz(-2*phi_3[i], 0)\n",
    "        qc.barrier(0)\n",
    "\n",
    "    qc.h(0)\n",
    "\n",
    "    qc.barrier(0)\n",
    "\n",
    "    # measure all the qubits\n",
    "    qc.measure([0], [0])\n",
    "\n",
    "    qc.draw(output=\"mpl\", style=\"iqp\")\n",
    "    return qc\n",
    "\n",
    "def gen_qc_full(a, phi_arr):\n",
    "    qc = QuantumCircuit(1,1)\n",
    "\n",
    "    # 2x\n",
    "\n",
    "    phi_list = phi_arr.tolist()\n",
    "    phi_list.reverse()\n",
    "\n",
    "    qc.h(0)\n",
    "\n",
    "    qc.barrier(0)\n",
    "\n",
    "    for i in range(len(phi_list)):\n",
    "        if(i != 0):\n",
    "            qc.rx(-2*np.arccos(a),0)\n",
    "            qc.barrier(0)\n",
    "        qc.rz(-2*phi_list[i], 0)\n",
    "        qc.barrier(0)\n",
    "\n",
    "    qc.h(0)\n",
    "\n",
    "    qc.barrier(0)\n",
    "\n",
    "    # measure all the qubits\n",
    "    qc.measure([0], [0])\n",
    "\n",
    "    qc.draw(output=\"mpl\", style=\"iqp\")\n",
    "    return qc\n",
    "\n",
    "def eval_qc(qc):\n",
    "    # Create an Estimator object\n",
    "    sampler = Sampler(backend_sim, options=options)\n",
    "\n",
    "    # Submit the circuit to Estimator\n",
    "    job = sampler.run(qc, shots=100)\n",
    "    jobid = job.job_id()\n",
    "    retrieve_job = service.job(jobid)\n",
    "    result = retrieve_job.result()\n",
    "    return result\n",
    "\n",
    "def eval_qc_list(qc_list):\n",
    "    # Create an Estimator object\n",
    "    sampler = Sampler(backend_sim, options=options)\n",
    "\n",
    "    # Submit the circuit to Estimator\n",
    "    job = sampler.run([x[1] for x in qc_list], shots=100)\n",
    "    jobid = job.job_id()\n",
    "    retrieve_job = service.job(jobid)\n",
    "    result = retrieve_job.result()\n",
    "    return result\n",
    "\n",
    "job_ids = []\n",
    "\n",
    "qc_list = []\n",
    "\n",
    "for x in np.linspace(-1, 1, 21):\n",
    "    qc_lcl = gen_qc(x)\n",
    "    print(x)\n",
    "    qc_list.append([x, qc_lcl])\n",
    "\n",
    "#res = eval_qc_list(qc_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "id": "ed39a079-84f2-460e-85cd-e2959376cbe7",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "For x = -1.0, computed y = 0.8944271909999159, actual sin(2x) = -0.9092974268256817\n",
      "For x = -0.9, computed y = 0.9848857801796105, actual sin(2x) = -0.9738476308781951\n",
      "For x = -0.8, computed y = 1.0, actual sin(2x) = -0.9995736030415051\n",
      "For x = -0.7, computed y = 0.9643650760992956, actual sin(2x) = -0.9854497299884601\n",
      "For x = -0.6, computed y = 0.9273618495495703, actual sin(2x) = -0.9320390859672263\n",
      "For x = -0.5, computed y = 0.8185352771872451, actual sin(2x) = -0.8414709848078965\n",
      "For x = -0.3999999999999999, computed y = 0.714142842854285, actual sin(2x) = -0.7173560908995227\n",
      "For x = -0.29999999999999993, computed y = 0.5477225575051661, actual sin(2x) = -0.5646424733950353\n",
      "For x = -0.19999999999999996, computed y = 0.33166247903553997, actual sin(2x) = -0.3894183423086504\n",
      "For x = -0.09999999999999998, computed y = 0.1414213562373095, actual sin(2x) = -0.19866933079506116\n",
      "For x = 0.0, computed y = 0.0, actual sin(2x) = 0.0\n",
      "For x = 0.10000000000000009, computed y = 0.17320508075688773, actual sin(2x) = 0.19866933079506138\n",
      "For x = 0.20000000000000018, computed y = 0.31622776601683794, actual sin(2x) = 0.3894183423086508\n",
      "For x = 0.30000000000000004, computed y = 0.4472135954999579, actual sin(2x) = 0.5646424733950355\n",
      "For x = 0.40000000000000013, computed y = 0.7211102550927979, actual sin(2x) = 0.7173560908995229\n",
      "For x = 0.5, computed y = 0.8306623862918074, actual sin(2x) = 0.8414709848078965\n",
      "For x = 0.6000000000000001, computed y = 0.9327379053088815, actual sin(2x) = 0.9320390859672264\n",
      "For x = 0.7000000000000002, computed y = 0.9797958971132712, actual sin(2x) = 0.9854497299884603\n",
      "For x = 0.8, computed y = 1.0, actual sin(2x) = 0.9995736030415051\n",
      "For x = 0.9000000000000001, computed y = 0.9643650760992956, actual sin(2x) = 0.9738476308781951\n",
      "For x = 1.0, computed y = 0.8944271909999159, actual sin(2x) = 0.9092974268256817\n"
     ]
    }
   ],
   "source": [
    "'''\n",
    "x = [u[0] for u in qc_list]\n",
    "y1 = []\n",
    "y2 = []\n",
    "i = 0\n",
    "for q in res.quasi_dists:\n",
    "    zero_val = q\n",
    "    if 0 in zero_val:\n",
    "        zero_val = zero_val[0]\n",
    "    else:\n",
    "        zero_val = 0\n",
    "    computed_y = np.sqrt(zero_val)\n",
    "    actual_y = np.sin(2*x[i])\n",
    "    print(f\"For x = {x[i]}, computed y = {computed_y}, actual sin(2x) = {actual_y}\")\n",
    "    y1.append(computed_y)\n",
    "    y2.append(actual_y)\n",
    "    i += 1\n",
    "'''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "id": "bfb7faed-925b-4544-80a8-814b32ddbd5d",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib as mpl\n",
    "\n",
    "mpl.pyplot.plot(x, np.sign(x) * y1)\n",
    "mpl.pyplot.plot(x, y2)\n",
    "mpl.pyplot.title(\"sin(2x) with QSP, n = 4, sim count = 100, sign-adjusted\")\n",
    "mpl.pyplot.xlabel(\"x\")\n",
    "mpl.pyplot.ylabel(\"sin(2x)\")\n",
    "mpl.pyplot.savefig(\"QSP_sim_4.png\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "fffdf73b-14f9-41bc-a88f-daa8a589356b",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import matplotlib as mpl"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "id": "8a0d6cf3-f870-4431-95f2-44808723cd9b",
   "metadata": {},
   "outputs": [],
   "source": [
    "o = {\"x\": x, \"y_sim\": y1, \"y_actual\": y2}\n",
    "of = open(\"QSP_sim_4.json\", \"w\")\n",
    "json.dump(o, of)\n",
    "of.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "id": "59d5de4a-bcd5-4a52-a0cf-81308b86521b",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[-1.0, SamplerResult(quasi_dists=[{0: 0.8, 1: 0.2}], metadata=[{'shots': 100, 'circuit_metadata': {}, 'readout_mitigation_overhead': 1.0, 'readout_mitigation_time': 0.011691818945109844, 'warning': 'Optimization level clipped from 3 to 1'}])], [-0.9, SamplerResult(quasi_dists=[{0: 0.92, 1: 0.08}], metadata=[{'shots': 100, 'circuit_metadata': {}, 'readout_mitigation_overhead': 1.0, 'readout_mitigation_time': 0.02065568696707487, 'warning': 'Optimization level clipped from 3 to 1'}])], [-0.8, SamplerResult(quasi_dists=[{0: 1.0}], metadata=[{'shots': 100, 'circuit_metadata': {}, 'readout_mitigation_overhead': 1.0, 'readout_mitigation_time': 0.006950044073164463, 'warning': 'Optimization level clipped from 3 to 1'}])], [-0.7, SamplerResult(quasi_dists=[{0: 0.94, 1: 0.06}], metadata=[{'shots': 100, 'circuit_metadata': {}, 'readout_mitigation_overhead': 1.0, 'readout_mitigation_time': 0.02080469485372305, 'warning': 'Optimization level clipped from 3 to 1'}])], [-0.6, SamplerResult(quasi_dists=[{0: 0.84, 1: 0.16}], metadata=[{'shots': 100, 'circuit_metadata': {}, 'readout_mitigation_overhead': 1.0, 'readout_mitigation_time': 0.023595498874783516, 'warning': 'Optimization level clipped from 3 to 1'}])], [-0.5, SamplerResult(quasi_dists=[{0: 0.72, 1: 0.28}], metadata=[{'shots': 100, 'circuit_metadata': {}, 'readout_mitigation_overhead': 1.0, 'readout_mitigation_time': 0.016423520632088184, 'warning': 'Optimization level clipped from 3 to 1'}])], [-0.3999999999999999, SamplerResult(quasi_dists=[{0: 0.46, 1: 0.54}], metadata=[{'shots': 100, 'circuit_metadata': {}, 'readout_mitigation_overhead': 1.0, 'readout_mitigation_time': 0.008462424390017986, 'warning': 'Optimization level clipped from 3 to 1'}])], [-0.29999999999999993, SamplerResult(quasi_dists=[{0: 0.26, 1: 0.74}], metadata=[{'shots': 100, 'circuit_metadata': {}, 'readout_mitigation_overhead': 1.0, 'readout_mitigation_time': 0.02964788768440485, 'warning': 'Optimization level clipped from 3 to 1'}])], [-0.19999999999999996, SamplerResult(quasi_dists=[{0: 0.1, 1: 0.9}], metadata=[{'shots': 100, 'circuit_metadata': {}, 'readout_mitigation_overhead': 1.0, 'readout_mitigation_time': 0.023218448273837566, 'warning': 'Optimization level clipped from 3 to 1'}])], [-0.09999999999999998, SamplerResult(quasi_dists=[{0: 0.03, 1: 0.97}], metadata=[{'shots': 100, 'circuit_metadata': {}, 'readout_mitigation_overhead': 1.0, 'readout_mitigation_time': 0.026075436733663082, 'warning': 'Optimization level clipped from 3 to 1'}])], [0.0, SamplerResult(quasi_dists=[{1: 1.0}], metadata=[{'shots': 100, 'circuit_metadata': {}, 'readout_mitigation_overhead': 1.0, 'readout_mitigation_time': 0.006409294903278351, 'warning': 'Optimization level clipped from 3 to 1'}])], [0.10000000000000009, SamplerResult(quasi_dists=[{0: 0.05, 1: 0.95}], metadata=[{'shots': 100, 'circuit_metadata': {}, 'readout_mitigation_overhead': 1.0, 'readout_mitigation_time': 0.01990762073546648, 'warning': 'Optimization level clipped from 3 to 1'}])], [0.20000000000000018, SamplerResult(quasi_dists=[{0: 0.07, 1: 0.93}], metadata=[{'shots': 100, 'circuit_metadata': {}, 'readout_mitigation_overhead': 1.0, 'readout_mitigation_time': 0.02317704539746046, 'warning': 'Optimization level clipped from 3 to 1'}])], [0.30000000000000004, SamplerResult(quasi_dists=[{0: 0.38, 1: 0.62}], metadata=[{'shots': 100, 'circuit_metadata': {}, 'readout_mitigation_overhead': 1.0, 'readout_mitigation_time': 0.021129446104168892, 'warning': 'Optimization level clipped from 3 to 1'}])], [0.40000000000000013, SamplerResult(quasi_dists=[{0: 0.48, 1: 0.52}], metadata=[{'shots': 100, 'circuit_metadata': {}, 'readout_mitigation_overhead': 1.0, 'readout_mitigation_time': 0.021849132142961025, 'warning': 'Optimization level clipped from 3 to 1'}])], [0.5, SamplerResult(quasi_dists=[{0: 0.7, 1: 0.3}], metadata=[{'shots': 100, 'circuit_metadata': {}, 'readout_mitigation_overhead': 1.0, 'readout_mitigation_time': 0.020084967836737633, 'warning': 'Optimization level clipped from 3 to 1'}])], [0.6000000000000001, SamplerResult(quasi_dists=[{0: 0.82, 1: 0.18}], metadata=[{'shots': 100, 'circuit_metadata': {}, 'readout_mitigation_overhead': 1.0, 'readout_mitigation_time': 0.03505833074450493, 'warning': 'Optimization level clipped from 3 to 1'}])], [0.7000000000000002, SamplerResult(quasi_dists=[{0: 0.94, 1: 0.06}], metadata=[{'shots': 100, 'circuit_metadata': {}, 'readout_mitigation_overhead': 1.0, 'readout_mitigation_time': 0.018341176211833954, 'warning': 'Optimization level clipped from 3 to 1'}])], [0.8, SamplerResult(quasi_dists=[{0: 1.0}], metadata=[{'shots': 100, 'circuit_metadata': {}, 'readout_mitigation_overhead': 1.0, 'readout_mitigation_time': 0.021165326237678528, 'warning': 'Optimization level clipped from 3 to 1'}])], [0.9000000000000001, SamplerResult(quasi_dists=[{0: 0.97, 1: 0.03}], metadata=[{'shots': 100, 'circuit_metadata': {}, 'readout_mitigation_overhead': 1.0, 'readout_mitigation_time': 0.02594634611159563, 'warning': 'Optimization level clipped from 3 to 1'}])], [1.0, SamplerResult(quasi_dists=[{0: 0.82, 1: 0.18}], metadata=[{'shots': 100, 'circuit_metadata': {}, 'readout_mitigation_overhead': 1.0, 'readout_mitigation_time': 0.021374083124101162, 'warning': 'Optimization level clipped from 3 to 1'}])]]\n"
     ]
    }
   ],
   "source": [
    "print(job_ids)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "648fbf8f-65b2-4ee5-a8c2-b8f8cbab1e28",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib as mpl"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "c2f381cb-817c-4ee2-ad75-53393d5480be",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Evaluating get_phi(19)\n",
      "b = 1127.3929411716574\n",
      "WARNING:tensorflow:You are casting an input of type complex64 to an incompatible dtype float32.  This will discard the imaginary part and may not be what you intended.\n",
      "WARNING:tensorflow:You are casting an input of type complex64 to an incompatible dtype float32.  This will discard the imaginary part and may not be what you intended.\n",
      "WARNING:tensorflow:You are casting an input of type complex64 to an incompatible dtype float32.  This will discard the imaginary part and may not be what you intended.\n",
      "WARNING:tensorflow:You are casting an input of type complex64 to an incompatible dtype float32.  This will discard the imaginary part and may not be what you intended.\n",
      "WARNING:tensorflow:You are casting an input of type complex64 to an incompatible dtype float32.  This will discard the imaginary part and may not be what you intended.\n",
      "WARNING:tensorflow:You are casting an input of type complex64 to an incompatible dtype float32.  This will discard the imaginary part and may not be what you intended.\n",
      "WARNING:tensorflow:You are casting an input of type complex64 to an incompatible dtype float32.  This will discard the imaginary part and may not be what you intended.\n",
      "WARNING:tensorflow:You are casting an input of type complex64 to an incompatible dtype float32.  This will discard the imaginary part and may not be what you intended.\n",
      "WARNING:tensorflow:You are casting an input of type complex64 to an incompatible dtype float32.  This will discard the imaginary part and may not be what you intended.\n",
      "WARNING:tensorflow:You are casting an input of type complex64 to an incompatible dtype float32.  This will discard the imaginary part and may not be what you intended.\n",
      "WARNING:tensorflow:You are casting an input of type complex64 to an incompatible dtype float32.  This will discard the imaginary part and may not be what you intended.\n",
      "WARNING:tensorflow:You are casting an input of type complex64 to an incompatible dtype float32.  This will discard the imaginary part and may not be what you intended.\n",
      "WARNING:tensorflow:You are casting an input of type complex64 to an incompatible dtype float32.  This will discard the imaginary part and may not be what you intended.\n",
      "WARNING:tensorflow:You are casting an input of type complex64 to an incompatible dtype float32.  This will discard the imaginary part and may not be what you intended.\n",
      "WARNING:tensorflow:You are casting an input of type complex64 to an incompatible dtype float32.  This will discard the imaginary part and may not be what you intended.\n",
      "WARNING:tensorflow:You are casting an input of type complex64 to an incompatible dtype float32.  This will discard the imaginary part and may not be what you intended.\n",
      "WARNING:tensorflow:You are casting an input of type complex64 to an incompatible dtype float32.  This will discard the imaginary part and may not be what you intended.\n",
      "WARNING:tensorflow:You are casting an input of type complex64 to an incompatible dtype float32.  This will discard the imaginary part and may not be what you intended.\n",
      "WARNING:tensorflow:You are casting an input of type complex64 to an incompatible dtype float32.  This will discard the imaginary part and may not be what you intended.\n",
      "WARNING:tensorflow:You are casting an input of type complex64 to an incompatible dtype float32.  This will discard the imaginary part and may not be what you intended.\n",
      "WARNING:tensorflow:You are casting an input of type complex64 to an incompatible dtype float32.  This will discard the imaginary part and may not be what you intended.\n",
      "WARNING:tensorflow:You are casting an input of type complex64 to an incompatible dtype float32.  This will discard the imaginary part and may not be what you intended.\n",
      "WARNING:tensorflow:You are casting an input of type complex64 to an incompatible dtype float32.  This will discard the imaginary part and may not be what you intended.\n",
      "WARNING:tensorflow:You are casting an input of type complex64 to an incompatible dtype float32.  This will discard the imaginary part and may not be what you intended.\n",
      "WARNING:tensorflow:You are casting an input of type complex64 to an incompatible dtype float32.  This will discard the imaginary part and may not be what you intended.\n",
      "WARNING:tensorflow:You are casting an input of type complex64 to an incompatible dtype float32.  This will discard the imaginary part and may not be what you intended.\n",
      "WARNING:tensorflow:You are casting an input of type complex64 to an incompatible dtype float32.  This will discard the imaginary part and may not be what you intended.\n",
      "WARNING:tensorflow:You are casting an input of type complex64 to an incompatible dtype float32.  This will discard the imaginary part and may not be what you intended.\n",
      "WARNING:tensorflow:You are casting an input of type complex64 to an incompatible dtype float32.  This will discard the imaginary part and may not be what you intended.\n",
      "WARNING:tensorflow:You are casting an input of type complex64 to an incompatible dtype float32.  This will discard the imaginary part and may not be what you intended.\n",
      "WARNING:tensorflow:You are casting an input of type complex64 to an incompatible dtype float32.  This will discard the imaginary part and may not be what you intended.\n",
      "WARNING:tensorflow:You are casting an input of type complex64 to an incompatible dtype float32.  This will discard the imaginary part and may not be what you intended.\n",
      "WARNING:tensorflow:You are casting an input of type complex64 to an incompatible dtype float32.  This will discard the imaginary part and may not be what you intended.\n",
      "WARNING:tensorflow:You are casting an input of type complex64 to an incompatible dtype float32.  This will discard the imaginary part and may not be what you intended.\n",
      "WARNING:tensorflow:You are casting an input of type complex64 to an incompatible dtype float32.  This will discard the imaginary part and may not be what you intended.\n",
      "WARNING:tensorflow:You are casting an input of type complex64 to an incompatible dtype float32.  This will discard the imaginary part and may not be what you intended.\n",
      "WARNING:tensorflow:You are casting an input of type complex64 to an incompatible dtype float32.  This will discard the imaginary part and may not be what you intended.\n",
      "WARNING:tensorflow:You are casting an input of type complex64 to an incompatible dtype float32.  This will discard the imaginary part and may not be what you intended.\n",
      "WARNING:tensorflow:You are casting an input of type complex64 to an incompatible dtype float32.  This will discard the imaginary part and may not be what you intended.\n",
      "WARNING:tensorflow:You are casting an input of type complex64 to an incompatible dtype float32.  This will discard the imaginary part and may not be what you intended.\n",
      "Finished evaluating get_phi(19)\n",
      "-1.0\n",
      "-0.9\n",
      "-0.8\n",
      "-0.7\n",
      "-0.6\n",
      "-0.5\n",
      "-0.3999999999999999\n",
      "-0.29999999999999993\n",
      "-0.19999999999999996\n",
      "-0.09999999999999998\n",
      "0.0\n",
      "0.10000000000000009\n",
      "0.20000000000000018\n",
      "0.30000000000000004\n",
      "0.40000000000000013\n",
      "0.5\n",
      "0.6000000000000001\n",
      "0.7000000000000002\n",
      "0.8\n",
      "0.9000000000000001\n",
      "1.0\n",
      "[[-1.0, <qiskit.circuit.quantumcircuit.QuantumCircuit object at 0x7fa807846850>], [-0.9, <qiskit.circuit.quantumcircuit.QuantumCircuit object at 0x7fa7546e4970>], [-0.8, <qiskit.circuit.quantumcircuit.QuantumCircuit object at 0x7fa78996dc40>], [-0.7, <qiskit.circuit.quantumcircuit.QuantumCircuit object at 0x7fa789f61ca0>], [-0.6, <qiskit.circuit.quantumcircuit.QuantumCircuit object at 0x7fa789da25b0>], [-0.5, <qiskit.circuit.quantumcircuit.QuantumCircuit object at 0x7fa78996dbe0>], [-0.3999999999999999, <qiskit.circuit.quantumcircuit.QuantumCircuit object at 0x7fa7893e0490>], [-0.29999999999999993, <qiskit.circuit.quantumcircuit.QuantumCircuit object at 0x7fa789da2d00>], [-0.19999999999999996, <qiskit.circuit.quantumcircuit.QuantumCircuit object at 0x7fa788f53df0>], [-0.09999999999999998, <qiskit.circuit.quantumcircuit.QuantumCircuit object at 0x7fa788f53400>], [0.0, <qiskit.circuit.quantumcircuit.QuantumCircuit object at 0x7fa767caab80>], [0.10000000000000009, <qiskit.circuit.quantumcircuit.QuantumCircuit object at 0x7fa767ef8a90>], [0.20000000000000018, <qiskit.circuit.quantumcircuit.QuantumCircuit object at 0x7fa76781df70>], [0.30000000000000004, <qiskit.circuit.quantumcircuit.QuantumCircuit object at 0x7fa767caaa60>], [0.40000000000000013, <qiskit.circuit.quantumcircuit.QuantumCircuit object at 0x7fa76739cfd0>], [0.5, <qiskit.circuit.quantumcircuit.QuantumCircuit object at 0x7fa76739cb80>], [0.6000000000000001, <qiskit.circuit.quantumcircuit.QuantumCircuit object at 0x7fa78adb6e50>], [0.7000000000000002, <qiskit.circuit.quantumcircuit.QuantumCircuit object at 0x7fa76718c5b0>], [0.8, <qiskit.circuit.quantumcircuit.QuantumCircuit object at 0x7fa766bd7d30>], [0.9000000000000001, <qiskit.circuit.quantumcircuit.QuantumCircuit object at 0x7fa789f53a90>], [1.0, <qiskit.circuit.quantumcircuit.QuantumCircuit object at 0x7fa757c47970>]]\n",
      "For x = -1.0, computed y = 0.0, actual 0.05/(x) = 0.05\n",
      "For x = -0.9, computed y = 0.0, actual 0.05/(x) = 0.05555555555555556\n",
      "For x = -0.8, computed y = 0.0, actual 0.05/(x) = 0.0625\n",
      "For x = -0.7, computed y = 0.17320508075688773, actual 0.05/(x) = 0.07142857142857144\n",
      "For x = -0.6, computed y = 0.0, actual 0.05/(x) = 0.08333333333333334\n",
      "For x = -0.5, computed y = 0.1, actual 0.05/(x) = 0.1\n",
      "For x = -0.3999999999999999, computed y = 0.1414213562373095, actual 0.05/(x) = 0.12500000000000003\n",
      "For x = -0.29999999999999993, computed y = 0.17320508075688773, actual 0.05/(x) = 0.1666666666666667\n",
      "For x = -0.19999999999999996, computed y = 0.33166247903553997, actual 0.05/(x) = 0.25000000000000006\n",
      "For x = -0.09999999999999998, computed y = 0.458257569495584, actual 0.05/(x) = 0.5000000000000001\n",
      "For x = 0.0, computed y = 0.0, actual 0.05/(x) = nan\n",
      "For x = 0.10000000000000009, computed y = 0.469041575982343, actual 0.05/(x) = 0.4999999999999996\n",
      "For x = 0.20000000000000018, computed y = 0.36055512754639896, actual 0.05/(x) = 0.2499999999999998\n",
      "For x = 0.30000000000000004, computed y = 0.0, actual 0.05/(x) = 0.16666666666666666\n",
      "For x = 0.40000000000000013, computed y = 0.0, actual 0.05/(x) = 0.12499999999999997\n",
      "For x = 0.5, computed y = 0.17320508075688773, actual 0.05/(x) = 0.1\n",
      "For x = 0.6000000000000001, computed y = 0.1, actual 0.05/(x) = 0.08333333333333333\n",
      "For x = 0.7000000000000002, computed y = 0.1414213562373095, actual 0.05/(x) = 0.07142857142857141\n",
      "For x = 0.8, computed y = 0.1, actual 0.05/(x) = 0.0625\n",
      "For x = 0.9000000000000001, computed y = 0.1, actual 0.05/(x) = 0.05555555555555555\n",
      "For x = 1.0, computed y = 0.0, actual 0.05/(x) = 0.05\n",
      "[0.0, 0.0, 0.0, 0.17320508075688773, 0.0, 0.1, 0.1414213562373095, 0.17320508075688773, 0.33166247903553997, 0.458257569495584, 0.0, 0.469041575982343, 0.36055512754639896, 0.0, 0.0, 0.17320508075688773, 0.1, 0.1414213562373095, 0.1, 0.1, 0.0]\n",
      "[0.05, 0.05555555555555556, 0.0625, 0.07142857142857144, 0.08333333333333334, 0.1, 0.12500000000000003, 0.1666666666666667, 0.25000000000000006, 0.5000000000000001, nan, 0.4999999999999996, 0.2499999999999998, 0.16666666666666666, 0.12499999999999997, 0.1, 0.08333333333333333, 0.07142857142857141, 0.0625, 0.05555555555555555, 0.05]\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x_phi_arr = get_phi(19)\n",
    "def run_everything(n):\n",
    "    d = n - 1\n",
    "    phi_arr = x_phi_arr\n",
    "    qc_list = []\n",
    "    for x in np.linspace(-1, 1, 21):\n",
    "        qc_lcl = gen_qc_full(x, phi_arr)\n",
    "        print(x)\n",
    "        qc_list.append([x, qc_lcl])\n",
    "    print(qc_list)\n",
    "    res = eval_qc_list(qc_list)\n",
    "    x = [u[0] for u in qc_list]\n",
    "    y1 = []\n",
    "    y2 = []\n",
    "    i = 0\n",
    "    for q in res.quasi_dists:\n",
    "        zero_val = q\n",
    "        if 0 in zero_val:\n",
    "            zero_val = zero_val[0]\n",
    "        else:\n",
    "            zero_val = 0\n",
    "        computed_y = np.sqrt(zero_val)\n",
    "        if(x[i] != 0):\n",
    "            actual_y = np.abs(0.05/x[i])\n",
    "        else:\n",
    "            actual_y = np.nan\n",
    "        print(f\"For x = {x[i]}, computed y = {computed_y}, actual 0.05/(x) = {actual_y}\")\n",
    "        y1.append(computed_y)\n",
    "        y2.append(actual_y)\n",
    "        i += 1\n",
    "    print(y1)\n",
    "    print(y2)\n",
    "    mpl.pyplot.plot(x, y1)\n",
    "    mpl.pyplot.plot(x, y2)\n",
    "    mpl.pyplot.title(f\"abs(0.05/x) with QSP using special polynomial, k = 15, epsilon = 0.01, n = {n}, sim count = 100, sign-adjusted\")\n",
    "    mpl.pyplot.xlabel(\"x\")\n",
    "    mpl.pyplot.ylabel(\"abs(0.05/x)\")\n",
    "    mpl.pyplot.savefig(f\"QSP_sim_{n}_reci2.png\")\n",
    "    o = {\"x\": x, \"y_sim\": y1, \"y_actual\": y2}\n",
    "    of = open(f\"QSP_sim_{n}_reci2.json\", \"w\")\n",
    "    json.dump(o, of)\n",
    "    of.close()\n",
    "    of2 = open(f\"QSP_sim{n}_phi_reci2.json\", \"w\")\n",
    "    o2 = {\"phi\": phi_arr}\n",
    "    json.dump(o, of2)\n",
    "    of2.close()\n",
    "    \n",
    "run_everything(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "8908d2d6-803e-4e90-9d5c-703dc78311ed",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "For x = -1.0, computed y = 0.9564230748153327, actual sin(2x) = -0.9092974268256817\n",
      "For x = -0.9, computed y = 1.0539561325841624, actual sin(2x) = -0.9738476308781951\n",
      "For x = -0.8, computed y = 1.0446126967802516, actual sin(2x) = -0.9995736030415051\n",
      "For x = -0.7, computed y = 1.0256705123966467, actual sin(2x) = -0.9854497299884601\n",
      "For x = -0.6, computed y = 1.0539561325841624, actual sin(2x) = -0.9320390859672263\n",
      "For x = -0.5, computed y = 0.9251602934775416, actual sin(2x) = -0.8414709848078965\n",
      "For x = -0.3999999999999999, computed y = 0.7625036161920036, actual sin(2x) = -0.7173560908995227\n",
      "For x = -0.29999999999999993, computed y = 0.6664509454904374, actual sin(2x) = -0.5646424733950353\n",
      "For x = -0.19999999999999996, computed y = 0.571410363855582, actual sin(2x) = -0.3894183423086504\n",
      "For x = -0.09999999999999998, computed y = 0.3020193475168598, actual sin(2x) = -0.19866933079506116\n",
      "For x = 0.0, computed y = 0.11306773954355921, actual sin(2x) = 0.0\n",
      "For x = 0.10000000000000009, computed y = 0.33290168129909564, actual sin(2x) = 0.19866933079506138\n",
      "For x = 0.20000000000000018, computed y = 0.6047524205789327, actual sin(2x) = 0.3894183423086508\n",
      "For x = 0.30000000000000004, computed y = 0.6952499903053633, actual sin(2x) = 0.5646424733950355\n",
      "For x = 0.40000000000000013, computed y = 0.7877991184181358, actual sin(2x) = 0.7173560908995229\n",
      "For x = 0.5, computed y = 0.8928034717762282, actual sin(2x) = 0.8414709848078965\n",
      "For x = 0.6000000000000001, computed y = 1.0723988137284908, actual sin(2x) = 0.9320390859672264\n",
      "For x = 0.7000000000000002, computed y = 1.0723988137284908, actual sin(2x) = 0.9854497299884603\n",
      "For x = 0.8, computed y = 1.099482944432536, actual sin(2x) = 0.9995736030415051\n",
      "For x = 0.9000000000000001, computed y = 1.0351849318538475, actual sin(2x) = 0.9738476308781951\n",
      "For x = 1.0, computed y = 0.9965823952831172, actual sin(2x) = 0.9092974268256817\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def data_extractor():\n",
    "    n = 20\n",
    "    retrieve_job = service.job(\"cpzja81dhsd0008e3hq0\")\n",
    "    res = retrieve_job.result()\n",
    "    x = [x for x in np.linspace(-1, 1, 21)]\n",
    "    y1 = []\n",
    "    y2 = []\n",
    "    i = 0\n",
    "    for q in res.quasi_dists:\n",
    "        zero_val = q\n",
    "        if 0 in zero_val and zero_val[0] > 0:\n",
    "            zero_val = zero_val[0]\n",
    "        else:\n",
    "            zero_val = 0\n",
    "        computed_y = np.sqrt(zero_val)\n",
    "        actual_y = np.sin(2*x[i])\n",
    "        print(f\"For x = {x[i]}, computed y = {computed_y}, actual sin(2x) = {actual_y}\")\n",
    "        y1.append(computed_y)\n",
    "        y2.append(actual_y)\n",
    "        i += 1\n",
    "    mpl.pyplot.plot(x, np.sign(x) * y1)\n",
    "    mpl.pyplot.plot(x, y2)\n",
    "    mpl.pyplot.title(f\"sin(2x) with QSP, n = {n}, sim count = 100, sign-adjusted\")\n",
    "    mpl.pyplot.xlabel(\"x\")\n",
    "    mpl.pyplot.ylabel(\"sin(2x)\")\n",
    "    mpl.pyplot.savefig(f\"QSP_sim_{n}_actual.png\")\n",
    "    o = {\"x\": x, \"y_sim\": y1, \"y_actual\": y2}\n",
    "    of = open(f\"QSP_sim_{n}_actual.json\", \"w\")\n",
    "    json.dump(o, of)\n",
    "    of.close()\n",
    "    #of2 = open(f\"QSP_sim{n}_phi_actual.json\", \"w\")\n",
    "    #o2 = {\"phi\": phi_arr}\n",
    "    #json.dump(o, of2)\n",
    "    #of2.close()\n",
    "data_extractor()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 [qBraid]",
   "language": "python",
   "name": "python3_qbraid_sdk_9j9sjy"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.18"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}