lih/lih_4qubits.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## LiH VQE  4 Qubit Example"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "JK_gVKFbS3mR"
   },
   "outputs": [],
   "source": [
    "#import packages\n",
    "import numpy as np\n",
    "import pennylane as qml\n",
    "from pennylane import expval, var, device, VQECost\n",
    "\n",
    "import sys\n",
    "sys.path.append('../')\n",
    "from run_vqe import *\n",
    "\n",
    "pi = np.pi"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "colab_type": "code",
    "id": "v3agYICYWLuq",
    "outputId": "0deef5fc-5fb9-4d07-a816-dba53d777223"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4\n"
     ]
    }
   ],
   "source": [
    "name ='LiH';charge = 0;multiplicity=1;basis= 'sto-3g';geometry = 'lih.xyz';\n",
    "h, nr_qubits = qml.qchem.generate_hamiltonian(\n",
    "    name,\n",
    "    geometry,\n",
    "    charge,\n",
    "    multiplicity,\n",
    "    basis,\n",
    "    mapping='jordan_wigner',\n",
    "    n_active_orbitals=2,\n",
    "    n_active_electrons=2,\n",
    ")\n",
    "print(nr_qubits)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "Uej1Hp1cS74d"
   },
   "outputs": [],
   "source": [
    "dev = qml.device(\"default.qubit\", wires=4)\n",
    "def ansatz(params, wires=[0,1,2,3]):\n",
    "    qml.BasisState(np.array([1, 1, 0, 0]), wires=wires)\n",
    "    for i in wires:\n",
    "        qml.RY(params[i], wires=wires[i])\n",
    "    qml.CNOT(wires=[wires[0], wires[1]])\n",
    "    qml.CNOT(wires=[wires[2], wires[3]])\n",
    "    qml.CNOT(wires=[wires[1], wires[2]])\n",
    "    for i in wires:\n",
    "        qml.RY(params[i+4], wires=wires[i])\n",
    "    qml.CNOT(wires=[wires[0], wires[1]])\n",
    "    qml.CNOT(wires=[wires[2], wires[3]])\n",
    "    qml.CNOT(wires=[wires[1], wires[2]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "b7zx1-VGXo2e"
   },
   "outputs": [],
   "source": [
    "cost = VQECost(ansatz, h, dev)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "colab_type": "code",
    "id": "7Kz1HQIc44UQ",
    "outputId": "646dbb51-e5a1-472e-9af8-38174dddead1"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "QNG\n",
      "Iteration = 0,  Ground-state energy = -7.39682593 Ha,  Convergence parameter = 0.01850782 Ha\n",
      "Iteration = 20,  Ground-state energy = -7.62671713 Ha,  Convergence parameter = 0.00443753 Ha\n",
      "Iteration = 40,  Ground-state energy = -7.68067582 Ha,  Convergence parameter = 0.00198604 Ha\n",
      "Iteration = 60,  Ground-state energy = -7.71427018 Ha,  Convergence parameter = 0.00144494 Ha\n",
      "Iteration = 80,  Ground-state energy = -7.73813195 Ha,  Convergence parameter = 0.00095651 Ha\n",
      "Iteration = 100,  Ground-state energy = -7.75221093 Ha,  Convergence parameter = 0.00049663 Ha\n",
      "Iteration = 120,  Ground-state energy = -7.75891855 Ha,  Convergence parameter = 0.00022084 Ha\n",
      "Iteration = 140,  Ground-state energy = -7.76188753 Ha,  Convergence parameter = 0.00010012 Ha\n",
      "Iteration = 160,  Ground-state energy = -7.76331137 Ha,  Convergence parameter = 0.00005180 Ha\n",
      "Iteration = 180,  Ground-state energy = -7.76410354 Ha,  Convergence parameter = 0.00003111 Ha\n",
      "Iteration = 200,  Ground-state energy = -7.76460890 Ha,  Convergence parameter = 0.00002100 Ha\n",
      "Iteration = 220,  Ground-state energy = -7.76496493 Ha,  Convergence parameter = 0.00001538 Ha\n",
      "Iteration = 240,  Ground-state energy = -7.76523358 Ha,  Convergence parameter = 0.00001192 Ha\n",
      "Iteration = 260,  Ground-state energy = -7.76544644 Ha,  Convergence parameter = 0.00000963 Ha\n",
      "Iteration = 280,  Ground-state energy = -7.76562127 Ha,  Convergence parameter = 0.00000802 Ha\n",
      "Iteration = 300,  Ground-state energy = -7.76576873 Ha,  Convergence parameter = 0.00000684 Ha\n",
      "Iteration = 320,  Ground-state energy = -7.76589562 Ha,  Convergence parameter = 0.00000593 Ha\n",
      "Iteration = 340,  Ground-state energy = -7.76600642 Ha,  Convergence parameter = 0.00000521 Ha\n",
      "Vanilla\n",
      "Iteration = 0,  Ground-state energy = -7.38206126 Ha,  Convergence parameter = 0.00374316 Ha\n",
      "Iteration = 20,  Ground-state energy = -7.45617231 Ha,  Convergence parameter = 0.00356984 Ha\n",
      "Iteration = 40,  Ground-state energy = -7.52088172 Ha,  Convergence parameter = 0.00287935 Ha\n",
      "Iteration = 60,  Ground-state energy = -7.56982876 Ha,  Convergence parameter = 0.00206842 Ha\n",
      "Iteration = 80,  Ground-state energy = -7.60410466 Ha,  Convergence parameter = 0.00142975 Ha\n",
      "Iteration = 100,  Ground-state energy = -7.62800412 Ha,  Convergence parameter = 0.00101432 Ha\n",
      "Iteration = 120,  Ground-state energy = -7.64543800 Ha,  Convergence parameter = 0.00076291 Ha\n",
      "Iteration = 140,  Ground-state energy = -7.65897268 Ha,  Convergence parameter = 0.00061032 Ha\n",
      "Iteration = 160,  Ground-state energy = -7.67009322 Ha,  Convergence parameter = 0.00051336 Ha\n",
      "Iteration = 180,  Ground-state energy = -7.67963265 Ha,  Convergence parameter = 0.00044776 Ha\n",
      "Iteration = 200,  Ground-state energy = -7.68806865 Ha,  Convergence parameter = 0.00040055 Ha\n",
      "Iteration = 220,  Ground-state energy = -7.69568756 Ha,  Convergence parameter = 0.00036461 Ha\n",
      "Iteration = 240,  Ground-state energy = -7.70266761 Ha,  Convergence parameter = 0.00033576 Ha\n",
      "Iteration = 260,  Ground-state energy = -7.70912062 Ha,  Convergence parameter = 0.00031132 Ha\n",
      "Iteration = 280,  Ground-state energy = -7.71511440 Ha,  Convergence parameter = 0.00028946 Ha\n",
      "Iteration = 300,  Ground-state energy = -7.72068628 Ha,  Convergence parameter = 0.00026890 Ha\n",
      "Iteration = 320,  Ground-state energy = -7.72585298 Ha,  Convergence parameter = 0.00024881 Ha\n",
      "Iteration = 340,  Ground-state energy = -7.73061861 Ha,  Convergence parameter = 0.00022874 Ha\n"
     ]
    }
   ],
   "source": [
    "max_iterations = 350\n",
    "step_size = 0.05\n",
    "conv_tol = 1e-06\n",
    "initial_params = np.random.uniform(low=0, high=2*np.pi, size=8)\n",
    "\n",
    "print(\"QNG\")\n",
    "qng_run = run_vqe(cost, max_iterations, initial_params, 'QNGOptimizer', step_size, diag_approx=False)\n",
    "\n",
    "print(\"Vanilla\")\n",
    "vanilla_run = run_vqe(cost, max_iterations, initial_params, 'GradientDescentOptimizer', step_size)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 279
    },
    "colab_type": "code",
    "id": "4tDMZhD045nO",
    "outputId": "6c1281c3-7c9f-4a19-8089-09f091dbfebc"
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 576x396 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib import pyplot as plt\n",
    "plt.style.use(\"seaborn\")\n",
    "ax = plt.axes()\n",
    "plt.plot(qng_run[0], \"black\", label=\"QNG\")\n",
    "plt.plot(vanilla_run[0], \"b\", label=\"Vanilla GD\")\n",
    "\n",
    "plt.ylabel(\"Energy\", fontsize=16)\n",
    "plt.xlabel(\"Step\", fontsize=16)\n",
    "ax.axhline(y=-7.767496700950545, label='Groundstate', color='r')\n",
    "\n",
    "plt.legend(fontsize=16)\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "colab": {
   "name": "lih_example.ipynb",
   "provenance": []
  },
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}