Graph Data Notebook2.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from qiskit import BasicAer, Aer, IBMQ\n",
    "from qiskit.aqua import QuantumInstance, aqua_globals\n",
    "from qiskit.aqua.algorithms import VQE, ExactEigensolver, NumPyEigensolver\n",
    "from qiskit.aqua.components.initial_states import Zero\n",
    "from qiskit.aqua.components.optimizers import ADAM, AQGD, COBYLA, L_BFGS_B, SLSQP\n",
    "from qiskit.aqua.components.optimizers import SPSA, TNC, POWELL, P_BFGS\n",
    "from qiskit.aqua.components.optimizers import NFT, NELDER_MEAD, GSLS, CG\n",
    "from qiskit.aqua.components.variational_forms import RY, RYRZ, SwapRZ\n",
    "from qiskit.aqua.operators import WeightedPauliOperator, Z2Symmetries\n",
    "from qiskit.chemistry import FermionicOperator\n",
    "from qiskit.chemistry.drivers import PySCFDriver, UnitsType\n",
    "from qiskit.chemistry.components.variational_forms import UCCSD\n",
    "from qiskit.chemistry.components.initial_states import HartreeFock\n",
    "\n",
    "from qiskit.providers.aer import QasmSimulator\n",
    "from qiskit.providers.aer.noise import NoiseModel\n",
    "from qiskit.providers.aer.noise.errors import QuantumError, ReadoutError\n",
    "from qiskit.providers.aer.noise.errors import pauli_error\n",
    "from qiskit.providers.aer.noise.errors import depolarizing_error\n",
    "from qiskit.providers.aer.noise.errors import thermal_relaxation_error\n",
    "\n",
    "from qiskit.providers.aer import noise\n",
    "\n",
    "from qiskit.tools.monitor import job_monitor\n",
    "\n",
    "from qiskit.providers.ibmq import least_busy\n",
    "\n",
    "import warnings\n",
    "\n",
    "IBMQ.load_account()\n",
    "provider = IBMQ.get_provider(group='open')\n",
    "backend = least_busy(provider.backends(simulator=False, operational=True))\n",
    "\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from functools import partial"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "inter_dist = 1.5108585\n",
    "driver = PySCFDriver(atom='Li .0 .0 .0; H .0 .0' + str(inter_dist), unit=UnitsType.ANGSTROM, charge=0, spin=0, basis='sto3g')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# please be aware that the idx here with respective to original idx\n",
    "freeze_list = [0]\n",
    "remove_list = [-3, -2] # negative number denotes the reverse order"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Classically solve for the lowest eigenvalue\n",
    "def exact_solver(qubitOp):\n",
    "    ee = ExactEigensolver(qubitOp)\n",
    "    result = ee.run()\n",
    "    ref = result['energy']\n",
    "    print('Reference value: {}'.format(ref))\n",
    "    return ref"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "backend = BasicAer.get_backend('statevector_simulator')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.7/site-packages/qiskit/providers/aer/noise/device/basic_device_model.py:115: DeprecationWarning: This function is been deprecated and moved to a method of the`NoiseModel` class. For equivalent functionality use `NoiseModel.from_backend(properties, **kwargs).\n",
      "  DeprecationWarning)\n"
     ]
    }
   ],
   "source": [
    "#Define our noise model based on the ibmq_essex chip\n",
    "chip_name = 'ibmq_essex'\n",
    "device = provider.get_backend(chip_name)\n",
    "coupling_map = device.configuration().coupling_map\n",
    "noise_model = noise.device.basic_device_noise_model(device.properties())\n",
    "basis_gates = noise_model.basis_gates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "# of electrons: 4\n",
      "# of spin orbitals: 12\n",
      "Reference value: 0.6401176053547593\n",
      "==================================================\n",
      "Reference value: 0.6401176053547593\n",
      "POWELL VQE energy:  0.64011761448508\n",
      "HF energy: -7.863382128457065\n",
      "POWELL Reference Value Percent Error: 1.4263505089248675e-06%\n",
      "POWELL Energy Value Percent Error: 8.1404871851329%\n",
      "==================================================\n"
     ]
    }
   ],
   "source": [
    "# Classically solve for the lowest eigenvalue\n",
    "# This is used just to compare how well you VQE approximation is performing\n",
    "def exact_solver(qubitOp):\n",
    "    ee = ExactEigensolver(qubitOp)\n",
    "    result = ee.run()\n",
    "    ref = result['energy']\n",
    "    print('Reference value: {}'.format(ref))\n",
    "    return ref\n",
    "\n",
    "counts = []\n",
    "values = []\n",
    "params = []\n",
    "deviation = []\n",
    "def store_intermediate_result(eval_count, parameters, mean, std):\n",
    "    counts.append(eval_count)\n",
    "    values.append(mean)\n",
    "    params.append(parameters)\n",
    "    deviation.append(std)\n",
    "\n",
    "# Define your function for computing the qubit operations of LiH\n",
    "def compute_LiH_qubitOp(map_type, inter_dist, basis='sto3g'):\n",
    "    \n",
    "    # Specify details of our molecule\n",
    "    driver = PySCFDriver(atom='Li 0 0 0; H 0 0 ' + str(inter_dist), unit=UnitsType.ANGSTROM, charge=0, spin=0, basis=basis)\n",
    "\n",
    "    # Compute relevant 1 and 2 body integrals.\n",
    "    molecule = driver.run()\n",
    "    h1 = molecule.one_body_integrals\n",
    "    h2 = molecule.two_body_integrals\n",
    "    \n",
    "    num_particles = molecule.num_alpha + molecule.num_beta\n",
    "    num_spin_orbitals = molecule.num_orbitals * 2\n",
    "    nuclear_repulsion_energy = molecule.nuclear_repulsion_energy\n",
    "    \n",
    "    print(\"# of electrons: {}\".format(num_particles))\n",
    "    print(\"# of spin orbitals: {}\".format(num_spin_orbitals))\n",
    "\n",
    "    # Please be aware that the idx here with respective to original idx\n",
    "    freeze_list = [0,1,6,7]\n",
    "    remove_list = [0,1,4,5] # negative number denotes the reverse order\n",
    "    \n",
    "    # Prepare full idx of freeze_list and remove_list\n",
    "    # Convert all negative idx to positive\n",
    "    \n",
    "    num_spin_orbitals -= len(remove_list)\n",
    "\n",
    "    # Prepare fermionic hamiltonian with orbital freezing and eliminating, and then map to qubit hamiltonian\n",
    "    # and if PARITY mapping is selected, reduction qubits\n",
    "    energy_shift = 0.0\n",
    "    qubit_reduction = True if map_type == 'parity' else False\n",
    "    ferOp = FermionicOperator(h1=h1, h2=h2)\n",
    "    if len(freeze_list) > 0:\n",
    "        ferOp, energy_shift = ferOp.fermion_mode_freezing(freeze_list)\n",
    "        num_spin_orbitals -= len(freeze_list)\n",
    "        num_particles -= len(freeze_list)//2\n",
    "    if len(remove_list) > 0:\n",
    "        ferOp = ferOp.fermion_mode_elimination(remove_list)\n",
    "    qubitOp = ferOp.mapping(map_type)\n",
    "    qubitOp = Z2Symmetries.two_qubit_reduction(qubitOp, num_particles)\n",
    "    return qubitOp, num_spin_orbitals, num_particles, qubit_reduction, molecule, energy_shift, nuclear_repulsion_energy\n",
    "\n",
    "map_type = 'parity'\n",
    "qubitOp, num_spin_orbitals, num_particles, qubit_reduction, molecule, energy_shift, nuclear_repulsion_energy = compute_LiH_qubitOp(map_type, inter_dist)\n",
    "\n",
    "# Classically solve for the exact solution and use that as your reference value\n",
    "ref = exact_solver(qubitOp)\n",
    "\n",
    "# Specify your initial state\n",
    "init_state = HartreeFock(num_spin_orbitals, num_particles, qubit_mapping=map_type)\n",
    "\n",
    "# Select a state preparation ansatz\n",
    "# Equivalently, choose a parameterization for our trial wave function.\n",
    "RY_var_form = RY(qubitOp.num_qubits, depth=2)\n",
    "\n",
    "# Choose where to run/simulate our circuit\n",
    "quantum_instance = backend\n",
    "\n",
    "max_eval = 1000\n",
    "\n",
    "# Choose the classical optimizer\n",
    "POWELL_optimizer = POWELL(max_eval)\n",
    "\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "POWELL_vqe = VQE(qubitOp, RY_var_form, POWELL_optimizer, callback=store_intermediate_result)\n",
    "\n",
    "POWELL_vqe_eigenvalue = np.real(POWELL_vqe.run(backend)['eigenvalue'])\n",
    "\n",
    "# Now compare the results of different compositions of your VQE algorithm!\n",
    "POWELL_vqe_result = np.real(energy_shift + POWELL_vqe_eigenvalue + nuclear_repulsion_energy)\n",
    "\n",
    "print(\"==================================================\")\n",
    "print('Reference value: {}'.format(ref))\n",
    "print(\"POWELL VQE energy: \", POWELL_vqe_eigenvalue)\n",
    "print(\"HF energy: {}\".format(molecule.hf_energy))\n",
    "print(\"POWELL Reference Value Percent Error: \" + str(abs((POWELL_vqe_eigenvalue-ref)/ref)*100) + \"%\")\n",
    "print(\"POWELL Energy Value Percent Error: \" + str(np.real(abs((POWELL_vqe_result-molecule.hf_energy)/molecule.hf_energy))*100) + \"%\")\n",
    "print(\"==================================================\")\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.5\n",
      "# of electrons: 4\n",
      "# of spin orbitals: 12\n",
      "Reference value: 0.5137757287635137\n",
      "0.6\n",
      "# of electrons: 4\n",
      "# of spin orbitals: 12\n",
      "Reference value: 0.561818561542677\n",
      "0.7\n",
      "# of electrons: 4\n",
      "# of spin orbitals: 12\n",
      "Reference value: 0.5924488668685409\n",
      "0.8\n",
      "# of electrons: 4\n",
      "# of spin orbitals: 12\n",
      "Reference value: 0.6111662451952492\n",
      "0.9\n",
      "# of electrons: 4\n",
      "# of spin orbitals: 12\n",
      "Reference value: 0.6221225497963409\n",
      "1.0\n",
      "# of electrons: 4\n",
      "# of spin orbitals: 12\n",
      "Reference value: 0.6285963306778014\n",
      "1.1\n",
      "# of electrons: 4\n",
      "# of spin orbitals: 12\n",
      "Reference value: 0.6327065458462042\n",
      "1.2\n",
      "# of electrons: 4\n",
      "# of spin orbitals: 12\n",
      "Reference value: 0.6355648759855331\n",
      "1.3\n",
      "# of electrons: 4\n",
      "# of spin orbitals: 12\n",
      "Reference value: 0.6376528790149082\n",
      "1.4\n",
      "# of electrons: 4\n",
      "# of spin orbitals: 12\n",
      "Reference value: 0.6391368249576034\n",
      "1.5\n",
      "# of electrons: 4\n",
      "# of spin orbitals: 12\n",
      "Reference value: 0.6400523416744445\n",
      "1.6\n",
      "# of electrons: 4\n",
      "# of spin orbitals: 12\n",
      "Reference value: 0.640400204098064\n",
      "1.7\n",
      "# of electrons: 4\n",
      "# of spin orbitals: 12\n",
      "Reference value: 0.6401874533857751\n",
      "1.8\n",
      "# of electrons: 4\n",
      "# of spin orbitals: 12\n",
      "Reference value: 0.6394426054322893\n",
      "1.9\n",
      "# of electrons: 4\n",
      "# of spin orbitals: 12\n",
      "Reference value: 0.6382164511108538\n",
      "2.0\n",
      "# of electrons: 4\n",
      "# of spin orbitals: 12\n",
      "Reference value: 0.6365767721859397\n",
      "2.1\n",
      "# of electrons: 4\n",
      "# of spin orbitals: 12\n",
      "Reference value: 0.6346014886448722\n",
      "2.2\n",
      "# of electrons: 4\n",
      "# of spin orbitals: 12\n",
      "Reference value: 0.6323725034116937\n",
      "2.3\n",
      "# of electrons: 4\n",
      "# of spin orbitals: 12\n",
      "Reference value: 0.6299709792284195\n",
      "2.4\n",
      "# of electrons: 4\n",
      "# of spin orbitals: 12\n",
      "Reference value: 0.627473932013423\n",
      "2.5\n",
      "# of electrons: 4\n",
      "# of spin orbitals: 12\n",
      "Reference value: 0.6249517050906205\n",
      "2.6\n",
      "# of electrons: 4\n",
      "# of spin orbitals: 12\n",
      "Reference value: 0.6224659042812536\n",
      "2.7\n",
      "# of electrons: 4\n",
      "# of spin orbitals: 12\n",
      "Reference value: 0.6200675420946266\n",
      "2.8\n",
      "# of electrons: 4\n",
      "# of spin orbitals: 12\n",
      "Reference value: 0.6177953230988369\n",
      "2.9\n",
      "# of electrons: 4\n",
      "# of spin orbitals: 12\n",
      "Reference value: 0.615674151918\n",
      "3.0\n",
      "# of electrons: 4\n",
      "# of spin orbitals: 12\n",
      "Reference value: 0.613713910830528\n",
      "3.1\n",
      "# of electrons: 4\n",
      "# of spin orbitals: 12\n",
      "Reference value: 0.6064054819810103\n",
      "3.2\n",
      "# of electrons: 4\n",
      "# of spin orbitals: 12\n",
      "Reference value: 0.5960363066866098\n",
      "3.3\n",
      "# of electrons: 4\n",
      "# of spin orbitals: 12\n",
      "Reference value: 0.5872923579250626\n",
      "3.4\n",
      "# of electrons: 4\n",
      "# of spin orbitals: 12\n",
      "Reference value: 0.5800578354078402\n",
      "3.5\n",
      "# of electrons: 4\n",
      "# of spin orbitals: 12\n",
      "Reference value: 0.5741863936681987\n",
      "3.6\n",
      "# of electrons: 4\n",
      "# of spin orbitals: 12\n",
      "Reference value: 0.5695091506748114\n",
      "3.7\n",
      "# of electrons: 4\n",
      "# of spin orbitals: 12\n",
      "Reference value: 0.5658467494190048\n",
      "3.8\n",
      "# of electrons: 4\n",
      "# of spin orbitals: 12\n",
      "Reference value: 0.5630211618375427\n",
      "3.9\n",
      "# of electrons: 4\n",
      "# of spin orbitals: 12\n",
      "Reference value: 0.5608660962912557\n",
      "4.0\n",
      "# of electrons: 4\n",
      "# of spin orbitals: 12\n",
      "Reference value: 0.5592397770695023\n"
     ]
    }
   ],
   "source": [
    "pinter_d = []\n",
    "pex_list = []\n",
    "pv_list = []\n",
    "peref_list = []\n",
    "paref_list = []\n",
    "\n",
    "for inter_dist in range(5, 41):\n",
    "    inter_dist /= 10.0\n",
    "    pinter_d.append(inter_dist)\n",
    "    print(inter_dist)\n",
    "    qubitOp, num_spin_orbitals, num_particles, qubit_reduction, molecule, energy_shift, nuclear_repulsion_energy = compute_LiH_qubitOp(map_type, inter_dist)\n",
    "\n",
    "    # Classically solve for the exact solution and use that as your reference value\n",
    "    ref = exact_solver(qubitOp)\n",
    "\n",
    "    # Specify your initial state\n",
    "    init_state = HartreeFock(num_spin_orbitals, num_particles, qubit_mapping=map_type)\n",
    "\n",
    "    # Select a state preparation ansatz\n",
    "    # Equivalently, choose a parameterization for our trial wave function.\n",
    "    RY_var_form = RY(qubitOp.num_qubits, depth=2)\n",
    "\n",
    "    # Choose where to run/simulate our circuit\n",
    "    quantum_instance = backend\n",
    "\n",
    "    max_eval = 1000\n",
    "\n",
    "    # Choose the classical optimizer\n",
    "    POWELL_optimizer = POWELL(max_eval)\n",
    "\n",
    "    warnings.filterwarnings(\"ignore\")\n",
    "    POWELL_vqe = VQE(qubitOp, RY_var_form, POWELL_optimizer, callback=store_intermediate_result)\n",
    "\n",
    "    POWELL_vqe_eigenvalue = np.real(POWELL_vqe.run(backend)['eigenvalue'])\n",
    "    shift = energy_shift + nuclear_repulsion_energy\n",
    "    # Now compare the results of different compositions of your VQE algorithm!\n",
    "    POWELL_vqe_result = np.real(shift + POWELL_vqe_eigenvalue)\n",
    "\n",
    "    pex_list.append(molecule.hf_energy)\n",
    "    pv_list.append(POWELL_vqe_result)\n",
    "    peref_list.append(ref)\n",
    "    paref_list.append(POWELL_vqe_eigenvalue)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15, 10))\n",
    "plt.title('')\n",
    "#pv_list + 2*(min(pex_list)-pv_list)\n",
    "plt.plot(pinter_d, pex_list, 'b-', label='Exact Energy')\n",
    "plt.plot(pinter_d, pv_list, 'r-', label='VQE Energy')\n",
    "#plt.plot(inter_d, eref_list, 'g-', label='expected_ref')\n",
    "#plt.plot(inter_d, aref_list, 'y-', label='actual_ref')\n",
    "plt.xlabel('Interatomic Distance (Angstrom)')\n",
    "plt.ylabel('Energy')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "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": "3.7.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}