{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "132bb9fb",
   "metadata": {},
   "source": [
    "# Example 1: Qiskit VQE calculation on water\n",
    "\n",
    "In this example, we will demonstrate how to use `qc2` in conjunction with `Qiskit-Nature` for conducting a simple VQE run on water molecule.\n",
    "We utilize the `VQE` class as implemented in `qc2`'s algorithms package along with `Qiskit` `Estimator` primitive to calculate the exact expectation values using `StateVector`."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "446f6890-b43b-470b-b415-1a1ae122126e",
   "metadata": {},
   "source": [
    "### Import required packages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "e7ab3741",
   "metadata": {},
   "outputs": [],
   "source": [
    "# ASE molecule object\n",
    "from ase.build import molecule\n",
    "\n",
    "# Qiskit-related packages\n",
    "from qiskit_algorithms.optimizers import SLSQP\n",
    "from qiskit.primitives import Estimator\n",
    "\n",
    "# ignore package import warnings\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "# qc2 packages\n",
    "from qc2.data import qc2Data\n",
    "from qc2.ase import PySCF\n",
    "from qc2.algorithms.utils import ActiveSpace\n",
    "from qc2.algorithms.qiskit import VQE"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d20e8585-f670-46e6-a5af-006f78f0f94e",
   "metadata": {},
   "source": [
    "### Instantiate `qc2Data` class and run qc2-ASE calculator"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "f47b6fcc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "* Reference energy (Hartree): -74.9644048239943\n",
      "* Saving qchem data in h2o.fcidump\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# set Atoms object\n",
    "mol = molecule('H2O')\n",
    "\n",
    "# instantiate qc2Data class\n",
    "qc2data = qc2Data(\n",
    "    molecule=mol,\n",
    "    filename='h2o.fcidump',\n",
    "    schema='fcidump'\n",
    ")\n",
    "\n",
    "# specify the qchem calculator\n",
    "qc2data.molecule.calc = PySCF(method='scf.RHF', basis='sto-3g')\n",
    "\n",
    "# run calculation and save qchem data in the hdf5 file\n",
    "qc2data.run()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c891b829-9c1d-4ef0-a3c8-0c0a12a2b52a",
   "metadata": {},
   "source": [
    "This generates a `h2o.fcidump` file containing all relevant qchem data according to `FCIDump` data format. To check the its content, run the following command in your shell:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "26442d20-d658-4786-9415-38b135d66d1c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " &FCI NORB=   7,NELEC=10,MS2=0,\n",
      "  ORBSYM=1,1,1,1,1,1,1,\n",
      "  ISYM=1,\n",
      " &END\n",
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      " -0.009651918573161179    7    2    7    6\n",
      " 0.3627647940282155    7    3    1    1\n",
      " -0.007453552241065664    7    3    2    1\n",
      " 0.1398649168466555    7    3    2    2\n",
      " 0.09015588138839581    7    3    3    3\n",
      " 0.000772285626756568    7    3    4    1\n",
      " 0.07678080123189159    7    3    4    2\n",
      " 0.1585407260120993    7    3    4    4\n",
      " 0.1906630638921215    7    3    5    5\n",
      " -0.006898943250865033    7    3    6    1\n",
      " 0.07617601003918831    7    3    6    2\n",
      " 1.356363021934841e-15    7    3    6    3\n",
      " -0.08045972980088245    7    3    6    4\n",
      " 0.03842653043551628    7    3    6    6\n",
      " -1.458652648385282e-15    7    3    7    2\n",
      " 0.1531342769573286    7    3    7    3\n",
      " 1.529327070110598e-15    7    3    7    4\n",
      " 1.153282008411514e-15    7    3    7    6\n",
      " 0.09297442082413787    7    3    7    7\n",
      " 3.364245803068242e-15    7    4    1    1\n",
      " 1.389716618145098e-15    7    4    2    2\n",
      " 0.009556726148861704    7    4    3    1\n",
      " 0.07706053589022074    7    4    3    2\n",
      " 0.001043475871893138    7    4    4    3\n",
      " 1.737940627616615e-15    7    4    4    4\n",
      " 1.789325501258022e-15    7    4    5    5\n",
      " 1.195578722987807e-15    7    4    6    2\n",
      " -0.0451905547175164    7    4    6    3\n",
      " 2.001519238114518e-15    7    4    6    6\n",
      " 0.01306260713334151    7    4    7    1\n",
      " 0.01668871234811916    7    4    7    2\n",
      " 1.552408457946998e-15    7    4    7    3\n",
      " 0.06931016714204277    7    4    7    4\n",
      " 0.05820341909693214    7    4    7    6\n",
      " 0.02370319498932772    7    5    5    3\n",
      " 0.02438276319881266    7    5    7    5\n",
      " 0.009063549750067235    7    6    3    1\n",
      " 0.09758762344445403    7    6    3    2\n",
      " 1.176568968163808e-15    7    6    4    2\n",
      " -0.04885190310188139    7    6    4    3\n",
      " -0.06440089752420797    7    6    6    3\n",
      " 2.174905696035165e-15    7    6    6    6\n",
      " 0.01198082551450466    7    6    7    1\n",
      " -0.009651918573161167    7    6    7    2\n",
      " 1.214911116732773e-15    7    6    7    3\n",
      " 0.05820341909693213    7    6    7    4\n",
      " 0.114939683966506    7    6    7    6\n",
      " -2.191026960003653e-15    7    6    7    7\n",
      " 0.8659055774540965    7    7    1    1\n",
      " -0.009328489006372955    7    7    2    1\n",
      " 0.6228599325576134    7    7    2    2\n",
      " -2.318866196652988e-15    7    7    3    2\n",
      " 0.6085814834244393    7    7    3    3\n",
      " -0.004158409575623865    7    7    4    1\n",
      " 0.01412779594662345    7    7    4    2\n",
      " 1.086375233124836e-15    7    7    4    3\n",
      " 0.6058183134590611    7    7    4    4\n",
      " 0.6235489186871658    7    7    5    5\n",
      " -0.004977251286559527    7    7    6    1\n",
      " 0.06843537147361967    7    7    6    2\n",
      " 2.607079502526012e-15    7    7    6    3\n",
      " -0.04222698765049276    7    7    6    4\n",
      " 0.5653753395057997    7    7    6    6\n",
      " 0.09297442082413805    7    7    7    3\n",
      " -2.067324805686628e-15    7    7    7    6\n",
      " 0.6176064918309245    7    7    7    7\n",
      " -32.6903898001836    1    1  0  0\n",
      " 0.5585649071450988    2    1  0  0\n",
      " -7.660069487200806    2    2  0  0\n",
      " -6.336245047052339    3    3  0  0\n",
      " 0.2342354719415093    4    1  0  0\n",
      " -0.4415980193065576    4    2  0  0\n",
      " -6.95595396650497    4    4  0  0\n",
      " 2.404327181214646e-15    5    3  0  0\n",
      " -7.448014560193811    5    5  0  0\n",
      " 0.3003578831714497    6    1  0  0\n",
      " -1.37009214945238    6    2  0  0\n",
      " -2.382256809471817e-14    6    3  0  0\n",
      " 1.100023238319837    6    4  0  0\n",
      " -5.337766699268862    6    6  0  0\n",
      " -3.757601657495413e-15    7    1  0  0\n",
      " 1.779283892953198e-14    7    2  0  0\n",
      " -1.712519470075941    7    3  0  0\n",
      " -1.605794172216009e-14    7    4  0  0\n",
      " -2.384772055350269e-15    7    6  0  0\n",
      " -5.595588929631097    7    7  0  0\n",
      " 9.088293769139284  0  0  0  0\n"
     ]
    }
   ],
   "source": [
    "!cat h2o.fcidump"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ce2405e7-e315-4001-a216-f7bab01b8e53",
   "metadata": {},
   "source": [
    "### Instantiate `qc2.algorithms.VQE` class"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "4b6beb41-81d4-4348-81b3-02acf6df5192",
   "metadata": {},
   "outputs": [],
   "source": [
    "# set up activate space\n",
    "active_space = ActiveSpace(\n",
    "    num_active_electrons=(2, 2),  # => (n_alpha, n_beta)\n",
    "    num_active_spatial_orbitals=3 # => active orbitals comprise of only 2px, 2py and 2pz of oxigen\n",
    ")\n",
    "\n",
    "# instantiate VQE class\n",
    "qc2data.algorithm = VQE(\n",
    "    active_space=active_space,\n",
    "    mapper=\"bk\",                  # => Bravyi-Kitaev mapper\n",
    "    optimizer=SLSQP(),            # => from SLSQP optimizer from Qiskit-Algorithms\n",
    "    estimator=Estimator()         # => Estimator primitive from Qiskit\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "00890d92-c10d-4210-bc77-40fba9449f2c",
   "metadata": {},
   "source": [
    "### Check reference state and ansatz"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "93eb4a6f-b10f-4422-b325-0ca8c13f1e2e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "* Reference state:\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 203.885x535.111 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "* Ansatz:\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 6140.27x535.111 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(f'* Reference state:')\n",
    "display(qc2data.algorithm.reference_state.draw('mpl'))\n",
    "\n",
    "print(f'* Ansatz:')\n",
    "# the option `fold=-1` forces the anzats to fit into the page\n",
    "display(qc2data.algorithm.ansatz.decompose().draw('mpl', fold=-1))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ea380f46-50e2-43cc-be27-e74484b06836",
   "metadata": {},
   "source": [
    "### Run VQE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "57e3f6b8-39ac-4d2d-a70a-5bda0ee7391c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "iter = 002, energy = -74.964404823994 Ha\n",
      "iter = 004, energy = -74.964404823994 Ha\n",
      "iter = 006, energy = -74.964404821877 Ha\n",
      "iter = 008, energy = -74.964404823994 Ha\n",
      "iter = 010, energy = -74.953712458779 Ha\n",
      "iter = 012, energy = -74.969016364134 Ha\n",
      "iter = 014, energy = -74.969016364134 Ha\n",
      "iter = 016, energy = -74.969016364311 Ha\n",
      "iter = 018, energy = -74.969016364250 Ha\n",
      "iter = 020, energy = -74.969038740061 Ha\n",
      "iter = 022, energy = -74.969038740061 Ha\n",
      "iter = 024, energy = -74.969038740061 Ha\n",
      "iter = 026, energy = -74.969038740061 Ha\n",
      "iter = 028, energy = -74.969038740220 Ha\n",
      "iter = 030, energy = -74.969072172816 Ha\n",
      "iter = 032, energy = -74.969072172816 Ha\n",
      "iter = 034, energy = -74.969072172813 Ha\n",
      "iter = 036, energy = -74.969072172797 Ha\n",
      "iter = 038, energy = -74.969074367841 Ha\n",
      "iter = 040, energy = -74.969074367841 Ha\n",
      "iter = 042, energy = -74.969074367841 Ha\n",
      "iter = 044, energy = -74.969074367841 Ha\n",
      "iter = 046, energy = -74.969074367844 Ha\n",
      "=== QISKIT VQE RESULTS ===\n",
      "* Electronic ground state energy (Hartree): -6.1419125033838\n",
      "* Inactive core energy (Hartree): -68.82716186445694\n",
      ">>> Total ground state energy (Hartree): -74.96907436784359\n",
      "\n"
     ]
    }
   ],
   "source": [
    "results = qc2data.algorithm.run()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "49884cae-7e33-4aa1-a00d-2685d1ed1d13",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "* Optimized circuit parameters:\n",
      "[-0.005355808793988312, -8.38625771153209e-08, -0.00535580955850707, -3.460468216184343e-07, -0.049389432156110066, -3.4748224844750195e-07, -3.296348580325791e-07, -0.030250429826813585] \n",
      "\n",
      "* VQE energy (Hartree):\n",
      "-74.96907436784359 \n",
      "\n",
      "* VQE intermediate info:\n"
     ]
    },
    {
     "data": {
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       "  {},\n",
       "  {},\n",
       "  {},\n",
       "  {},\n",
       "  {},\n",
       "  {},\n",
       "  {},\n",
       "  {},\n",
       "  {},\n",
       "  {},\n",
       "  {},\n",
       "  {},\n",
       "  {},\n",
       "  {},\n",
       "  {},\n",
       "  {},\n",
       "  {},\n",
       "  {},\n",
       "  {},\n",
       "  {},\n",
       "  {},\n",
       "  {},\n",
       "  {},\n",
       "  {},\n",
       "  {},\n",
       "  {}])"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# print optimized circuit parameters\n",
    "print(f'* Optimized circuit parameters:')\n",
    "print(results.optimal_params, \"\\n\")\n",
    "\n",
    "print(f'* VQE energy (Hartree):')\n",
    "print(results.optimal_energy, \"\\n\")\n",
    "\n",
    "# `intermediate_info` saves all data at each optimization step\n",
    "print(f'* VQE intermediate info:')\n",
    "results.optimizer_evals, results.energy, results.parameters, results.metadata"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f49fcdf8-8567-48e6-88e7-93ba732194c1",
   "metadata": {},
   "source": [
    "### Compare VQE result with classical qchem calculations\n",
    "\n",
    "Now, let's compare our VQE enery with quantum chemistry calculations performed solely using PySCF. Here is a typical example run:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "b96d2611-f3f6-4764-b57f-be7f4b4a5a96",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "* Final VQE energy (hartree):           -74.969074\n",
      "* Final CASCI/sto-3g energy (hartree):  -74.969075\n",
      "* Final CASSCF/sto-3g energy (hartree): -74.976643\n",
      "* Final FCI/sto-3g energy (hartree):    -75.015429\n"
     ]
    }
   ],
   "source": [
    "import pyscf\n",
    "from pyscf import gto, scf, mcscf, fci\n",
    "\n",
    "# set up molecule\n",
    "mol_h2o = gto.M(\n",
    "    atom='O 0 0 0.1192622641;\\\n",
    "          H 0 0.763237638 -0.4770469398;\\\n",
    "          H 0 -0.763237638, -0.4770469398',\n",
    "    basis='sto-3g',\n",
    "    verbose=0\n",
    ")\n",
    "\n",
    "# run HF\n",
    "scf_h2o = scf.HF(mol_h2o)\n",
    "hf_h2o = mol_h2o.HF().run()\n",
    "\n",
    "# run CASCI and CASSCF single-point calculations\n",
    "ncas, nelecas = (3, 4)\n",
    "energy_casci = mcscf.CASCI(hf_h2o, ncas, nelecas).kernel()\n",
    "energy_casscf = mcscf.CASSCF(hf_h2o, ncas, nelecas).kernel()\n",
    "\n",
    "# run FCI single-point calculation\n",
    "energy_fci = fci.FCI(hf_h2o).kernel()\n",
    "\n",
    "print('')\n",
    "print(f'* Final VQE energy (hartree):           {results.optimal_energy:.6f}')\n",
    "print(f'* Final CASCI/sto-3g energy (hartree):  {energy_casci[0]:.6f}')\n",
    "print(f'* Final CASSCF/sto-3g energy (hartree): {energy_casscf[0]:.6f}')\n",
    "print(f'* Final FCI/sto-3g energy (hartree):    {energy_fci[0]:.6f}')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "273c287b-67c0-4358-a9b8-ae175e75a0e4",
   "metadata": {},
   "source": [
    "As expected, our final VQE energy agrees excelently well with the one obtained with PySCF at CASCI level."
   ]
  }
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