Circuit Library Demo (working).ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Circuit library demo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from qiskit.circuit.library import QuantumVolume, FourierChecking, WeightedAdder #,  ..."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Chemistry example: Variational Quantum Eigensolver"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# simulator\n",
    "from qiskit import Aer\n",
    "\n",
    "# the actual algorithm\n",
    "from qiskit.aqua.algorithms import VQE\n",
    "\n",
    "# for representation of the H2 molecule\n",
    "from qiskit.aqua.operators import WeightedPauliOperator\n",
    "\n",
    "# optimizer for the variational algorithm\n",
    "from qiskit.aqua.components.optimizers import SLSQP"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# building the qubit operator for the H2 molecule\n",
    "h2_hamiltonian = {\n",
    "    'paulis': [{\"coeff\": {\"imag\": 0.0, \"real\": -1.052373245772859}, \"label\": \"II\"},\n",
    "               {\"coeff\": {\"imag\": 0.0, \"real\": 0.39793742484318045}, \"label\": \"IZ\"},\n",
    "               {\"coeff\": {\"imag\": 0.0, \"real\": -0.39793742484318045}, \"label\": \"ZI\"},\n",
    "               {\"coeff\": {\"imag\": 0.0, \"real\": -0.01128010425623538}, \"label\": \"ZZ\"},\n",
    "               {\"coeff\": {\"imag\": 0.0, \"real\": 0.18093119978423156}, \"label\": \"XX\"}\n",
    "               ]\n",
    "}\n",
    "h2_qubit_operator = WeightedPauliOperator.from_dict(h2_hamiltonian)\n",
    "\n",
    "# defining the backend for the simulation\n",
    "backend = Aer.get_backend('statevector_simulator')\n",
    "\n",
    "# defining the numerical optimizer\n",
    "optimizer = SLSQP()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The algorithm uses a \"guess\" or \"Ansatz\" wavefunction: this comes from our circuit library."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1200.56x206.4 with 1 Axes>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from qiskit.circuit.library import RYRZ  # Kandala et al., Nature Cover \n",
    "varform = RYRZ(h2_qubit_operator.num_qubits)\n",
    "varform.draw(scale=1, output='mpl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# run the algorithm\n",
    "algo = VQE(h2_qubit_operator, varform, optimizer=optimizer, quantum_instance=backend)\n",
    "result = algo.compute_minimum_eigenvalue()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Energy: -1.8572749538592355\n",
      "Optimal parameters: [ 0.05149911  0.41794549 -1.87552336  0.06836094  0.59734492 -2.93636169\n",
      " -1.88960097 -1.03848127  0.68987157 -2.51715307  0.58197612 -1.64592804\n",
      " -0.05490746 -0.38971754 -0.97332264 -2.40445358]\n"
     ]
    }
   ],
   "source": [
    "print('Energy:', result['eigenvalue'].real)\n",
    "print('Optimal parameters:', result['optimal_point'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plug-n-play: circuit library has plentiful options, e.g. `TwoLocal`, `NLocal`, `RY`, ... ."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1027.87x247.68 with 1 Axes>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from qiskit.circuit.library import TwoLocal\n",
    "\n",
    "# easy to implement all efficient 2-local circuits, e.g. all of Alan Aspuru-Guzik's Paper\n",
    "varform = TwoLocal(h2_qubit_operator.num_qubits, \n",
    "                   rotation_blocks='ry',\n",
    "                   entanglement_blocks='crx',\n",
    "                   entanglement='linear', \n",
    "                   reps=3)\n",
    "varform.draw(scale=1.2, output='mpl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "algo = VQE(h2_qubit_operator, varform, optimizer=SLSQP(), quantum_instance=backend)\n",
    "result = algo.compute_minimum_eigenvalue()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Energy: -1.857274905615748\n",
      "Optimal parameters: [-4.29572621 -1.2806668   3.80962385  5.40578658  0.48584993  3.33120281\n",
      "  6.88411738  4.75456922  1.92170827  0.7727848  -5.74463275]\n"
     ]
    }
   ],
   "source": [
    "print('Energy:', result['eigenvalue'].real)\n",
    "print('Optimal parameters:', result['optimal_point'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Circuit mutability\n",
    "\n",
    "An important feature of the circuit library is, that the circuits can be passed around as blueprints. The structure can be specified upfront, but only once all information is available we set the missing parts, e.g. the number of qubits."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![alt text](./mutability.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "class VariationalQuantumEigensolver:\n",
    "    def __init__(self, guess_wavefunction):\n",
    "        # store the guess wavefunction\n",
    "        self.guess_wavefunction = guess_wavefunction\n",
    "        \n",
    "    def compute_minumum_eigenvalue(self, hamiltonian):\n",
    "        # adjust guess wavefunction to size of the Hamiltonian\n",
    "        self.guess_wavefunction.num_qubits = hamiltonian.num_qubits\n",
    "        # run the iterative algorithm...\n",
    "        \n",
    "class QuadraticUnconstrainedBinaryOptimizer:\n",
    "    def __init__(self, variational_quantum_eigensolver):\n",
    "        # store the algorithm uses as subroutine\n",
    "        self.vqe = variational_quantum_eigensolver\n",
    "        \n",
    "    def solve(self, problem):\n",
    "        # the Variational Quantum Eigensolver must run for any problem size! \n",
    "        solution = self.vqe.compute_minimum_eigenvalue(problem)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 942.56x378.4 with 1 Axes>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "varform.num_qubits = 4\n",
    "varform.reps = 1\n",
    "varform.insert_barriers = True\n",
    "varform.entanglement = 'circular'\n",
    "varform.rotation_gates = ['z', 'ry']\n",
    "varform.draw(scale=1, output='mpl')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Machine learning example: Variational Quantum Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "os.environ['QISKIT_IN_PARALLEL'] = 'TRUE'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# the algorithm\n",
    "from qiskit.aqua.algorithms import VQC\n",
    "\n",
    "# the training and test data\n",
    "from sample import training_data, test_data, feature_dimension"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "# fix the random seeds\n",
    "from qiskit.aqua import aqua_globals\n",
    "from numpy import random\n",
    "\n",
    "aqua_globals.seed = 373\n",
    "random.seed = 53\n",
    "seed_simulator, seed_transpiler = 981, 74"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "from qiskit.circuit.library import SecondOrderExpansion\n",
    "feature_map = SecondOrderExpansion(feature_dimension=feature_dimension, depth=1)  # Havlicek et al., Nature Cover"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 840.392x144.48 with 1 Axes>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "feature_map.draw(output='mpl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "varform = RYRZ(num_qubits=feature_map.num_qubits, reps=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "vqc = VQC(optimizer, feature_map, varform, training_data, test_data)\n",
    "result = vqc.run(backend, seed_simulator=seed_simulator, seed_transpiler=seed_transpiler)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training loss: 0.1295201003294418\n",
      "Test loss: 1.2172340532672068\n"
     ]
    }
   ],
   "source": [
    "print('Training loss:', result['training_loss'])\n",
    "print('Test loss:', result['testing_loss'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "from qiskit.circuit.library import QFT"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "varform = QFT(2) + RYRZ(2, reps=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 900.592x144.48 with 1 Axes>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "varform.draw(output='mpl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "vqc = VQC(optimizer, feature_map, varform, training_data, test_data)\n",
    "result = vqc.run(backend, seed_simulator=seed_simulator, seed_transpiler=seed_transpiler)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training loss: 0.10581905313965678\n",
      "Test loss: 0.7851322859180203\n"
     ]
    }
   ],
   "source": [
    "print('Training loss:', result['training_loss'])\n",
    "print('Test loss:', result['testing_loss'])"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3.7.5 64-bit ('latest': virtualenv)",
   "language": "python",
   "name": "python37564bitlatestvirtualenv76cfe9d7d3f749489cf2ff69593234af"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}