tutorial/400_chemistry.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "0DeQHhKHtIS9"
   },
   "source": [
    "#VQE and quantum chemistry"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "xoLga_-FtPxy"
   },
   "source": [
    "On a quantum chemistry we can make hamiltonian using pauli operators and solve it using VQE. On a chemistry the advanced research on ansatz is done better than other problems and many researchers are working on this problem."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "ofFbnPOGqbKf"
   },
   "source": [
    "##2-3-1 Install\n",
    "We need to install blueqat, openfermionblueqat and openfermion."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "colab_type": "code",
    "id": "_NQIdXmptSJU",
    "outputId": "db2075c6-9dbe-4e71-9b97-71ff39b2aa28"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting blueqat\n",
      "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/f6/73/20f9cff48caee1f69190f2e1ea93c4d7d0a745fc48defb5d3072d8337583/blueqat-0.3.13-py3-none-any.whl (50kB)\n",
      "\u001b[K     |████████████████████████████████| 51kB 1.7MB/s \n",
      "\u001b[?25hCollecting openfermionblueqat\n",
      "  Downloading https://files.pythonhosted.org/packages/e0/ef/d2a4242ef5f4ff518921dfd608d7841f476e947bc23efc25bd7c3ba0042d/openfermionblueqat-0.2.2-py3-none-any.whl\n",
      "Collecting openfermion\n",
      "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/66/72/e058e3d8ececd54212bb98848e8e8227d4887fed6fe8d87f425a034add8b/openfermion-0.10.0.tar.gz (625kB)\n",
      "\u001b[K     |████████████████████████████████| 634kB 6.6MB/s \n",
      "\u001b[?25hRequirement already satisfied: scipy>=1.1.0 in /usr/local/lib/python3.6/dist-packages (from blueqat) (1.4.1)\n",
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      "Requirement already satisfied: matplotlib in /usr/local/lib/python3.6/dist-packages (from openfermion) (3.1.3)\n",
      "Collecting pubchempy\n",
      "  Downloading https://files.pythonhosted.org/packages/aa/fb/8de3aa9804b614dbc8dc5c16ed061d819cc360e0ddecda3dcd01c1552339/PubChemPy-1.0.4.tar.gz\n",
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      "Requirement already satisfied: pexpect; sys_platform != \"win32\" in /usr/local/lib/python3.6/dist-packages (from ipython>=4.0.0; python_version >= \"3.3\"->ipywidgets->jupyter->openfermion) (4.8.0)\n",
      "Requirement already satisfied: simplegeneric>0.8 in /usr/local/lib/python3.6/dist-packages (from ipython>=4.0.0; python_version >= \"3.3\"->ipywidgets->jupyter->openfermion) (0.8.1)\n",
      "Requirement already satisfied: pickleshare in /usr/local/lib/python3.6/dist-packages (from ipython>=4.0.0; python_version >= \"3.3\"->ipywidgets->jupyter->openfermion) (0.7.5)\n",
      "Requirement already satisfied: wcwidth in /usr/local/lib/python3.6/dist-packages (from prompt-toolkit<2.0.0,>=1.0.0->jupyter-console->jupyter->openfermion) (0.1.8)\n",
      "Requirement already satisfied: MarkupSafe>=0.23 in /usr/local/lib/python3.6/dist-packages (from jinja2>=2.4->nbconvert->jupyter->openfermion) (1.1.1)\n",
      "Requirement already satisfied: webencodings in /usr/local/lib/python3.6/dist-packages (from bleach->nbconvert->jupyter->openfermion) (0.5.1)\n",
      "Requirement already satisfied: ptyprocess; os_name != \"nt\" in /usr/local/lib/python3.6/dist-packages (from terminado>=0.3.3; sys_platform != \"win32\"->notebook->jupyter->openfermion) (0.6.0)\n",
      "Building wheels for collected packages: openfermion, pubchempy\n",
      "  Building wheel for openfermion (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
      "  Created wheel for openfermion: filename=openfermion-0.10.0-cp36-none-any.whl size=731375 sha256=a6a7b18652f246d94640f5fa1a2a85b9d71d28a0ea20032213626d14f8ed3f0f\n",
      "  Stored in directory: /root/.cache/pip/wheels/3c/49/cd/098bb337ffcbb70021864403773043df332c065590873f1813\n",
      "  Building wheel for pubchempy (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
      "  Created wheel for pubchempy: filename=PubChemPy-1.0.4-cp36-none-any.whl size=13825 sha256=6f935bbf877c8746569afa0d242dedb965bea20387048f6839b9f8ef47d91339\n",
      "  Stored in directory: /root/.cache/pip/wheels/10/4d/51/6b843681a9a5aef35f0d0fbce243de46f85080036e16118752\n",
      "Successfully built openfermion pubchempy\n",
      "Installing collected packages: blueqat, pubchempy, openfermion, openfermionblueqat\n",
      "Successfully installed blueqat-0.3.13 openfermion-0.10.0 openfermionblueqat-0.2.2 pubchempy-1.0.4\n"
     ]
    }
   ],
   "source": [
    "!pip3 install blueqat openfermionblueqat openfermion"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "HHLJDoRXtXta"
   },
   "source": [
    "##2-3-2 Coding\n",
    "Here we use a prepared data provided by openfermion."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 283
    },
    "colab_type": "code",
    "id": "hDacEDd6tVIK",
    "outputId": "cdb33658-119e-4c37-caf6-04d06ba417b7"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f34c6b411c0>]"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from blueqat import *\n",
    "from openfermion import *\n",
    "from openfermionblueqat import*\n",
    "import numpy as np\n",
    "\n",
    "def get_molecule(bond_len):\n",
    "  geometry = [('H',(0.,0.,0.)),('H',(0.,0.,bond_len))]\n",
    "\n",
    "  description = format(bond_len)\n",
    "  molecule = MolecularData(geometry, \"sto-3g\",1,description=description)\n",
    "\n",
    "  molecule.load()\n",
    "  return molecule\n",
    "\n",
    "x = [];e=[];fullci=[]\n",
    "for bond_len in np.arange(0.2,2.5,0.1):\n",
    "  m = get_molecule(\"{:.2}\".format(bond_len))\n",
    "  h = bravyi_kitaev(get_fermion_operator(m.get_molecular_hamiltonian()))\n",
    "  runner = vqe.Vqe(UCCAnsatz(h,6,Circuit().x[0]))\n",
    "  result = runner.run()\n",
    "  x.append(bond_len)\n",
    "  e.append(runner.ansatz.get_energy_sparse(result.circuit))\n",
    "  fullci.append(m.fci_energy)\n",
    "\n",
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "plt.plot(x,fullci)\n",
    "plt.plot(x,e,\"o\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "XC1xaDwbtcxb"
   },
   "source": [
    "We got a result on the H2 problem."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "3vw6h4eytfK2"
   },
   "source": [
    "##2-3-3 Checking inside the coding\n",
    "Using MoluculearData function on OpenFermion, we can use prepare data of Sto-3g on H2 molecule."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "U2xwZyAItaMu"
   },
   "outputs": [],
   "source": [
    "def get_molecule(bond_len):\n",
    "  geometry = [('H',(0.,0.,0.)),('H',(0.,0.,bond_len))]\n",
    "\n",
    "  description = format(bond_len)\n",
    "  molecule = MolecularData(geometry, \"sto-3g\",1,description=description)\n",
    "\n",
    "  molecule.load()\n",
    "  return molecule"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "jy8xNG4Etk2S"
   },
   "source": [
    "Here we select 0.4 as the bond length between atoms. We get a formulation on second quantization."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 646
    },
    "colab_type": "code",
    "id": "TO1lSYvbtikE",
    "outputId": "51191d25-cec4-4d64-a0c5-99ddeebe83d8"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "() 1.322943021475\n",
       "((0, 1), (0, 0)) -1.4820918858979102\n",
       "((1, 1), (1, 0)) -1.4820918858979102\n",
       "((2, 1), (2, 0)) -0.1187350527865787\n",
       "((3, 1), (3, 0)) -0.1187350527865787\n",
       "((0, 1), (0, 1), (0, 0), (0, 0)) 0.36843967630348756\n",
       "((0, 1), (0, 1), (2, 0), (2, 0)) 0.08225771204699692\n",
       "((0, 1), (1, 1), (1, 0), (0, 0)) 0.36843967630348756\n",
       "((0, 1), (1, 1), (3, 0), (2, 0)) 0.08225771204699692\n",
       "((0, 1), (2, 1), (0, 0), (2, 0)) 0.082257712046997\n",
       "((0, 1), (2, 1), (2, 0), (0, 0)) 0.3626667179796745\n",
       "((0, 1), (3, 1), (1, 0), (2, 0)) 0.082257712046997\n",
       "((0, 1), (3, 1), (3, 0), (0, 0)) 0.3626667179796745\n",
       "((1, 1), (0, 1), (0, 0), (1, 0)) 0.36843967630348756\n",
       "((1, 1), (0, 1), (2, 0), (3, 0)) 0.08225771204699692\n",
       "((1, 1), (1, 1), (1, 0), (1, 0)) 0.36843967630348756\n",
       "((1, 1), (1, 1), (3, 0), (3, 0)) 0.08225771204699692\n",
       "((1, 1), (2, 1), (0, 0), (3, 0)) 0.082257712046997\n",
       "((1, 1), (2, 1), (2, 0), (1, 0)) 0.3626667179796745\n",
       "((1, 1), (3, 1), (1, 0), (3, 0)) 0.082257712046997\n",
       "((1, 1), (3, 1), (3, 0), (1, 0)) 0.3626667179796745\n",
       "((2, 1), (0, 1), (0, 0), (2, 0)) 0.36266671797967454\n",
       "((2, 1), (0, 1), (2, 0), (0, 0)) 0.08225771204699726\n",
       "((2, 1), (1, 1), (1, 0), (2, 0)) 0.36266671797967454\n",
       "((2, 1), (1, 1), (3, 0), (0, 0)) 0.08225771204699726\n",
       "((2, 1), (2, 1), (0, 0), (0, 0)) 0.08225771204699728\n",
       "((2, 1), (2, 1), (2, 0), (2, 0)) 0.38272169831413727\n",
       "((2, 1), (3, 1), (1, 0), (0, 0)) 0.08225771204699728\n",
       "((2, 1), (3, 1), (3, 0), (2, 0)) 0.38272169831413727\n",
       "((3, 1), (0, 1), (0, 0), (3, 0)) 0.36266671797967454\n",
       "((3, 1), (0, 1), (2, 0), (1, 0)) 0.08225771204699726\n",
       "((3, 1), (1, 1), (1, 0), (3, 0)) 0.36266671797967454\n",
       "((3, 1), (1, 1), (3, 0), (1, 0)) 0.08225771204699726\n",
       "((3, 1), (2, 1), (0, 0), (1, 0)) 0.08225771204699728\n",
       "((3, 1), (2, 1), (2, 0), (3, 0)) 0.38272169831413727\n",
       "((3, 1), (3, 1), (1, 0), (1, 0)) 0.08225771204699728\n",
       "((3, 1), (3, 1), (3, 0), (3, 0)) 0.38272169831413727"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m = get_molecule(0.4)\n",
    "m.get_molecular_hamiltonian()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "MCcUSNvltukv"
   },
   "source": [
    "This is not a hamiltonian with pauli operator, so we need a transformation."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "sP1KphQktw8Z"
   },
   "source": [
    "##2-3-4 Transformation\n",
    "\n",
    "Bravi-Kitaev transformation or Jordan–Wigner transformation are famous. Now we use the former BK transformation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 272
    },
    "colab_type": "code",
    "id": "3B3loOfutn2j",
    "outputId": "99f7f869-64b7-4297-ac30-52d52b4147ff"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(0.7407724940116754+0j) [] +\n",
      "(0.041128856023498556+0j) [X0 Z1 X2] +\n",
      "(0.041128856023498556+0j) [X0 Z1 X2 Z3] +\n",
      "(0.041128856023498556+0j) [Y0 Z1 Y2] +\n",
      "(0.041128856023498556+0j) [Y0 Z1 Y2 Z3] +\n",
      "(0.23528824284103544+0j) [Z0] +\n",
      "(0.23528824284103542+0j) [Z0 Z1] +\n",
      "(0.18133335898983727+0j) [Z0 Z1 Z2] +\n",
      "(0.18133335898983727+0j) [Z0 Z1 Z2 Z3] +\n",
      "(0.14020450296633868+0j) [Z0 Z2] +\n",
      "(0.14020450296633868+0j) [Z0 Z2 Z3] +\n",
      "(0.18421983815174378+0j) [Z1] +\n",
      "(-0.45353118471995524+0j) [Z1 Z2 Z3] +\n",
      "(0.19136084915706864+0j) [Z1 Z3] +\n",
      "(-0.45353118471995524+0j) [Z2]\n"
     ]
    }
   ],
   "source": [
    "h = bravyi_kitaev(get_fermion_operator(m.get_molecular_hamiltonian()))\n",
    "print(h)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "xWER997LuB9V"
   },
   "source": [
    "Now we got a hamiltonian with pauli operator, now we can solve it with VQE. To solve this efficiently we use UCC theory on ansatz. By using a prepared UCCansatz in blueqat we get,"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 714
    },
    "colab_type": "code",
    "id": "97RcOB7Rt4_7",
    "outputId": "730c7fd9-0eed-4349-edd0-24aae9f26ba5"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "params: [0.70164565 0.11001547] val: -0.5797580164453399\n",
      "params: [0.70164565 0.11001547] val: -0.5797580164453399\n",
      "params: [1.70164565 0.11001547] val: 0.6866759852375767\n",
      "params: [-0.91638835  0.11001547] val: -0.290721644465588\n",
      "params: [0.70164565 0.11001547] val: -0.5797580164453399\n",
      "params: [0.08361167 0.11001547] val: -0.8940999003449719\n",
      "params: [0.08361167 0.11001547] val: -0.8940999003449719\n",
      "params: [0.08361167 1.11001547] val: -0.19463031566496408\n",
      "params: [ 0.08361167 -1.50801853] val: 0.3583855877329869\n",
      "params: [0.08361167 0.11001547] val: -0.8940999003449719\n",
      "params: [ 0.08361167 -0.50801851] val: -0.709320072764051\n",
      "params: [0.08361167 0.49198147] val: -0.7381515159460594\n",
      "params: [0.08361167 0.01236402] val: -0.8991130288137822\n",
      "params: [-0.5344223  -0.08528742] val: -0.7059118687677622\n",
      "params: [0.08361167 0.01236402] val: -0.8991130288137822\n",
      "params: [-0.5344223  -0.08528742] val: -0.7059118687677622\n",
      "params: [1.08361166 0.17036738] val: -0.24407758400920698\n",
      "params: [0.08361167 0.01236402] val: -0.8991130288137822\n",
      "params: [0.46557767 0.07271593] val: -0.7505506656373621\n",
      "params: [-0.15245629 -0.02493551] val: -0.886924766960334\n",
      "params: [ 0.00179119 -0.00056389] val: -0.9043588023121355\n",
      "params: [ 0.00179119 -0.00056389] val: -0.9043588023121355\n",
      "params: [0.00179119 0.99943611] val: -0.23407346885577465\n",
      "params: [ 0.00179119 -1.61859789] val: 0.6139694789345231\n",
      "params: [ 0.00179119 -0.00056389] val: -0.9043588023121355\n",
      "params: [ 0.00179119 -0.61859786] val: -0.6333409811348125\n",
      "params: [0.00179119 0.38140211] val: -0.799593361886041\n",
      "params: [ 0.00179119 -0.00197791] val: -0.9043561565053925\n",
      "params: [1.79119297e-03 2.79368516e-05] val: -0.9043590357751042\n",
      "params: [0.00179119 0.14569991] val: -0.8889379138723625\n",
      "params: [0.00179119 0.00298706] val: -0.9043524711418675\n",
      "params: [1.79119297e-03 2.79368516e-05] val: -0.9043590357751042\n",
      "params: [-0.08002929 -0.01289997] val: -0.8995345185001391\n",
      "params: [0.13417951 0.02094574] val: -0.8908571280247335\n",
      "params: [1.79119297e-03 2.79368516e-05] val: -0.9043590357751042\n",
      "params: [0.05235903 0.00801782] val: -0.9022979207686519\n",
      "params: [-0.02946145 -0.00491009] val: -0.9037050083744771\n",
      "params: [ 4.36428587e-05 -2.48181936e-04] val: -0.9043613509785144\n",
      "params: [-0.00869411 -0.00162878] val: -0.9043038145362967\n"
     ]
    }
   ],
   "source": [
    "runner = vqe.Vqe(UCCAnsatz(h,2,Circuit().x[0]))\n",
    "result = runner.run(verbose = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "pogBuf8HuG9y"
   },
   "source": [
    "After the optimizing process we get the minimized result. This time we use UCC ansatz with Hamiltonian."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "7aogyg6YuD6S",
    "outputId": "48520c01-5ad1-4d42-fc6c-ccf395d1df9b"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-0.9043613509785144"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "runner.ansatz.get_energy_sparse(result.circuit)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "gFgZVsk6uMXf"
   },
   "source": [
    "Again this is the whole code for H2 UCCansatz"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 283
    },
    "colab_type": "code",
    "id": "uP2XQ8j3uJnr",
    "outputId": "83f553f0-957b-4660-b275-4e56a1ef0ab2"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f34c9e17850>]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from blueqat import *\n",
    "from openfermion import *\n",
    "from openfermionblueqat import*\n",
    "import numpy as np\n",
    "\n",
    "def get_molecule(bond_len):\n",
    "  geometry = [('H',(0.,0.,0.)),('H',(0.,0.,bond_len))]\n",
    "\n",
    "  description = format(bond_len)\n",
    "  molecule = MolecularData(geometry, \"sto-3g\",1,description=description)\n",
    "\n",
    "  molecule.load()\n",
    "  return molecule\n",
    "\n",
    "x = [];e=[];fullci=[]\n",
    "for bond_len in np.arange(0.2,2.5,0.1):\n",
    "  m = get_molecule(\"{:.2}\".format(bond_len))\n",
    "  h = bravyi_kitaev(get_fermion_operator(m.get_molecular_hamiltonian()))\n",
    "  runner = vqe.Vqe(UCCAnsatz(h,6,Circuit().x[0]))\n",
    "  result = runner.run()\n",
    "  x.append(bond_len)\n",
    "  e.append(runner.ansatz.get_energy_sparse(result.circuit))\n",
    "  fullci.append(m.fci_energy)\n",
    "\n",
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "plt.plot(x,fullci)\n",
    "plt.plot(x,e,\"o\")"
   ]
  }
 ],
 "metadata": {
  "colab": {
   "name": "102vqe_qaoa02.ipynb",
   "provenance": []
  },
  "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": "3.8.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}