grover_max_cut.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Grover Oracle for MAXCut probrem\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "MAXCut probrem is NP-Hard probrem. But once we define threshold as a condition, MAXCut becomes NP-Complete."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "##Mar 1, 2019\n",
    "#\n",
    "#code designed by Yasuhiro Okura and Takahiko Satoh and Miguel Ramalho and Lakshmi Prakash\n",
    "\n",
    "\n",
    "# useful additional packages \n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.axes as axes\n",
    "%matplotlib inline\n",
    "import numpy as np\n",
    "import networkx as nx\n",
    "\n",
    "\n",
    "from qiskit import QuantumCircuit, ClassicalRegister, QuantumRegister\n",
    "from qiskit import execute\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# from qiskit import IBMQ #TODO\n",
    "from qiskit import LegacySimulators as Aer\n",
    "\n",
    "\n",
    "# Import Aer\n",
    "# from qiskit import BasicAer\n",
    "\n",
    "# qr = QuantumRegister(5, 'q')\n",
    "# cr = ClassicalRegister(5, 'c')\n",
    "# qc = QuantumCircuit(qr, cr)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4\n"
     ]
    }
   ],
   "source": [
    "import random\n",
    "\n",
    "n = 4 # number of node\n",
    "edge_direct = random.randint(1, n)\n",
    "weight = 1.0\n",
    "print(edge_direct)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/Yasuhiro/anaconda3/lib/python3.7/site-packages/networkx/drawing/nx_pylab.py:611: MatplotlibDeprecationWarning: isinstance(..., numbers.Number)\n",
      "  if cb.is_numlike(alpha):\n"
     ]
    },
    {
     "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": [
    "# define edges and weight\n",
    "# elist = []\n",
    "# while len(elist)<3: \n",
    "# #     print(\"success1\")\n",
    "#     e_start = random.randint(0, n-1)#it is used to define node connection\n",
    "#     e_end = random.randint(0, n-1)#it is used to define node connection\n",
    "# #     print(\"success2\")\n",
    "    \n",
    "#     if e_start != e_end:\n",
    "#         edge = (e_start, e_end, weight)\n",
    "#         elist.append(edge)\n",
    "#         print(elist)\n",
    "\n",
    "# print(elist)\n",
    "elist=[(2,3,1.0), (0, 1, 1.0)]\n",
    "G=nx.Graph()\n",
    "G.add_nodes_from(np.arange(0, n,1))\n",
    "G.add_weighted_edges_from(elist)\n",
    "\n",
    "colors = ['r' for node in G.nodes()]\n",
    "pos = nx.spring_layout(G)\n",
    "default_axes = plt.axes(frameon=True)\n",
    "nx.draw_networkx(G, node_color=colors, node_size=600, alpha=.8, ax=default_axes, pos=pos)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n",
      "{'weight': 1.0}\n",
      "0\n",
      "0\n",
      "{'weight': 1.0}\n",
      "0\n",
      "0\n",
      "0\n",
      "0\n",
      "0\n",
      "0\n",
      "{'weight': 1.0}\n",
      "0\n",
      "0\n",
      "{'weight': 1.0}\n",
      "0\n",
      "[[0. 1. 0. 0.]\n",
      " [1. 0. 0. 0.]\n",
      " [0. 0. 0. 1.]\n",
      " [0. 0. 1. 0.]]\n"
     ]
    }
   ],
   "source": [
    "# Computing the weight matrix from the random graph\n",
    "w = np.zeros([n,n])\n",
    "for i in range(n):\n",
    "    for j in range(n):\n",
    "        temp = G.get_edge_data(i,j,default=0)\n",
    "        print(temp)\n",
    "        if temp != 0:\n",
    "            w[i,j] = temp[\"weight\"] \n",
    "print(w)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[(0, 1), (2, 3)]\n",
      "2 3\n",
      "0 1\n"
     ]
    }
   ],
   "source": [
    "edges = list(G.edges)\n",
    "print(edges)\n",
    "\n",
    "while edges:\n",
    "    i, j = edges.pop()\n",
    "#     i = int(i)\n",
    "    print(i, j)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "## difine circuit\n",
    "\n",
    "num_x = n # num of node\n",
    "num_a = 1\n",
    "num_b = 2\n",
    "num_c = 1\n",
    "\n",
    "x = QuantumRegister(num_x, \"x\")\n",
    "a = QuantumRegister(num_a, \"a\")\n",
    "b = QuantumRegister(num_b, \"b\")\n",
    "c = QuantumRegister(num_c, \"c\")\n",
    "\n",
    "cr = ClassicalRegister(4, \"cr\")\n",
    "qc = QuantumCircuit(x, a ,b, c,  cr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 301x740.46 with 1 Axes>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#initialize graph weight\n",
    "#If Threshold >=2 : SUCCESS!!\n",
    "\n",
    "qc = QuantumCircuit(x, a ,b, c,  cr)\n",
    "\n",
    "\n",
    "# qc.x(x[0])\n",
    "# qc.x(x[1])\n",
    "# qc.x(x[2])\n",
    "# qc.x(x[3])\n",
    "\n",
    "qc.h(x[0])\n",
    "qc.h(x[1])\n",
    "qc.h(x[2])\n",
    "qc.h(x[3])\n",
    "\n",
    "qc.h(c[0])\n",
    "\n",
    "qc.barrier()\n",
    "qc.draw(output = \"mpl\")\n",
    "\n",
    "\n",
    "\n",
    "# colors = []\n",
    "# colors = ['r' if xbest_brute[i] == 0 else 'b' for i in range(n)]\n",
    "# nx.draw_networkx(G, node_color=colors, node_size=600, alpha=.8, pos=pos)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[(0, 1), (2, 3)]\n",
      "4\n",
      "0 1\n",
      "success!\n",
      "2 3\n",
      "success!\n",
      "2 3\n",
      "success!\n",
      "0 1\n",
      "success!\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1384.6x2305.66 with 1 Axes>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "edges = list(G.edges)\n",
    "print(edges)\n",
    "\n",
    "## V part(Oracle)\n",
    "def cut(qc, x, a, b, i, j):\n",
    "    \n",
    "    qc.cx(x[i], a)\n",
    "    qc.cx(x[j], a)\n",
    "\n",
    "    qc.ccx(a[0], b[0], b[1])\n",
    "    qc.cx(a[0], b[0])\n",
    "\n",
    "    qc.cx(x[j], a)\n",
    "    qc.cx(x[i], a)\n",
    "    \n",
    "## V inverse\n",
    "def icut(qc, x, a, b, i, j):\n",
    "    \n",
    "    qc.cx(x[i], a)\n",
    "    qc.cx(x[j], a)\n",
    "\n",
    "    qc.cx(a[0], b[0])\n",
    "    qc.ccx(a[0], b[0], b[1])\n",
    "    \n",
    "    qc.cx(x[j], a)\n",
    "    qc.cx(x[i], a)\n",
    "    \n",
    "    \n",
    "def cnx(qc,*qubits): # C^n-NOT gate\n",
    "    if len(qubits) > 3:\n",
    "        last = qubits[-1]\n",
    "        #A matrix: (made up of a  and Y rotation, lemma4.3)\n",
    "        qc.crz(np.pi/2, qubits[-2], qubits[-1])\n",
    "        #cry\n",
    "        qc.cu3(np.pi/2, 0, 0, qubits[-2],qubits[-1])\n",
    "        \n",
    "        #Control not gate\n",
    "        cnx(qc,*qubits[:-2],qubits[-1])\n",
    "        \n",
    "        #B matrix (cry again, but opposite angle)\n",
    "        qc.cu3(-np.pi/2, 0, 0, qubits[-2], qubits[-1])\n",
    "        \n",
    "        #Control\n",
    "        cnx(qc,*qubits[:-2],qubits[-1])\n",
    "        \n",
    "        #C matrix (final rotation)\n",
    "        qc.crz(-np.pi/2,qubits[-2],qubits[-1])\n",
    "    elif len(qubits)==3:\n",
    "        qc.ccx(*qubits)\n",
    "    elif len(qubits)==2:\n",
    "        qc.cx(*qubits)\n",
    "\n",
    "print(num_x)\n",
    "## D part(Diffusion matrix)\n",
    "def diff(qc, N): # Diffusion matrix\n",
    "    for k in range(N):\n",
    "        qc.h(x[k])\n",
    "        qc.x(x[k])\n",
    "\n",
    "    \n",
    "\n",
    "#     qc.h(x[3])\n",
    "    qc.h(x[3])\n",
    "#     cnx(qc, x[0], x[1], x[2], x[3])\n",
    "    cnx(qc, x[0], x[1], x[2], x[3])\n",
    "#     qc.h(x[3])\n",
    "    qc.h(x[3])\n",
    "\n",
    "    for k in range(N):\n",
    "        qc.x(x[k])\n",
    "        qc.h(x[k])\n",
    "\n",
    "#         qc.h(x[k])\n",
    "#         qc.x(x[k])\n",
    "\n",
    "\n",
    "# print(round(np.log(num_x))) # How many times execute V and D\n",
    "# for l in np.arange(round(np.log(num_x))):\n",
    "\n",
    "\n",
    "\n",
    "for l in np.arange(1):\n",
    "    qc.barrier()\n",
    "    for e in edges:\n",
    "        i, j = e\n",
    "        print(i, j)\n",
    "        cut(qc, x, a, b, int(i), int(j)) # when we should apply int ? Is this right timing?\n",
    "        print(\"success!\")\n",
    "    \n",
    "    qc.barrier()\n",
    "    qc.cz(b[1], c[0])\n",
    "    qc.barrier()\n",
    "    \n",
    "    for e in edges[::-1]:\n",
    "        i, j = e\n",
    "        print(i, j)\n",
    "        icut(qc, x, a, b, int(i), int(j)) # when we should apply int ? Is this right timing?\n",
    "        print(\"success!\")\n",
    "    \n",
    "    qc.barrier()\n",
    "    diff(qc, num_x)\n",
    "    qc.barrier()\n",
    "\n",
    "\n",
    "qc.barrier()\n",
    "\n",
    "qc.h(c[0])\n",
    "## measurement\n",
    "for l in range(num_x):\n",
    "    qc.measure(x[l], cr[l])\n",
    "\n",
    "\n",
    "# qc.measure(a[0], cr[4])\n",
    "# for m in range(num_a):\n",
    "#     qc.measure(a[1], cr[num_x+m-1])\n",
    "\n",
    "\n",
    "qc.draw(output = \"mpl\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Running on QASM simulater"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "from qiskit.tools.visualization import plot_histogram"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "############ running on QASM simulater ############\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/Yasuhiro/anaconda3/lib/python3.7/site-packages/qiskit/providers/legacysimulators/legacyprovider.py:41: DeprecationWarning: Simulators in the LegacySimulators provider are deprecated. If you want\n",
      " maximum compatibility, you can use those in the Simulators provider. If you want to make the most\n",
      " of performance, consider installing the Qiskit Aer element and user the Aer provider.\n",
      "  warnings.warn(DEPRECATION_MSG, DeprecationWarning)\n",
      "/Users/Yasuhiro/anaconda3/lib/python3.7/site-packages/qiskit/providers/legacysimulators/legacyprovider.py:37: DeprecationWarning: Simulators in the LegacySimulators provider are deprecated. If you want\n",
      " maximum compatibility, you can use those in the Simulators provider. If you want to make the most\n",
      " of performance, consider installing the Qiskit Aer element and user the Aer provider.\n",
      "  warnings.warn(DEPRECATION_MSG, DeprecationWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('0110', 119)\n",
      "{'1100': 40, '0010': 45, '1000': 44, '1001': 106, '0000': 50, '1010': 104, '0111': 49, '0011': 47, '0100': 62, '1101': 58, '1011': 44, '0001': 52, '1110': 51, '1111': 42, '0110': 119, '0101': 111}\n"
     ]
    }
   ],
   "source": [
    "print(\"############ running on QASM simulater ############\")\n",
    "\n",
    "backend = Aer.get_backend('qasm_simulator')\n",
    "## StateVector Sim\n",
    "# Run the quantum circuit on a statevector simulator backend\n",
    "# backend = BasicAer.get_backend('statevector_simulator')\n",
    "# job_sim = execute(qc, backend=backend)\n",
    "# result = job_sim.result()\n",
    "# outputstate = result.get_statevector(qc, decimals=3)\n",
    "\n",
    "# print(outputstate)\n",
    "\n",
    "job_sim = execute(qc, backend=backend)\n",
    "result = job_sim.result()\n",
    "count = result.get_counts(qc)\n",
    "print(sorted(count.items(), key = lambda x: x[1],reverse=True)[0])\n",
    "print(count)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 504x360 with 1 Axes>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# from qiskit.tools.visualization import plot_state_city\n",
    "# plot_state_city(outputstate)\n",
    "plot_histogram(count)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Runnning on IBMQ"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "############ running on IBMQ  ############\n"
     ]
    }
   ],
   "source": [
    "# TODO\n",
    "print(\"############ running on IBMQ  ############\")"
   ]
  }
 ],
 "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": "3.7.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}