Statistics.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/Riley/anaconda3/lib/python3.6/site-packages/marshmallow/schema.py:364: ChangedInMarshmallow3Warning: strict=False is not recommended. In marshmallow 3.0, schemas will always be strict. See https://marshmallow.readthedocs.io/en/latest/upgrading.html#schemas-are-always-strict\n",
      "  ChangedInMarshmallow3Warning\n"
     ]
    }
   ],
   "source": [
    "from qiskit import QuantumRegister, ClassicalRegister, QuantumCircuit\n",
    "from qiskit import IBMQ\n",
    "from qiskit import BasicAer, execute\n",
    "from qiskit.providers.aer import QasmSimulator\n",
    "from qiskit.tools.visualization import plot_histogram\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import numpy.random as rand\n",
    "import networkx as nx\n",
    "from random_graph import random_graph\n",
    "from itertools import product"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<qiskit.circuit.instructionset.InstructionSet at 0x120521588>"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#take a state vector and turn it into an array for \n",
    "vertices=2\n",
    "colors=2\n",
    "qubits=vertices*colors\n",
    "#Assume qubits (0,1) represent (red0,blue0) and (2,3) represent (red1,blue1) \n",
    "\n",
    "#lists of q and c registers\n",
    "qr=QuantumRegister(qubits)\n",
    "cr=ClassicalRegister(qubits)\n",
    "#for i in range(vertices):\n",
    "#    #make a list of quantum registers for the qubits in each vertex with a qubit in each register for each color\n",
    "#    vertex_q_registers.append(QuantumRegister(colors))\n",
    "#    vertext_c_registers.append(ClassicalRegister(colors))\n",
    "    \n",
    "circ=QuantumCircuit(qr,cr)\n",
    "#circ=QuantumCircuit(vertex_q_regs[i] for i in range(vertices),vertex_c_regs[i] for i in range(vertices))\n",
    "#generage some sample state\n",
    "for i in range(qubits):\n",
    "    circ.h(qr[i])\n",
    "\n",
    "circ.measure(qr,cr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/Riley/anaconda3/lib/python3.6/site-packages/marshmallow/schema.py:364: ChangedInMarshmallow3Warning: strict=False is not recommended. In marshmallow 3.0, schemas will always be strict. See https://marshmallow.readthedocs.io/en/latest/upgrading.html#schemas-are-always-strict\n",
      "  ChangedInMarshmallow3Warning\n"
     ]
    }
   ],
   "source": [
    "backend_sim=BasicAer.get_backend('qasm_simulator')\n",
    "result=execute(circ, backend_sim).result()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/Riley/anaconda3/lib/python3.6/site-packages/marshmallow/schema.py:364: ChangedInMarshmallow3Warning: strict=False is not recommended. In marshmallow 3.0, schemas will always be strict. See https://marshmallow.readthedocs.io/en/latest/upgrading.html#schemas-are-always-strict\n",
      "  ChangedInMarshmallow3Warning\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 504x360 with 1 Axes>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "counts=result.get_counts(circ)\n",
    "plot_histogram(counts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "#for n qubits what construct the hamming weight 1 binary strings\n",
    "#data={'1001':30,'1101':11,'0101':15}\n",
    "data=counts\n",
    "bad_strings=[]\n",
    "\n",
    "good_data={}\n",
    "dumpster={'bad':0}\n",
    "\n",
    "for k,v in data.items():\n",
    "    for j in range(vertices):\n",
    "        if k[j*colors:(j+1)*colors].count('1') != 1 and k not in bad_strings:\n",
    "            bad_strings.append(k)\n",
    "\n",
    "for i in bad_strings:\n",
    "    dumpster['bad'] += data[i]\n",
    "    \n",
    "for k,v in data.items():\n",
    "    if k not in bad_strings:\n",
    "        good_data[k]=v\n",
    "\n",
    "color_data={}\n",
    "if colors==2:\n",
    "    for k,v in good_data.items():\n",
    "        color_code=''\n",
    "        for j in range(vertices):\n",
    "            if k[j*colors:(j+1)*colors]=='01':\n",
    "                color_code+='0'\n",
    "            else:\n",
    "                color_code+='1'\n",
    "        color_data[k]=color_code\n",
    "elif colors==3:\n",
    "    for k,v in good_data.items():\n",
    "        color_code=''\n",
    "        for j in range(vertices):\n",
    "            if k[j*colors:(j+1)*colors]=='001':\n",
    "                color_code+='0'\n",
    "            elif k[j*colors:(j+1)*colors]=='010':\n",
    "                color_code+='1'\n",
    "            else:\n",
    "                color_code+='2'\n",
    "        color_data[k]=color_code\n",
    "elif colors==4:\n",
    "    for k,v in good_data.items():\n",
    "        color_code=''\n",
    "        for j in range(vertices):\n",
    "            if k[j*colors:(j+1)*colors]=='0001':\n",
    "                color_code+='0'\n",
    "            elif k[j*colors:(j+1)*colors]=='0010':\n",
    "                color_code+='1'\n",
    "            elif k[j*colors:(j+1)*colors]=='0010':\n",
    "                color_code+='2'\n",
    "            else:\n",
    "                color_code+='3'\n",
    "        color_data[k]=color_code\n",
    "\n",
    "best_solution=max(good_data, key=good_data.get)\n",
    "best_color=color_data[best_solution]\n",
    "\n",
    "total_data={**good_data,**dumpster}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x360 with 1 Axes>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plot_histogram(total_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#graph=random_graph(vertices,edge_prob=1)\n",
    "#G = nx.from_numpy_matrix(graph) \n",
    "color_map=[]\n",
    "for node in G:\n",
    "    if best_color[node]=='0':\n",
    "        color_map.append('red')\n",
    "    elif best_color[node]=='1':\n",
    "        color_map.append('blue')\n",
    "    elif best_color[node]=='2':\n",
    "        color_map.append('green')\n",
    "    elif best_color[node]=='3':\n",
    "        color_map.append('yellow')\n",
    "        \n",
    "nx.draw(G,node_color=color_map, with_labels=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "def generate_histogram(vertices,colors,counts):\n",
    "    data=counts\n",
    "    bad_strings=[]\n",
    "\n",
    "    #bin_string_list = [''.join(p) for p in product('10', repeat=N)]\n",
    "    #bin_string_list.sort(key=lambda s: s.count('1'))\n",
    "    # ex. N=3bits ['000', '100', '010', '001', '110', '101', '011', '111']\n",
    "    good_data={}\n",
    "    dumpster={'bad':0}\n",
    "    \n",
    "    for k,v in data.items():\n",
    "        for j in range(vertices):\n",
    "            if k[j*colors:(j+1)*colors].count('1') != 1 and k not in bad_strings:\n",
    "                bad_strings.append(k)\n",
    "    \n",
    "    for i in bad_strings:\n",
    "        dumpster['bad'] += data[i]\n",
    "        \n",
    "                                         \n",
    "\n",
    "    for k,v in data.items():\n",
    "        if k not in bad_strings:\n",
    "            good_data[k]=v\n",
    "    \n",
    "\n",
    "    color_data={}\n",
    "    if colors==2:\n",
    "        for k,v in good_data.items():\n",
    "            color_code=''\n",
    "            for j in range(vertices):\n",
    "                if k[j*colors:(j+1)*colors]=='01':\n",
    "                    color_code+='0'\n",
    "                else:\n",
    "                    color_code+='1'\n",
    "            color_data[k]=color_code\n",
    "    elif colors==3:\n",
    "        for k,v in good_data.items():\n",
    "            color_code=''\n",
    "            for j in range(vertices):\n",
    "                if k[j*colors:(j+1)*colors]=='001':\n",
    "                    color_code+='0'\n",
    "                elif k[j*colors:(j+1)*colors]=='010':\n",
    "                    color_code+='1'\n",
    "                else:\n",
    "                    color_code+='2'\n",
    "            color_data[k]=color_code\n",
    "    elif colors==4:\n",
    "        for k,v in good_data.items():\n",
    "            color_code=''\n",
    "            for j in range(vertices):\n",
    "                if k[j*colors:(j+1)*colors]=='0001':\n",
    "                    color_code+='0'\n",
    "                elif k[j*colors:(j+1)*colors]=='0010':\n",
    "                    color_code+='1'\n",
    "                elif k[j*colors:(j+1)*colors]=='0010':\n",
    "                    color_code+='2'\n",
    "                else:\n",
    "                    color_code+='3'\n",
    "            color_data[k]=color_code\n",
    "    try:\n",
    "        best_solution=max(good_data, key=good_data.get)\n",
    "        best_color=color_data[best_solution]\n",
    "\n",
    "    except ValueError:\n",
    "        print('no solutions in dataset')\n",
    "    \n",
    "    total_data={**good_data,**dumpster}\n",
    "    print(total_data)\n",
    "    plot_histogram(total_data)\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "no solutions in dataset\n",
      "{'bad': 1024}\n"
     ]
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "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": "3.6.8"
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 },
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}