QNIM Dynamic .ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "3cee9c01",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<AccountProvider for IBMQ(hub='ibm-q', group='open', project='main')>,\n",
       " <AccountProvider for IBMQ(hub='ibm-q-community', group='qamp', project='fall-22-29')>,\n",
       " <AccountProvider for IBMQ(hub='ibm-q-community', group='qhack-hackathon', project='7-qubit')>,\n",
       " <AccountProvider for IBMQ(hub='q-summer-school', group='main', project='project-2')>]"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "from qiskit import BasicAer, QuantumCircuit, QuantumRegister, ClassicalRegister, execute\n",
    "from qiskit import IBMQ\n",
    "provider = IBMQ.load_account()\n",
    "from collections import OrderedDict\n",
    "from operator import itemgetter\n",
    "IBMQ.providers()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "4124a507",
   "metadata": {},
   "outputs": [],
   "source": [
    "# provider = IBMQ.load_account()\n",
    "# provider.backends()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "0595620f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<IBMQSimulator('ibmq_qasm_simulator') from IBMQ(hub='ibm-q-community', group='qamp', project='fall-22-29')>,\n",
       " <IBMQBackend('ibmq_lima') from IBMQ(hub='ibm-q-community', group='qamp', project='fall-22-29')>,\n",
       " <IBMQBackend('ibmq_belem') from IBMQ(hub='ibm-q-community', group='qamp', project='fall-22-29')>,\n",
       " <IBMQBackend('ibmq_quito') from IBMQ(hub='ibm-q-community', group='qamp', project='fall-22-29')>,\n",
       " <IBMQBackend('ibmq_guadalupe') from IBMQ(hub='ibm-q-community', group='qamp', project='fall-22-29')>,\n",
       " <IBMQBackend('ibmq_jakarta') from IBMQ(hub='ibm-q-community', group='qamp', project='fall-22-29')>,\n",
       " <IBMQBackend('ibmq_manila') from IBMQ(hub='ibm-q-community', group='qamp', project='fall-22-29')>,\n",
       " <IBMQBackend('ibm_lagos') from IBMQ(hub='ibm-q-community', group='qamp', project='fall-22-29')>,\n",
       " <IBMQBackend('ibm_nairobi') from IBMQ(hub='ibm-q-community', group='qamp', project='fall-22-29')>,\n",
       " <IBMQBackend('ibm_perth') from IBMQ(hub='ibm-q-community', group='qamp', project='fall-22-29')>,\n",
       " <IBMQBackend('ibm_oslo') from IBMQ(hub='ibm-q-community', group='qamp', project='fall-22-29')>]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "provider = IBMQ.get_provider(hub='ibm-q-community', group='qamp', project='fall-22-29')\n",
    "provider.backends()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "aa53daae",
   "metadata": {},
   "outputs": [],
   "source": [
    "def misere_step(ones,piles):\n",
    "    # even number of piles of 1 eg (1,1,3,0) or (0,0,3,0)\n",
    "    if ones%2 == 0:\n",
    "        objects_to_remove = []\n",
    "        removable_amount = 1\n",
    "        for i in range(len(piles)):\n",
    "            if piles[i] > 1:\n",
    "                objects_to_remove.append(piles[i]-1)\n",
    "            else:\n",
    "                objects_to_remove.append(0)\n",
    "    # odd number of piles of 1 eg (1,1,3,1)\n",
    "    else:\n",
    "        objects_to_remove = []\n",
    "        removable_amount = 1\n",
    "        for i in range(len(piles)):\n",
    "            if piles[i] > 1:\n",
    "                objects_to_remove.append(piles[i])\n",
    "            else:\n",
    "                objects_to_remove.append(0)\n",
    "    return objects_to_remove, removable_amount\n",
    "\n",
    "def get_piles_to_remove(piles):\n",
    "    nim_sum = 0\n",
    "    for p in piles:\n",
    "        nim_sum = nim_sum ^ p\n",
    "    objects_to_remove = []\n",
    "    removable_amount = 0\n",
    "    for p in piles:\n",
    "        new_p = p^nim_sum\n",
    "        if new_p < p:\n",
    "            objects_to_remove.append(p-new_p)\n",
    "            removable_amount = removable_amount + 1\n",
    "        else:\n",
    "            objects_to_remove.append(0)\n",
    "    return objects_to_remove, removable_amount\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3971f6ff",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "5efff069",
   "metadata": {},
   "outputs": [],
   "source": [
    "def custom_qft(data_qubits):\n",
    "    qr_data = QuantumRegister(data_qubits)\n",
    "    qc = QuantumCircuit(qr_data)\n",
    "    i = data_qubits\n",
    "    while i>=1:\n",
    "        n = i - 1\n",
    "        qc.h(qr_data[n]) \n",
    "        for qubit in range(n):\n",
    "            qc.cp(np.pi/2**(n-qubit), qr_data[qubit], qr_data[n])\n",
    "        i = i-1\n",
    "    return qc\n",
    "\n",
    "def subroutine_add_const(data_qubits: int, const: int, to_gate=True):\n",
    "    qc = QuantumCircuit(data_qubits)\n",
    "    for i in range(data_qubits):\n",
    "        angle = const*np.pi/(2**i)\n",
    "        qc.p(angle,i)\n",
    "    return qc.to_gate(label=\" [\"+str(const)+\"] \") if to_gate else qc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "2a66e023",
   "metadata": {},
   "outputs": [],
   "source": [
    "def diffusion_operation(qc, address, flag, removable_pile):\n",
    "    def nim_oracle(qc,address,flag,removable_pile):\n",
    "\n",
    "        # 0001 -> 001\n",
    "        if removable_pile[0] != 0:\n",
    "            print(\"Removable Pile 0\")\n",
    "            qc.x(address[1])\n",
    "            qc.x(address[2])\n",
    "            qc.mct(address[:],flag)\n",
    "            qc.x(address[2])\n",
    "            qc.x(address[1])\n",
    "        \n",
    "        # 0010 -> 010\n",
    "        if removable_pile[1] != 0:\n",
    "            print(\"Removable Pile 1\")\n",
    "            qc.x(address[0])\n",
    "            qc.x(address[2])\n",
    "            qc.mct(address[:],flag)\n",
    "            qc.x(address[2])\n",
    "            qc.x(address[0])\n",
    "    \n",
    "        # 0100 -> 011\n",
    "        if removable_pile[2] != 0:\n",
    "            print(\"Removable Pile 2\")\n",
    "            qc.x(address[2])\n",
    "            qc.mct(address[:],flag)\n",
    "            qc.x(address[2])\n",
    "        \n",
    "        # 1000 -> 100\n",
    "        if removable_pile[3] != 0:\n",
    "            print(\"Removable Pile 3\")\n",
    "            qc.x(address[0])\n",
    "            qc.x(address[1])\n",
    "            qc.mct(address[:],flag)\n",
    "            qc.x(address[1])\n",
    "            qc.x(address[0])\n",
    "\n",
    "\n",
    "    qc.x(flag)\n",
    "    qc.h(flag)\n",
    "\n",
    "    qc.h(address[:])\n",
    "    nim_oracle(qc,address,flag,removable_pile)\n",
    "    qc.h(address[:])\n",
    "    qc.x(address[:])\n",
    "    qc.h(address[2])\n",
    "    qc.mct(address[0:2], address[2])\n",
    "    qc.h(address[2])\n",
    "    qc.x(address[:])\n",
    "    qc.h(address[:])\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "c4f8d8d3",
   "metadata": {},
   "outputs": [],
   "source": [
    "def qc_process(qc,objects_to_remove,address,removable_pile,removable_count):\n",
    "    \n",
    "    if removable_count == 0:\n",
    "        for i in range(len(removable_pile)):\n",
    "            if piles[i] > 0:\n",
    "                removable_pile[i] = 1\n",
    "                removable_count += 1\n",
    "\n",
    "    if removable_count == 4:\n",
    "        removable_pile[removable_pile.index(min(removable_pile))] = 0\n",
    "        removable_count = removable_count - 1\n",
    "    \n",
    "    qft_gate = custom_qft(3).to_gate()\n",
    "    inverse_qft_gate = custom_qft(3).inverse().to_gate()\n",
    "\n",
    "    if removable_count == 1:\n",
    "        qc.swap(objects_to_remove[0],objects_to_remove[2])\n",
    "        qc.append(qft_gate,objects_to_remove[:])\n",
    "        # 0001 -> 001\n",
    "        if removable_pile[0] != 0:\n",
    "            add_gate = subroutine_add_const(3,removable_pile[0])\n",
    "            qc.x(address[0])\n",
    "        # 0010 -> 010\n",
    "        elif removable_pile[1] != 0:\n",
    "            add_gate = subroutine_add_const(3,removable_pile[1])\n",
    "            qc.x(address[1])\n",
    "        # 0100 -> 011\n",
    "        elif removable_pile[2] != 0:\n",
    "            add_gate = subroutine_add_const(3,removable_pile[2])\n",
    "            qc.x(address[0])\n",
    "            qc.x(address[1])\n",
    "        # 1000 -> 100\n",
    "        elif removable_pile[3] != 0:\n",
    "            add_gate = subroutine_add_const(3,removable_pile[3])\n",
    "            qc.x(address[2])\n",
    "\n",
    "        qc.append(add_gate,objects_to_remove[:])\n",
    "        qc.append(inverse_qft_gate,objects_to_remove[:])\n",
    "        qc.swap(objects_to_remove[0],objects_to_remove[2])\n",
    "\n",
    "    else:\n",
    "        diffusion_operation(qc,address, flag, removable_pile)\n",
    "        qc.swap(objects_to_remove[0],objects_to_remove[2])\n",
    "        qc.append(qft_gate,objects_to_remove[:])\n",
    "        for i,remove_amount in enumerate(removable_pile):\n",
    "            if remove_amount != 0:\n",
    "\n",
    "                bin_i = list(bin(i+1)[2:])\n",
    "                while len(bin_i) != 3:\n",
    "                    bin_i.insert(0,'0')\n",
    "                bin_i = bin_i[::-1]\n",
    "                for j in range(len(bin_i)):\n",
    "                    if bin_i[j] == '0':\n",
    "                        qc.x(address[j])\n",
    "\n",
    "                controlled_add_gate = subroutine_add_const(3,remove_amount).control(3)    \n",
    "                qc.append(controlled_add_gate,address[:]+objects_to_remove[:])\n",
    "\n",
    "                for j in range(len(bin_i)):\n",
    "                    if bin_i[j] == '0':\n",
    "                        qc.x(address[j])\n",
    "\n",
    "        qc.append(inverse_qft_gate,objects_to_remove[:])\n",
    "        qc.swap(objects_to_remove[0],objects_to_remove[2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "b29e2d9c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0, 2, 2, 2] 3\n",
      "Removable Pile 1\n",
      "Removable Pile 2\n",
      "Removable Pile 3\n"
     ]
    }
   ],
   "source": [
    "## check misere_step\n",
    "\n",
    "# piles = [1,3,4,5]\n",
    "piles = [1,2,6,7]\n",
    "# piles = [6,6,7,6]\n",
    "# piles = [1,3,4,7]\n",
    "# piles = [0,0,7,7]\n",
    "# piles = [1,3,2,0]\n",
    "# piles = [1,2,4,7]\n",
    "# piles = [1,0,0,4]\n",
    "\n",
    "ones = piles.count(1)\n",
    "zeros = piles.count(0)\n",
    "non_zeros = 4 - (ones+zeros)\n",
    "\n",
    "# all zeros except one eg (0,0,0,7) OR some zeros some ones some non_zeros\n",
    "# leave odd piles of 1s\n",
    "if non_zeros == 1: \n",
    "    removable_pile, removable_count = misere_step(ones, piles) \n",
    "else:\n",
    "    removable_pile, removable_count = get_piles_to_remove(piles)\n",
    "print(removable_pile, removable_count)\n",
    "\n",
    "objects_to_remove = QuantumRegister(3,'piles')\n",
    "flag = QuantumRegister(1,'flag')\n",
    "output_piles = ClassicalRegister(3,'final_piles')\n",
    "address = QuantumRegister(3,'address')\n",
    "pick_pile = ClassicalRegister(3,'choose_pile')\n",
    "qc = QuantumCircuit(objects_to_remove,address,flag,output_piles,pick_pile)\n",
    "qc_process(qc,objects_to_remove,address,removable_pile,removable_count)\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e6955c33",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "29622c08",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "a59947eb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<qiskit.circuit.instructionset.InstructionSet at 0x253bb1b8580>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "qc.measure(address[:],pick_pile[:])\n",
    "qc.measure(objects_to_remove[:],output_piles[:])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "fcb77be8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "25\n"
     ]
    }
   ],
   "source": [
    "print(qc.depth())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "25d88553",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'010 010': 292,\n",
       " '100 010': 271,\n",
       " '111 000': 40,\n",
       " '011 010': 277,\n",
       " '000 000': 39,\n",
       " '101 000': 37,\n",
       " '110 000': 36,\n",
       " '001 000': 32}"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "backend = BasicAer.get_backend('qasm_simulator')\n",
    "# backend = provider.backends.ibmq_qasm_simulator\n",
    "# backend = provider.get_backend('ibm_nairobi')\n",
    "job = execute(qc,backend,shots=1024)\n",
    "result = job.result()\n",
    "counts = result.get_counts()\n",
    "qc_move = (counts.most_frequent())\n",
    "counts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "893869b9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 504x360 with 1 Axes>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from qiskit.tools.visualization import plot_histogram\n",
    "plot_histogram(counts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "52f1db5a",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-12-25bb87d63097>:1: DeprecationWarning: The `backends` provider attribute is deprecated. Please use `provider.backend` (singular) instead. You can continue to use `provider.backends()` to retrieve all backends.\n",
      "  backend = provider.backends.ibmq_qasm_simulator\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "OrderedDict([('100 010', 297), ('011 010', 282), ('010 010', 280), ('110 000', 43), ('000 000', 37), ('001 000', 32), ('111 000', 27), ('101 000', 26)])\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "OrderedDict([('100 010', 297),\n",
       "             ('011 010', 282),\n",
       "             ('010 010', 280),\n",
       "             ('110 000', 43),\n",
       "             ('000 000', 37),\n",
       "             ('001 000', 32),\n",
       "             ('111 000', 27),\n",
       "             ('101 000', 26)])"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "backend = provider.backends.ibmq_qasm_simulator\n",
    "job = execute(qc,backend,shots=1024)\n",
    "result = job.result()\n",
    "counts = result.get_counts()\n",
    "counts = OrderedDict(sorted(counts.items(), key=itemgetter(1), reverse=True)) # sort by counts\n",
    "print(counts)\n",
    "counts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2105f8ed",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "16d71ebf",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "db3ad0d1",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8d991331",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "27a7e23d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 504x360 with 1 Axes>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from qiskit.tools.visualization import plot_histogram\n",
    "plot_histogram(counts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "63189d76",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "OrderedDict([('101 000', 34), ('100 010', 33), ('010 000', 32), ('111 000', 29), ('000 000', 28), ('011 010', 28), ('011 000', 27), ('100 110', 26), ('100 000', 26), ('010 010', 24), ('110 000', 23), ('001 000', 23), ('001 001', 23), ('110 001', 22), ('000 010', 21), ('101 100', 20), ('010 001', 20), ('110 011', 19), ('011 100', 18), ('000 011', 18), ('011 001', 17), ('000 111', 17), ('101 010', 16), ('111 111', 16), ('100 100', 16), ('001 011', 16), ('001 110', 16), ('101 001', 16), ('110 010', 15), ('011 101', 15), ('110 100', 15), ('011 110', 15), ('101 011', 14), ('001 101', 14), ('010 011', 14), ('010 100', 14), ('000 001', 13), ('100 001', 13), ('001 010', 13), ('110 111', 13), ('111 100', 13), ('111 010', 13), ('100 011', 13), ('010 111', 12), ('001 100', 12), ('000 100', 12), ('100 111', 11), ('111 101', 11), ('101 101', 11), ('011 011', 11), ('101 111', 10), ('100 101', 10), ('010 110', 10), ('101 110', 9), ('110 110', 8), ('110 101', 8), ('001 111', 8), ('111 011', 8), ('011 111', 8), ('010 101', 8), ('111 110', 7), ('111 001', 7), ('000 110', 6), ('000 101', 6)])\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "OrderedDict([('101 000', 34),\n",
       "             ('100 010', 33),\n",
       "             ('010 000', 32),\n",
       "             ('111 000', 29),\n",
       "             ('000 000', 28),\n",
       "             ('011 010', 28),\n",
       "             ('011 000', 27),\n",
       "             ('100 110', 26),\n",
       "             ('100 000', 26),\n",
       "             ('010 010', 24),\n",
       "             ('110 000', 23),\n",
       "             ('001 000', 23),\n",
       "             ('001 001', 23),\n",
       "             ('110 001', 22),\n",
       "             ('000 010', 21),\n",
       "             ('101 100', 20),\n",
       "             ('010 001', 20),\n",
       "             ('110 011', 19),\n",
       "             ('011 100', 18),\n",
       "             ('000 011', 18),\n",
       "             ('011 001', 17),\n",
       "             ('000 111', 17),\n",
       "             ('101 010', 16),\n",
       "             ('111 111', 16),\n",
       "             ('100 100', 16),\n",
       "             ('001 011', 16),\n",
       "             ('001 110', 16),\n",
       "             ('101 001', 16),\n",
       "             ('110 010', 15),\n",
       "             ('011 101', 15),\n",
       "             ('110 100', 15),\n",
       "             ('011 110', 15),\n",
       "             ('101 011', 14),\n",
       "             ('001 101', 14),\n",
       "             ('010 011', 14),\n",
       "             ('010 100', 14),\n",
       "             ('000 001', 13),\n",
       "             ('100 001', 13),\n",
       "             ('001 010', 13),\n",
       "             ('110 111', 13),\n",
       "             ('111 100', 13),\n",
       "             ('111 010', 13),\n",
       "             ('100 011', 13),\n",
       "             ('010 111', 12),\n",
       "             ('001 100', 12),\n",
       "             ('000 100', 12),\n",
       "             ('100 111', 11),\n",
       "             ('111 101', 11),\n",
       "             ('101 101', 11),\n",
       "             ('011 011', 11),\n",
       "             ('101 111', 10),\n",
       "             ('100 101', 10),\n",
       "             ('010 110', 10),\n",
       "             ('101 110', 9),\n",
       "             ('110 110', 8),\n",
       "             ('110 101', 8),\n",
       "             ('001 111', 8),\n",
       "             ('111 011', 8),\n",
       "             ('011 111', 8),\n",
       "             ('010 101', 8),\n",
       "             ('111 110', 7),\n",
       "             ('111 001', 7),\n",
       "             ('000 110', 6),\n",
       "             ('000 101', 6)])"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from qiskit.providers.aer import QasmSimulator\n",
    "sim_noisy_nairobi = QasmSimulator.from_backend(provider.get_backend('ibm_nairobi'))\n",
    "job = execute(qc,sim_noisy_nairobi,shots=1024)\n",
    "result = job.result()\n",
    "counts = result.get_counts()\n",
    "# qc_move = (counts.most_frequent())\n",
    "counts = OrderedDict(sorted(counts.items(), key=itemgetter(1), reverse=True)) # sort by counts\n",
    "print(counts)\n",
    "counts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "534ae766",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 504x360 with 1 Axes>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from qiskit.tools.visualization import plot_histogram\n",
    "plot_histogram(counts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "302471af",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "OrderedDict([('100 010', 69), ('011 010', 41), ('111 000', 35), ('110 000', 33), ('101 000', 32), ('100 011', 31), ('010 010', 31), ('001 000', 30), ('011 011', 28), ('000 000', 28), ('100 000', 27), ('100 110', 23), ('010 000', 23), ('000 010', 20), ('001 010', 20), ('011 000', 19), ('110 010', 18), ('010 001', 18), ('101 011', 17), ('011 001', 17), ('100 001', 17), ('011 110', 16), ('010 011', 16), ('101 111', 16), ('101 010', 16), ('101 100', 15), ('001 011', 14), ('111 010', 14), ('000 001', 14), ('101 001', 14), ('011 100', 14), ('010 111', 14), ('111 011', 13), ('000 100', 12), ('000 111', 12), ('000 101', 12), ('010 100', 12), ('000 110', 11), ('001 001', 11), ('000 011', 11), ('100 111', 11), ('110 011', 10), ('111 100', 10), ('100 101', 10), ('110 111', 10), ('010 110', 10), ('011 111', 10), ('100 100', 9), ('101 110', 9), ('001 111', 8), ('110 001', 8), ('001 100', 8), ('010 101', 8), ('111 111', 8), ('110 100', 8), ('111 001', 8), ('111 110', 7), ('001 110', 7), ('101 101', 6), ('110 101', 6), ('011 101', 6), ('110 110', 5), ('001 101', 5), ('111 101', 3)])\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "OrderedDict([('100 010', 69),\n",
       "             ('011 010', 41),\n",
       "             ('111 000', 35),\n",
       "             ('110 000', 33),\n",
       "             ('101 000', 32),\n",
       "             ('100 011', 31),\n",
       "             ('010 010', 31),\n",
       "             ('001 000', 30),\n",
       "             ('011 011', 28),\n",
       "             ('000 000', 28),\n",
       "             ('100 000', 27),\n",
       "             ('100 110', 23),\n",
       "             ('010 000', 23),\n",
       "             ('000 010', 20),\n",
       "             ('001 010', 20),\n",
       "             ('011 000', 19),\n",
       "             ('110 010', 18),\n",
       "             ('010 001', 18),\n",
       "             ('101 011', 17),\n",
       "             ('011 001', 17),\n",
       "             ('100 001', 17),\n",
       "             ('011 110', 16),\n",
       "             ('010 011', 16),\n",
       "             ('101 111', 16),\n",
       "             ('101 010', 16),\n",
       "             ('101 100', 15),\n",
       "             ('001 011', 14),\n",
       "             ('111 010', 14),\n",
       "             ('000 001', 14),\n",
       "             ('101 001', 14),\n",
       "             ('011 100', 14),\n",
       "             ('010 111', 14),\n",
       "             ('111 011', 13),\n",
       "             ('000 100', 12),\n",
       "             ('000 111', 12),\n",
       "             ('000 101', 12),\n",
       "             ('010 100', 12),\n",
       "             ('000 110', 11),\n",
       "             ('001 001', 11),\n",
       "             ('000 011', 11),\n",
       "             ('100 111', 11),\n",
       "             ('110 011', 10),\n",
       "             ('111 100', 10),\n",
       "             ('100 101', 10),\n",
       "             ('110 111', 10),\n",
       "             ('010 110', 10),\n",
       "             ('011 111', 10),\n",
       "             ('100 100', 9),\n",
       "             ('101 110', 9),\n",
       "             ('001 111', 8),\n",
       "             ('110 001', 8),\n",
       "             ('001 100', 8),\n",
       "             ('010 101', 8),\n",
       "             ('111 111', 8),\n",
       "             ('110 100', 8),\n",
       "             ('111 001', 8),\n",
       "             ('111 110', 7),\n",
       "             ('001 110', 7),\n",
       "             ('101 101', 6),\n",
       "             ('110 101', 6),\n",
       "             ('011 101', 6),\n",
       "             ('110 110', 5),\n",
       "             ('001 101', 5),\n",
       "             ('111 101', 3)])"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from qiskit.providers.aer import QasmSimulator\n",
    "sim_noisy_oslo = QasmSimulator.from_backend(provider.get_backend('ibm_oslo'))\n",
    "job = execute(qc,sim_noisy_oslo,shots=1024)\n",
    "result = job.result()\n",
    "counts = result.get_counts()\n",
    "# qc_move = (counts.most_frequent())\n",
    "counts = OrderedDict(sorted(counts.items(), key=itemgetter(1), reverse=True)) # sort by count\n",
    "print(counts)\n",
    "counts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "6da26a9b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 504x360 with 1 Axes>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from qiskit.tools.visualization import plot_histogram\n",
    "plot_histogram(counts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "3946770b",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1687.21x1167.88 with 1 Axes>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "qc.draw(output='mpl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8a90c507",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "5886e36e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "011\n",
      "Pick from: 3\n",
      "Amount: 2\n"
     ]
    }
   ],
   "source": [
    "board_choice = qc_move.split(' ')[0]\n",
    "# board_choice = board_choice[::-1]\n",
    "\n",
    "print(board_choice)\n",
    "board_choice = int(board_choice,2)\n",
    "\n",
    "print(\"Pick from:\",board_choice)\n",
    "board_state = qc_move.split(' ')[1]\n",
    "\n",
    "\n",
    "board_state = board_state[::-1]\n",
    "amount = int(board_state,2)\n",
    "print(\"Amount:\", amount)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "98e2d1ec",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4f8881bb",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "467800c0",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8a7bfc67",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5fa9afe2",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "501c3470",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6116abbb",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9bc0b076",
   "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": "3.8.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}