shor_algorithm.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "a9e7bf01",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[31mERROR: Could not find a version that satisfies the requirement ibm_quantum_widgets (from versions: none)\u001b[0m\u001b[31m\r\n",
      "\u001b[0m\u001b[31mERROR: No matching distribution found for ibm_quantum_widgets\u001b[0m\u001b[31m\r\n",
      "\u001b[0m"
     ]
    }
   ],
   "source": [
    "# Install a pip package in the current Jupyter kernel\n",
    "import sys\n",
    "!{sys.executable} -m pip install ibm_quantum_widgets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "17ab7037",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Shor's Algorithm Qiskit: https://learn.qiskit.org/course/ch-algorithms/shors-algorithm\n",
    "#controlled multiplcation circuits: https://arxiv.org/abs/quant-ph/0205095"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "3cf3a223",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Ignoring warnings\n",
    "import warnings\n",
    "\n",
    "if not sys.warnoptions:\n",
    "    warnings.simplefilter(\"ignore\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "fd2ab75b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Note: This can be any hub/group/project that has access to the required device and the Qiskit runtime.\n",
    "hub = \"ibm-q\"\n",
    "group = \"open\"\n",
    "project = \"main\"\n",
    "backend_name = \"ibm_brisbane\"\n",
    "hgp = f\"{hub}/{group}/{project}\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "cc09d924",
   "metadata": {},
   "outputs": [],
   "source": [
    "#fetch api_key\n",
    "with open(\"api_key.txt\",\"r\") as f:\n",
    "    api_key = f.read()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "08dff487",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using backend ibm_brisbane\n"
     ]
    }
   ],
   "source": [
    "import qiskit\n",
    "from qiskit_ibm_provider import IBMProvider\n",
    "\n",
    "# Save token if necessary\n",
    "IBMProvider.save_account(overwrite=True,token=api_key)\n",
    "\n",
    "# Get our backend\n",
    "provider = IBMProvider()\n",
    "available_backends = provider.backends()\n",
    "backend = provider.get_backend(backend_name, instance=hgp)\n",
    "print(f\"Using backend {backend.name}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "8bb4c6df-3fd9-49d3-aadf-4cf849a23703",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Imports Successful\n"
     ]
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "from qiskit import QuantumCircuit, Aer, transpile\n",
    "from qiskit.visualization import plot_histogram\n",
    "from math import gcd\n",
    "from numpy.random import randint\n",
    "import pandas as pd\n",
    "from fractions import Fraction\n",
    "print(\"Imports Successful\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "666d5a5a-f3ce-4d8f-93ec-facc084778e5",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "def c_amod15(a, power):\n",
    "    \"\"\"Controlled multiplication by a mod 15\"\"\"\n",
    "    if a not in [2,4,7,8,11,13]:\n",
    "        raise ValueError(\"'a' must be 2,4,7,8,11 or 13\")\n",
    "    U = QuantumCircuit(4)\n",
    "    for _iteration in range(power):\n",
    "        if a in [2,13]:\n",
    "            U.swap(2,3)\n",
    "            U.swap(1,2)\n",
    "            U.swap(0,1)\n",
    "        if a in [7,8]:\n",
    "            U.swap(0,1)\n",
    "            U.swap(1,2)\n",
    "            U.swap(2,3)\n",
    "        if a in [4, 11]:\n",
    "            U.swap(1,3)\n",
    "            U.swap(0,2)\n",
    "        if a in [7,11,13]:\n",
    "            for q in range(4):\n",
    "                U.x(q)\n",
    "    U = U.to_gate()\n",
    "    U.name = f\"{a}^{power} mod 15\"\n",
    "    c_U = U.control()\n",
    "    return c_U"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "f4b3f259-a0cb-416a-8ad5-8feccd6d216f",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Specify variables\n",
    "N_COUNT = 8  # number of counting qubits\n",
    "a = 7"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "104654ed-284a-40ae-9159-730451c1d0a1",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "def qft_dagger(n):\n",
    "    \"\"\"n-qubit QFTdagger the first n qubits in circ\"\"\"\n",
    "    qc = QuantumCircuit(n)\n",
    "    # Don't forget the Swaps!\n",
    "    for qubit in range(n//2):\n",
    "        qc.swap(qubit, n-qubit-1)\n",
    "    for j in range(n):\n",
    "        for m in range(j):\n",
    "            qc.cp(-np.pi/float(2**(j-m)), m, j)\n",
    "        qc.h(j)\n",
    "    qc.name = \"QFT†\"\n",
    "    return qc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "1c8eb594-eec9-4105-9337-6337f949f96a",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<pre style=\"word-wrap: normal;white-space: pre;background: #fff0;line-height: 1.1;font-family: &quot;Courier New&quot;,Courier,monospace\">      ┌───┐                                                                                                                             ┌───────┐┌─┐                     \n",
       " q_0: ┤ H ├───────■─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤0      ├┤M├─────────────────────\n",
       "      ├───┤       │                                                                                                                     │       │└╥┘┌─┐                  \n",
       " q_1: ┤ H ├───────┼──────────────■──────────────────────────────────────────────────────────────────────────────────────────────────────┤1      ├─╫─┤M├──────────────────\n",
       "      ├───┤       │              │                                                                                                      │       │ ║ └╥┘┌─┐               \n",
       " q_2: ┤ H ├───────┼──────────────┼──────────────■───────────────────────────────────────────────────────────────────────────────────────┤2      ├─╫──╫─┤M├───────────────\n",
       "      ├───┤       │              │              │                                                                                       │       │ ║  ║ └╥┘┌─┐            \n",
       " q_3: ┤ H ├───────┼──────────────┼──────────────┼──────────────■────────────────────────────────────────────────────────────────────────┤3      ├─╫──╫──╫─┤M├────────────\n",
       "      ├───┤       │              │              │              │                                                                        │  QFT† │ ║  ║  ║ └╥┘┌─┐         \n",
       " q_4: ┤ H ├───────┼──────────────┼──────────────┼──────────────┼──────────────■─────────────────────────────────────────────────────────┤4      ├─╫──╫──╫──╫─┤M├─────────\n",
       "      ├───┤       │              │              │              │              │                                                         │       │ ║  ║  ║  ║ └╥┘┌─┐      \n",
       " q_5: ┤ H ├───────┼──────────────┼──────────────┼──────────────┼──────────────┼───────────────■─────────────────────────────────────────┤5      ├─╫──╫──╫──╫──╫─┤M├──────\n",
       "      ├───┤       │              │              │              │              │               │                                         │       │ ║  ║  ║  ║  ║ └╥┘┌─┐   \n",
       " q_6: ┤ H ├───────┼──────────────┼──────────────┼──────────────┼──────────────┼───────────────┼───────────────■─────────────────────────┤6      ├─╫──╫──╫──╫──╫──╫─┤M├───\n",
       "      ├───┤       │              │              │              │              │               │               │                         │       │ ║  ║  ║  ║  ║  ║ └╥┘┌─┐\n",
       " q_7: ┤ H ├───────┼──────────────┼──────────────┼──────────────┼──────────────┼───────────────┼───────────────┼────────────────■────────┤7      ├─╫──╫──╫──╫──╫──╫──╫─┤M├\n",
       "      ├───┤┌──────┴──────┐┌──────┴──────┐┌──────┴──────┐┌──────┴──────┐┌──────┴───────┐┌──────┴───────┐┌──────┴───────┐┌───────┴───────┐└───────┘ ║  ║  ║  ║  ║  ║  ║ └╥┘\n",
       " q_8: ┤ X ├┤0            ├┤0            ├┤0            ├┤0            ├┤0             ├┤0             ├┤0             ├┤0              ├──────────╫──╫──╫──╫──╫──╫──╫──╫─\n",
       "      └───┘│             ││             ││             ││             ││              ││              ││              ││               │          ║  ║  ║  ║  ║  ║  ║  ║ \n",
       " q_9: ─────┤1            ├┤1            ├┤1            ├┤1            ├┤1             ├┤1             ├┤1             ├┤1              ├──────────╫──╫──╫──╫──╫──╫──╫──╫─\n",
       "           │  7^1 mod 15 ││  7^2 mod 15 ││  7^4 mod 15 ││  7^8 mod 15 ││  7^16 mod 15 ││  7^32 mod 15 ││  7^64 mod 15 ││  7^128 mod 15 │          ║  ║  ║  ║  ║  ║  ║  ║ \n",
       "q_10: ─────┤2            ├┤2            ├┤2            ├┤2            ├┤2             ├┤2             ├┤2             ├┤2              ├──────────╫──╫──╫──╫──╫──╫──╫──╫─\n",
       "           │             ││             ││             ││             ││              ││              ││              ││               │          ║  ║  ║  ║  ║  ║  ║  ║ \n",
       "q_11: ─────┤3            ├┤3            ├┤3            ├┤3            ├┤3             ├┤3             ├┤3             ├┤3              ├──────────╫──╫──╫──╫──╫──╫──╫──╫─\n",
       "           └─────────────┘└─────────────┘└─────────────┘└─────────────┘└──────────────┘└──────────────┘└──────────────┘└───────────────┘          ║  ║  ║  ║  ║  ║  ║  ║ \n",
       " c: 8/════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════╩══╩══╩══╩══╩══╩══╩══╩═\n",
       "                                                                                                                                                  0  1  2  3  4  5  6  7 </pre>"
      ],
      "text/plain": [
       "      ┌───┐                                                                                                                             ┌───────┐┌─┐                     \n",
       " q_0: ┤ H ├───────■─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤0      ├┤M├─────────────────────\n",
       "      ├───┤       │                                                                                                                     │       │└╥┘┌─┐                  \n",
       " q_1: ┤ H ├───────┼──────────────■──────────────────────────────────────────────────────────────────────────────────────────────────────┤1      ├─╫─┤M├──────────────────\n",
       "      ├───┤       │              │                                                                                                      │       │ ║ └╥┘┌─┐               \n",
       " q_2: ┤ H ├───────┼──────────────┼──────────────■───────────────────────────────────────────────────────────────────────────────────────┤2      ├─╫──╫─┤M├───────────────\n",
       "      ├───┤       │              │              │                                                                                       │       │ ║  ║ └╥┘┌─┐            \n",
       " q_3: ┤ H ├───────┼──────────────┼──────────────┼──────────────■────────────────────────────────────────────────────────────────────────┤3      ├─╫──╫──╫─┤M├────────────\n",
       "      ├───┤       │              │              │              │                                                                        │  QFT† │ ║  ║  ║ └╥┘┌─┐         \n",
       " q_4: ┤ H ├───────┼──────────────┼──────────────┼──────────────┼──────────────■─────────────────────────────────────────────────────────┤4      ├─╫──╫──╫──╫─┤M├─────────\n",
       "      ├───┤       │              │              │              │              │                                                         │       │ ║  ║  ║  ║ └╥┘┌─┐      \n",
       " q_5: ┤ H ├───────┼──────────────┼──────────────┼──────────────┼──────────────┼───────────────■─────────────────────────────────────────┤5      ├─╫──╫──╫──╫──╫─┤M├──────\n",
       "      ├───┤       │              │              │              │              │               │                                         │       │ ║  ║  ║  ║  ║ └╥┘┌─┐   \n",
       " q_6: ┤ H ├───────┼──────────────┼──────────────┼──────────────┼──────────────┼───────────────┼───────────────■─────────────────────────┤6      ├─╫──╫──╫──╫──╫──╫─┤M├───\n",
       "      ├───┤       │              │              │              │              │               │               │                         │       │ ║  ║  ║  ║  ║  ║ └╥┘┌─┐\n",
       " q_7: ┤ H ├───────┼──────────────┼──────────────┼──────────────┼──────────────┼───────────────┼───────────────┼────────────────■────────┤7      ├─╫──╫──╫──╫──╫──╫──╫─┤M├\n",
       "      ├───┤┌──────┴──────┐┌──────┴──────┐┌──────┴──────┐┌──────┴──────┐┌──────┴───────┐┌──────┴───────┐┌──────┴───────┐┌───────┴───────┐└───────┘ ║  ║  ║  ║  ║  ║  ║ └╥┘\n",
       " q_8: ┤ X ├┤0            ├┤0            ├┤0            ├┤0            ├┤0             ├┤0             ├┤0             ├┤0              ├──────────╫──╫──╫──╫──╫──╫──╫──╫─\n",
       "      └───┘│             ││             ││             ││             ││              ││              ││              ││               │          ║  ║  ║  ║  ║  ║  ║  ║ \n",
       " q_9: ─────┤1            ├┤1            ├┤1            ├┤1            ├┤1             ├┤1             ├┤1             ├┤1              ├──────────╫──╫──╫──╫──╫──╫──╫──╫─\n",
       "           │  7^1 mod 15 ││  7^2 mod 15 ││  7^4 mod 15 ││  7^8 mod 15 ││  7^16 mod 15 ││  7^32 mod 15 ││  7^64 mod 15 ││  7^128 mod 15 │          ║  ║  ║  ║  ║  ║  ║  ║ \n",
       "q_10: ─────┤2            ├┤2            ├┤2            ├┤2            ├┤2             ├┤2             ├┤2             ├┤2              ├──────────╫──╫──╫──╫──╫──╫──╫──╫─\n",
       "           │             ││             ││             ││             ││              ││              ││              ││               │          ║  ║  ║  ║  ║  ║  ║  ║ \n",
       "q_11: ─────┤3            ├┤3            ├┤3            ├┤3            ├┤3             ├┤3             ├┤3             ├┤3              ├──────────╫──╫──╫──╫──╫──╫──╫──╫─\n",
       "           └─────────────┘└─────────────┘└─────────────┘└─────────────┘└──────────────┘└──────────────┘└──────────────┘└───────────────┘          ║  ║  ║  ║  ║  ║  ║  ║ \n",
       " c: 8/════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════╩══╩══╩══╩══╩══╩══╩══╩═\n",
       "                                                                                                                                                  0  1  2  3  4  5  6  7 "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create QuantumCircuit with N_COUNT counting qubits\n",
    "# plus 4 qubits for U to act on\n",
    "qc = QuantumCircuit(N_COUNT + 4, N_COUNT)\n",
    "\n",
    "# Initialize counting qubits\n",
    "# in state |+>\n",
    "for q in range(N_COUNT):\n",
    "    qc.h(q)\n",
    "\n",
    "# And auxiliary register in state |1>\n",
    "qc.x(N_COUNT)\n",
    "\n",
    "# Do controlled-U operations\n",
    "for q in range(N_COUNT):\n",
    "    qc.append(c_amod15(a, 2**q),\n",
    "             [q] + [i+N_COUNT for i in range(4)])\n",
    "\n",
    "# Do inverse-QFT\n",
    "qc.append(qft_dagger(N_COUNT), range(N_COUNT))\n",
    "\n",
    "# Measure circuit\n",
    "qc.measure(range(N_COUNT), range(N_COUNT))\n",
    "qc.draw(fold=-1)  # -1 means 'do not fold'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "b0f150c9-e608-46b5-b020-51c3ffd34fb4",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 700x500 with 1 Axes>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#aer_sim = Aer.get_backend('aer_simulator')\n",
    "#t_qc = transpile(qc, aer_sim)\n",
    "#transpile quantum circuit qc using real quantum backend\n",
    "t_qc = transpile(qc, backend)\n",
    "counts = backend.run(t_qc).result().get_counts()\n",
    "plot_histogram(counts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "7719918a-28ee-407a-9159-dcf160205d46",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              Register Output           Phase\n",
      "0    00000000(bin) =   0(dec)    0/256 = 0.00\n",
      "1    00000001(bin) =   1(dec)    1/256 = 0.00\n",
      "2    00010000(bin) =  16(dec)   16/256 = 0.06\n",
      "3    00010001(bin) =  17(dec)   17/256 = 0.07\n",
      "4    00010010(bin) =  18(dec)   18/256 = 0.07\n",
      "..                        ...             ...\n",
      "251  11111011(bin) = 251(dec)  251/256 = 0.98\n",
      "252  11111100(bin) = 252(dec)  252/256 = 0.98\n",
      "253  11111101(bin) = 253(dec)  253/256 = 0.99\n",
      "254  11111110(bin) = 254(dec)  254/256 = 0.99\n",
      "255  11111111(bin) = 255(dec)  255/256 = 1.00\n",
      "\n",
      "[256 rows x 2 columns]\n"
     ]
    }
   ],
   "source": [
    "rows, measured_phases = [], []\n",
    "for output in counts:\n",
    "    decimal = int(output, 2)  # Convert (base 2) string to decimal\n",
    "    phase = decimal/(2**N_COUNT)  # Find corresponding eigenvalue\n",
    "    measured_phases.append(phase)\n",
    "    # Add these values to the rows in our table:\n",
    "    rows.append([f\"{output}(bin) = {decimal:>3}(dec)\",\n",
    "                 f\"{decimal}/{2**N_COUNT} = {phase:.2f}\"])\n",
    "# Print the rows in a table\n",
    "headers=[\"Register Output\", \"Phase\"]\n",
    "df = pd.DataFrame(rows, columns=headers)\n",
    "print(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "5bc94ef8-eec3-40eb-b8d6-04ffc82f10e0",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "        Phase Fraction  Guess for r\n",
      "0    0.000000      0/1            1\n",
      "1    0.003906      0/1            1\n",
      "2    0.062500     1/15           15\n",
      "3    0.066406     1/15           15\n",
      "4    0.070312     1/14           14\n",
      "..        ...      ...          ...\n",
      "251  0.980469      1/1            1\n",
      "252  0.984375      1/1            1\n",
      "253  0.988281      1/1            1\n",
      "254  0.992188      1/1            1\n",
      "255  0.996094      1/1            1\n",
      "\n",
      "[256 rows x 3 columns]\n"
     ]
    }
   ],
   "source": [
    "rows = []\n",
    "for phase in measured_phases:\n",
    "    frac = Fraction(phase).limit_denominator(15)\n",
    "    rows.append([phase,\n",
    "                 f\"{frac.numerator}/{frac.denominator}\",\n",
    "                 frac.denominator])\n",
    "# Print as a table\n",
    "headers=[\"Phase\", \"Fraction\", \"Guess for r\"]\n",
    "df = pd.DataFrame(rows, columns=headers)\n",
    "print(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "13184b4b-a0a8-41f1-b78d-8a0a0c8b5d63",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "7\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "N = 15\n",
    "np.random.seed(1) # This is to make sure we get reproduceable results\n",
    "a = randint(2, N)\n",
    "print(a)\n",
    "from math import gcd # greatest common divisor\n",
    "gcd(a, N)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "20025ca7-71ed-4215-a8a4-a6d1ab5173a3",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "#quantum phase estimation for N=15 with manual U gates\n",
    "def qpe_amod15(a):\n",
    "    \"\"\"Performs quantum phase estimation on the operation a*r mod 15.\n",
    "    Args:\n",
    "        a (int): This is 'a' in a*r mod 15\n",
    "    Returns:\n",
    "        float: Estimate of the phase\n",
    "    \"\"\"\n",
    "    N_COUNT = 8\n",
    "    qc = QuantumCircuit(4+N_COUNT, N_COUNT)\n",
    "    for q in range(N_COUNT):\n",
    "        qc.h(q)     # Initialize counting qubits in state |+>\n",
    "    qc.x(3+N_COUNT) # And auxiliary register in state |1>\n",
    "    for q in range(N_COUNT): # Do controlled-U operations\n",
    "        qc.append(c_amod15(a, 2**q),\n",
    "                 [q] + [i+N_COUNT for i in range(4)])\n",
    "    qc.append(qft_dagger(N_COUNT), range(N_COUNT)) # Do inverse-QFT\n",
    "    qc.measure(range(N_COUNT), range(N_COUNT))\n",
    "    # Simulate Results\n",
    "    #aer_sim = Aer.get_backend('aer_simulator')\n",
    "    # `memory=True` tells the backend to save each measurement in a list\n",
    "    job = backend.run(transpile(qc, backend), shots=1, memory=True)\n",
    "    readings = job.result().get_memory()\n",
    "    print(\"Register Reading: \" + readings[0])\n",
    "    phase = int(readings[0],2)/(2**N_COUNT)\n",
    "    print(f\"Corresponding Phase: {phase}\")\n",
    "    return phase"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "e3307c70-8206-4d03-ae03-1831149fe68f",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Register Reading: 00101011\n",
      "Corresponding Phase: 0.16796875\n"
     ]
    },
    {
     "ename": "NameError",
     "evalue": "name 'r' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m/var/folders/4d/rp7dhwss6znf18sks0v304d80000gn/T/ipykernel_2675/2496551432.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mphase\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mqpe_amod15\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;31m# Phase = s/r\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0mFraction\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mphase\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlimit_denominator\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m15\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0mguesses\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mgcd\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mr\u001b[0m\u001b[0;34m//\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mN\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mgcd\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mr\u001b[0m\u001b[0;34m//\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mN\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      4\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mguesses\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'r' is not defined"
     ]
    }
   ],
   "source": [
    "phase = qpe_amod15(a) # Phase = s/r\n",
    "Fraction(phase).limit_denominator(15)\n",
    "r = frac.denominator\n",
    "guesses = [gcd(a**(r//2)-1, N), gcd(a**(r//2)+1, N)]\n",
    "print(guesses)"
   ]
  },
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   "execution_count": 48,
   "id": "fd95996d-c25b-4f38-9653-127b9893d87d",
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     "name": "stdout",
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     "text": [
      "\n",
      "ATTEMPT 1:\n",
      "Register Reading: 10000000\n",
      "Corresponding Phase: 0.5\n",
      "Result: r = 2\n",
      "Guessed Factors: 3 and 1\n",
      "*** Non-trivial factor found: {guess} ***\n"
     ]
    }
   ],
   "source": [
    "a = 7\n",
    "FACTOR_FOUND = False\n",
    "ATTEMPT = 0\n",
    "while not FACTOR_FOUND:\n",
    "    ATTEMPT += 1\n",
    "    print(f\"\\nATTEMPT {ATTEMPT}:\")\n",
    "    phase = qpe_amod15(a) # Phase = s/r\n",
    "    frac = Fraction(phase).limit_denominator(N)\n",
    "    r = frac.denominator\n",
    "    print(f\"Result: r = {r}\")\n",
    "    if phase != 0:\n",
    "        # Guesses for factors are gcd(x^{r/2} ±1 , 15)\n",
    "        guesses = [gcd(a**(r//2)-1, N), gcd(a**(r//2)+1, N)]\n",
    "        print(f\"Guessed Factors: {guesses[0]} and {guesses[1]}\")\n",
    "        for guess in guesses:\n",
    "            if guess not in [1,N] and (N % guess) == 0:\n",
    "                # Guess is a factor!\n",
    "                print(\"*** Non-trivial factor found: {guess} ***\")\n",
    "                FACTOR_FOUND = True"
   ]
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