qk.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "configrc.store_credentials:WARNING:2023-01-28 11:31:11,154: Credentials already present. Set overwrite=True to overwrite.\n",
      "ibmqfactory.load_account:WARNING:2023-01-28 11:31:11,634: Credentials are already in use. The existing account in the session will be replaced.\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "import random\n",
    "import numpy as np\n",
    "import math\n",
    "from qiskit import QuantumRegister, ClassicalRegister, QuantumCircuit, execute, Aer, IBMQ\n",
    "from qiskit.tools.visualization import plot_histogram\n",
    "from qiskit_braket_provider import AWSBraketProvider, BraketLocalBackend\n",
    "from dotenv import load_dotenv\n",
    "# Load the API key from the .env file\n",
    "load_dotenv()\n",
    "IBMQ.save_account(os.getenv('IBM_TOKEN'))\n",
    "IBMQ.load_account()\n",
    "max_qubits = 7\n",
    "\n",
    "# Define functions to simplify interactions with quantum computers\n",
    "def get_ibm_quantum_computer(qubits=2, simulation=False, verbose=False):\n",
    "    quantum_computer = None\n",
    "    if qubits > max_qubits:\n",
    "        print(f\"No Quantum Computers available with {qubits} qubits.\")\n",
    "        simulation = True\n",
    "    # Finds a quantum computer at IBM with the lowest queue as long as it has enough qubits for the circuit\n",
    "    if simulation == False: # Execute the circuit on a quantum computer at IBM\n",
    "        lowest = float('inf')\n",
    "        provider = IBMQ.get_provider(hub='ibm-q')\n",
    "        for backend in provider.backends():\n",
    "            try:\n",
    "                if \"simulator\" not in backend.name():\n",
    "                    queue = backend.status().pending_jobs\n",
    "                    qubit_count = len(backend.properties().qubits)\n",
    "                    if qubit_count >= qubits:\n",
    "                        if queue < lowest:\n",
    "                            lowest = queue\n",
    "                            quantum_computer = provider.get_backend(backend.name())\n",
    "                        if verbose == True:\n",
    "                            print(f\"Quantum Computer {backend.name()} has {queue} queued jobs and {qubit_count} qubits\")\n",
    "                    else:\n",
    "                        if verbose == True:\n",
    "                            print(f\"Quantum Computer {backend.name()} has {qubit_count} qubits, but we need {qubits} qubits\")\n",
    "            except:\n",
    "                print(f\"Quantum Computer {backend.name()} is not operational\")\n",
    "        if quantum_computer is None:\n",
    "            print(f\"No Quantum Computers available with {qubits} qubits.\")\n",
    "            simulation = True\n",
    "    if simulation == True or quantum_computer is None: # Execute the circuit on the simulator\n",
    "        quantum_computer = Aer.get_backend('qasm_simulator')\n",
    "        if verbose == True:\n",
    "            print(f\"Using Quantum Computer Simulator\")\n",
    "    if verbose == True and simulation == False:\n",
    "        print(f\"Using Quantum Computer: {quantum_computer.name()} with {quantum_computer.status().pending_jobs} queued jobs\")\n",
    "    return quantum_computer\n",
    "\n",
    "def prepare_quantum_circuit(qubits=2, classical_bits=2, simulation=False, verbose=False, quantum_computer=\"IBM\"):\n",
    "    # Create registers for the circuit\n",
    "    quantum_register = QuantumRegister(qubits)\n",
    "    classical_register = ClassicalRegister(classical_bits)\n",
    "    # Create a Quantum Circuit\n",
    "    quantum_circuit = QuantumCircuit(quantum_register, classical_register)\n",
    "    if quantum_computer == \"IBM\": # Pick a quantum computer that has enough qubits to execute the circuit from IBM\n",
    "        # Get a quantum computer with at least the number of selected qubits for the circuit and the lowest queue, or the simulator\n",
    "        quantum_computer = get_ibm_quantum_computer(qubits=qubits, simulation=simulation, verbose=verbose)\n",
    "    else: # Use the quantum computer specified from quantum_computer on AWS Braket\n",
    "        aws_provider = AWSBraketProvider()\n",
    "        if quantum_computer in aws_provider.backends():\n",
    "            quantum_computer = aws_provider.get_backend(quantum_computer)\n",
    "        else:\n",
    "            quantum_computer = BraketLocalBackend()\n",
    "    return quantum_circuit, quantum_register, classical_register, quantum_computer\n",
    "\n",
    "def execute_quantum_circuit(quantum_circuit, quantum_computer, shots=500, verbose=False):\n",
    "    queue_position = quantum_computer.status().pending_jobs + 1\n",
    "    if verbose == True:\n",
    "        print(f\"Your job is number {queue_position} in the queue on {quantum_computer.name()}.  Please wait...\")\n",
    "    result = execute(quantum_circuit, backend=quantum_computer, shots=shots).result()\n",
    "    counts = result.get_counts(quantum_circuit)\n",
    "    if verbose == True:\n",
    "        print(f\"{result.status} in {result.time_taken} seconds on {quantum_computer.name()}\")\n",
    "    highest_probable = result.get_counts(quantum_circuit).most_frequent()\n",
    "    if verbose == True:\n",
    "        probability = 100 * float(result.get_counts(quantum_circuit)[highest_probable])/float(shots)\n",
    "        print(f\"The probable result was {highest_probable} with {probability}% probability\")\n",
    "    return highest_probable, result, counts\n",
    "\n",
    "def bell_state_circuit(quantum_circuit, quantum_register, classical_register):\n",
    "    # Build a Bell State circuit\n",
    "    # https://en.wikipedia.org/wiki/Bell_state\n",
    "    quantum_circuit.h(quantum_register[0])\n",
    "    quantum_circuit.cx(quantum_register[0], quantum_register[1])\n",
    "    quantum_circuit.measure(quantum_register, classical_register)\n",
    "    drawing = quantum_circuit.draw()\n",
    "    return quantum_circuit, drawing\n",
    "\n",
    "def brute_force_circuit(quantum_circuit, quantum_register, classical_register):\n",
    "    # Build a brute force circuit using 3 qubits\n",
    "    # https://en.wikipedia.org/wiki/Quantum_logic_gate#Quantum_logic_gates\n",
    "    quantum_circuit.h(quantum_register[0])\n",
    "    quantum_circuit.x(quantum_register[1])\n",
    "    quantum_circuit.cx(quantum_register[0], quantum_register[1])\n",
    "    quantum_circuit.h(quantum_register[2])\n",
    "    quantum_circuit.cx(quantum_register[1], quantum_register[2])\n",
    "    quantum_circuit.measure(quantum_register, classical_register)\n",
    "    drawing = quantum_circuit.draw()\n",
    "    return quantum_circuit, drawing\n",
    "\n",
    "def iterate_words(words, shots=500, verbose=False):\n",
    "    new_words = {}\n",
    "    for word in words:\n",
    "        quantum_circuit, quantum_register, classical_register, quantum_computer = prepare_quantum_circuit(qubits=3, classical_bits=3, simulation=True, verbose=True)\n",
    "        quantum_circuit, drawing = brute_force_circuit(quantum_circuit, quantum_register, classical_register)\n",
    "        highest_probable, result, counts = execute_quantum_circuit(quantum_circuit, quantum_computer, shots=shots, verbose=True)\n",
    "        \n",
    "        if highest_probable not in new_words:\n",
    "            new_words[highest_probable] = word\n",
    "        else:\n",
    "            # I didn't like that answer, try again with less shots!\n",
    "            if shots > 1:\n",
    "                shots = int(shots/2)\n",
    "            else:\n",
    "                # We have exhausted our options, the gods have willed that this is not an option.\n",
    "                continue\n",
    "            new_words = iterate_words(words, quantum_circuit, quantum_register, classical_register, quantum_computer, shots=shots, verbose=verbose)\n",
    "    return new_words\n",
    "\n",
    "def brute_force(words, quantum_circuit, quantum_register, classical_register, quantum_computer, shots=500, verbose=False):\n",
    "    # Lets do something silly.  Lets brute force a password if there are only 8 possible passwords.\n",
    "    # We could wrap this in a function and pass in a dictionary then make this roll a probability for each, but we will just use a list for now.\n",
    "    # There is no way this can be accurate, but it is fun to see the results.\n",
    "    # Shuffle the list of words so that they're essentially in super position until observed below.\n",
    "    quantum_circuit, drawing = brute_force_circuit(quantum_circuit, quantum_register, classical_register)\n",
    "    highest_probable, result, counts = execute_quantum_circuit(quantum_circuit, quantum_computer, shots=shots, verbose=True)\n",
    "    random.shuffle(words)\n",
    "    probabilities = {}\n",
    "    # This isn't working below - I need to find a way to match a word randomly to each result and then calculate the probability of each word.\n",
    "    for key, value in counts.items():\n",
    "        for word in words:\n",
    "            if word not in probabilities.keys():\n",
    "                probability = 100 * float(value)/float(shots)\n",
    "                probabilities[word] = f\"{probability}%\"\n",
    "\n",
    "    return probabilities, drawing, highest_probable, result, counts\n",
    "\n",
    "def shors_circuit(quantum_circuit, quantum_register, classical_register):\n",
    "    # The number we want to factorize\n",
    "    for j in range(len(quantum_register)):\n",
    "        quantum_circuit.h(quantum_register[j])\n",
    "        for k in range(j + 1, len(quantum_register)):\n",
    "            quantum_circuit.cp(math.pi / float(2 ** (k - j)), quantum_register[k], quantum_register[j])\n",
    "        quantum_circuit.measure(quantum_register[j], classical_register[j])\n",
    "    drawing = quantum_circuit.draw()\n",
    "    return quantum_circuit, drawing\n",
    "\n",
    "def factorize(N=39):\n",
    "    # We want to find factors of 39, so set N to 39\n",
    "    # We need to know how many bits we need to represent N\n",
    "    bit_count = math.ceil(math.log(N, 2)) + 1\n",
    "    # Set up the circuit\n",
    "    quantum_circuit, quantum_register, classical_register, quantum_computer = prepare_quantum_circuit(qubits=bit_count, classical_bits=bit_count, simulation=False, verbose=True)\n",
    "    quantum_circuit, drawing = shors_circuit(quantum_circuit, quantum_register, classical_register)\n",
    "    highest_probable, result, counts = execute_quantum_circuit(quantum_circuit, quantum_computer, shots=8192, verbose=True)\n",
    "    \n",
    "    return highest_probable, result, counts, drawing\n",
    "\n",
    "def deutsch_jozsa_circuit(f, n, quantum_circuit, quantum_register, classical_register, quantum_computer):\n",
    "    \"\"\"\n",
    "    Uses the Deutsch-Jozsa algorithm to determine whether a given Boolean function f on n qubits is\n",
    "    constant (returns always 0 or always 1) or balanced (returns 0 and 1 with equal probability)\n",
    "    \"\"\"    \n",
    "    # Prepare the input qubits in the |+> state\n",
    "    for i in range(n):\n",
    "        quantum_circuit.h(i)\n",
    "    # Oracle that evaluates the given Boolean function f on n qubits\n",
    "    for i in range(n):\n",
    "        if f[i] == '1':\n",
    "            quantum_circuit.x(i)\n",
    "    quantum_circuit.cx(0, n)\n",
    "    for i in range(n):\n",
    "        if f[i] == '1':\n",
    "            quantum_circuit.x(i)\n",
    "\n",
    "    # Apply Hadamard gate again\n",
    "    for i in range(n):\n",
    "        quantum_circuit.h(i)\n",
    "    # Measure the output\n",
    "    quantum_circuit.measure(quantum_register[0], classical_register[0])\n",
    "    drawing = quantum_circuit.draw()\n",
    "    return quantum_circuit, drawing\n",
    "\n",
    "def alethiometer(statement):\n",
    "    # I thought it would be fun to make a quantum alethiometer.\n",
    "    # https://en.wikipedia.org/wiki/Alethiometer\n",
    "    # It is just a joke, but it is fun to see how it works.\n",
    "    # We also don't even have enough qubits available to convert a single letter to binary, so it will only run on the simulator.\n",
    "    f = ''.join(format(ord(x), 'b') for x in statement)\n",
    "    n = len(f)\n",
    "    qubits = n+1\n",
    "    quantum_circuit, quantum_register, classical_register, quantum_computer = prepare_quantum_circuit(qubits=qubits, classical_bits=1, simulation=False, verbose=True)\n",
    "    quantum_circuit, drawing = deutsch_jozsa_circuit(f, n, quantum_circuit, quantum_register, classical_register, quantum_computer)\n",
    "    highest_probable, result, counts = execute_quantum_circuit(quantum_circuit, quantum_computer, shots=8192, verbose=True)\n",
    "    if '0' in highest_probable:\n",
    "        answer = True\n",
    "    else:\n",
    "        answer = False\n",
    "    print(f\"Your statement '{statement}' is {answer}.\")\n",
    "    return answer, drawing\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "No Quantum Computers available with 10 qubits.\n",
      "Using Quantum Computer Simulator\n",
      "Your job is number 1 in the queue on qasm_simulator.  Please wait...\n",
      "COMPLETED in 0.010221719741821289 seconds on qasm_simulator\n",
      "The probable result was 1100001011 with 0.23193359375% probability\n"
     ]
    },
    {
     "data": {
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       "<pre style=\"word-wrap: normal;white-space: pre;background: #fff0;line-height: 1.1;font-family: &quot;Courier New&quot;,Courier,monospace\">        ┌───┐                                                                 »\n",
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       "        └───┘ │P(π/2)  │       ┌───┐ │                 │                      »\n",
       "q122_1: ──────■────────┼───────┤ H ├─┼────────■────────┼─────────■────────────»\n",
       "                       │P(π/4) └───┘ │        │P(π/2)  │         │       ┌───┐»\n",
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       "                                     │P(π/8)           │         │P(π/4) └───┘»\n",
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       "«         │         │P(π/8)           │         │         │P(π/4) └───┘»\n",
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       "«         │P(π/4) └───┘ │          │          │         │        │P(π/2)  ║ »\n",
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       "«q122_6: ─┼────────■────────╫──┼─────────┼─────────┼───────┤ H ├─╫──┼────────»\n",
       "«         │P(π/8)           ║  │         │         │P(π/4) └───┘ ║  │        »\n",
       "«q122_7: ─■─────────────────╫──┼─────────┼─────────■─────────────╫──┼────────»\n",
       "«                           ║  │         │P(π/16)                ║  │        »\n",
       "«q122_8: ───────────────────╫──┼─────────■───────────────────────╫──┼────────»\n",
       "«                           ║  │P(π/64)                          ║  │P(π/32) »\n",
       "«q122_9: ───────────────────╫──■─────────────────────────────────╫──■────────»\n",
       "«                           ║                                    ║           »\n",
       "«c25: 10/═══════════════════╩════════════════════════════════════╩═══════════»\n",
       "«                           2                                    3           »\n",
       "«                                                                             »\n",
       "«q122_0: ─────────────────────────────────────────────────────────────────────»\n",
       "«                                                                             »\n",
       "«q122_1: ─────────────────────────────────────────────────────────────────────»\n",
       "«                                                                             »\n",
       "«q122_2: ─────────────────────────────────────────────────────────────────────»\n",
       "«                                                                             »\n",
       "«q122_3: ─────────────────────────────────────────────────────────────────────»\n",
       "«                          ┌─┐                                                »\n",
       "«q122_4: ──────────────────┤M├────────────────────────────────────────────────»\n",
       "«                          └╥┘                        ┌─┐                     »\n",
       "«q122_5: ─■─────────────────╫──■──────────────────────┤M├─────────────────────»\n",
       "«         │                 ║  │                      └╥┘                  ┌─┐»\n",
       "«q122_6: ─┼────────■────────╫──┼─────────■─────────────╫──■────────────────┤M├»\n",
       "«         │        │P(π/2)  ║  │         │       ┌───┐ ║  │                └╥┘»\n",
       "«q122_7: ─┼────────■────────╫──┼─────────┼───────┤ H ├─╫──┼────────■────────╫─»\n",
       "«         │P(π/8)           ║  │         │P(π/4) └───┘ ║  │        │P(π/2)  ║ »\n",
       "«q122_8: ─■─────────────────╫──┼─────────■─────────────╫──┼────────■────────╫─»\n",
       "«                           ║  │P(π/16)                ║  │P(π/8)           ║ »\n",
       "«q122_9: ───────────────────╫──■───────────────────────╫──■─────────────────╫─»\n",
       "«                           ║                          ║                    ║ »\n",
       "«c25: 10/═══════════════════╩══════════════════════════╩════════════════════╩═»\n",
       "«                           4                          5                    6 »\n",
       "«                                             \n",
       "«q122_0: ─────────────────────────────────────\n",
       "«                                             \n",
       "«q122_1: ─────────────────────────────────────\n",
       "«                                             \n",
       "«q122_2: ─────────────────────────────────────\n",
       "«                                             \n",
       "«q122_3: ─────────────────────────────────────\n",
       "«                                             \n",
       "«q122_4: ─────────────────────────────────────\n",
       "«                                             \n",
       "«q122_5: ─────────────────────────────────────\n",
       "«                                             \n",
       "«q122_6: ─────────────────────────────────────\n",
       "«                      ┌─┐                    \n",
       "«q122_7: ─■────────────┤M├────────────────────\n",
       "«         │       ┌───┐└╥┘         ┌─┐        \n",
       "«q122_8: ─┼───────┤ H ├─╫──■───────┤M├────────\n",
       "«         │P(π/4) └───┘ ║  │P(π/2) └╥┘┌───┐┌─┐\n",
       "«q122_9: ─■─────────────╫──■────────╫─┤ H ├┤M├\n",
       "«                       ║           ║ └───┘└╥┘\n",
       "«c25: 10/═══════════════╩═══════════╩═══════╩═\n",
       "«                       7           8       9 </pre>"
      ],
      "text/plain": [
       "        ┌───┐                                                                 »\n",
       "q122_0: ┤ H ├─■────────■─────────────■─────────────────■──────────────────────»\n",
       "        └───┘ │P(π/2)  │       ┌───┐ │                 │                      »\n",
       "q122_1: ──────■────────┼───────┤ H ├─┼────────■────────┼─────────■────────────»\n",
       "                       │P(π/4) └───┘ │        │P(π/2)  │         │       ┌───┐»\n",
       "q122_2: ───────────────■─────────────┼────────■────────┼─────────┼───────┤ H ├»\n",
       "                                     │P(π/8)           │         │P(π/4) └───┘»\n",
       "q122_3: ─────────────────────────────■─────────────────┼─────────■────────────»\n",
       "                                                       │P(π/16)               »\n",
       "q122_4: ───────────────────────────────────────────────■──────────────────────»\n",
       "                                                                              »\n",
       "q122_5: ──────────────────────────────────────────────────────────────────────»\n",
       "                                                                              »\n",
       "q122_6: ──────────────────────────────────────────────────────────────────────»\n",
       "                                                                              »\n",
       "q122_7: ──────────────────────────────────────────────────────────────────────»\n",
       "                                                                              »\n",
       "q122_8: ──────────────────────────────────────────────────────────────────────»\n",
       "                                                                              »\n",
       "q122_9: ──────────────────────────────────────────────────────────────────────»\n",
       "                                                                              »\n",
       "c25: 10/══════════════════════════════════════════════════════════════════════»\n",
       "                                                                              »\n",
       "«                                                                      »\n",
       "«q122_0: ─■───────────────────────────■────────────────────────────────»\n",
       "«         │                           │                                »\n",
       "«q122_1: ─┼─────────■─────────────────┼─────────■──────────────────────»\n",
       "«         │         │                 │         │                      »\n",
       "«q122_2: ─┼─────────┼────────■────────┼─────────┼─────────■────────────»\n",
       "«         │         │        │P(π/2)  │         │         │       ┌───┐»\n",
       "«q122_3: ─┼─────────┼────────■────────┼─────────┼─────────┼───────┤ H ├»\n",
       "«         │         │P(π/8)           │         │         │P(π/4) └───┘»\n",
       "«q122_4: ─┼─────────■─────────────────┼─────────┼─────────■────────────»\n",
       "«         │P(π/32)                    │         │P(π/16)               »\n",
       "«q122_5: ─■───────────────────────────┼─────────■──────────────────────»\n",
       "«                                     │P(π/64)                         »\n",
       "«q122_6: ─────────────────────────────■────────────────────────────────»\n",
       "«                                                                      »\n",
       "«q122_7: ──────────────────────────────────────────────────────────────»\n",
       "«                                                                      »\n",
       "«q122_8: ──────────────────────────────────────────────────────────────»\n",
       "«                                                                      »\n",
       "«q122_9: ──────────────────────────────────────────────────────────────»\n",
       "«                                                                      »\n",
       "«c25: 10/══════════════════════════════════════════════════════════════»\n",
       "«                                                                      »\n",
       "«                                                                              »\n",
       "«q122_0: ─■──────────────────────────────────────■─────────────────────────────»\n",
       "«         │                                      │                             »\n",
       "«q122_1: ─┼──────────■───────────────────────────┼──────────■──────────────────»\n",
       "«         │          │                           │          │                  »\n",
       "«q122_2: ─┼──────────┼─────────■─────────────────┼──────────┼─────────■────────»\n",
       "«         │          │         │                 │          │         │        »\n",
       "«q122_3: ─┼──────────┼─────────┼────────■────────┼──────────┼─────────┼────────»\n",
       "«         │          │         │        │P(π/2)  │          │         │        »\n",
       "«q122_4: ─┼──────────┼─────────┼────────■────────┼──────────┼─────────┼────────»\n",
       "«         │          │         │P(π/8)           │          │         │        »\n",
       "«q122_5: ─┼──────────┼─────────■─────────────────┼──────────┼─────────┼────────»\n",
       "«         │          │P(π/32)                    │          │         │P(π/16) »\n",
       "«q122_6: ─┼──────────■───────────────────────────┼──────────┼─────────■────────»\n",
       "«         │P(π/128)                              │          │P(π/64)           »\n",
       "«q122_7: ─■──────────────────────────────────────┼──────────■──────────────────»\n",
       "«                                                │P(π/256)                     »\n",
       "«q122_8: ────────────────────────────────────────■─────────────────────────────»\n",
       "«                                                                              »\n",
       "«q122_9: ──────────────────────────────────────────────────────────────────────»\n",
       "«                                                                              »\n",
       "«c25: 10/══════════════════════════════════════════════════════════════════════»\n",
       "«                                                                              »\n",
       "«                                                                        ┌─┐»\n",
       "«q122_0: ───────────────■────────────────────────────────────────────────┤M├»\n",
       "«                       │                                                └╥┘»\n",
       "«q122_1: ───────────────┼──────────■──────────────────────────────────────╫─»\n",
       "«                       │          │                                      ║ »\n",
       "«q122_2: ───────────────┼──────────┼──────────■───────────────────────────╫─»\n",
       "«                       │          │          │                           ║ »\n",
       "«q122_3: ─■─────────────┼──────────┼──────────┼─────────■─────────────────╫─»\n",
       "«         │       ┌───┐ │          │          │         │                 ║ »\n",
       "«q122_4: ─┼───────┤ H ├─┼──────────┼──────────┼─────────┼────────■────────╫─»\n",
       "«         │P(π/4) └───┘ │          │          │         │        │P(π/2)  ║ »\n",
       "«q122_5: ─■─────────────┼──────────┼──────────┼─────────┼────────■────────╫─»\n",
       "«                       │          │          │         │P(π/8)           ║ »\n",
       "«q122_6: ───────────────┼──────────┼──────────┼─────────■─────────────────╫─»\n",
       "«                       │          │          │P(π/32)                    ║ »\n",
       "«q122_7: ───────────────┼──────────┼──────────■───────────────────────────╫─»\n",
       "«                       │          │P(π/128)                              ║ »\n",
       "«q122_8: ───────────────┼──────────■──────────────────────────────────────╫─»\n",
       "«                       │P(π/512)                                         ║ »\n",
       "«q122_9: ───────────────■─────────────────────────────────────────────────╫─»\n",
       "«                                                                         ║ »\n",
       "«c25: 10/═════════════════════════════════════════════════════════════════╩═»\n",
       "«                                                                         0 »\n",
       "«                                                                             »\n",
       "«q122_0: ─────────────────────────────────────────────────────────────────────»\n",
       "«                                                     ┌─┐                     »\n",
       "«q122_1: ─■───────────────────────────────────────────┤M├─────────────────────»\n",
       "«         │                                           └╥┘                     »\n",
       "«q122_2: ─┼──────────■─────────────────────────────────╫──■───────────────────»\n",
       "«         │          │                                 ║  │                   »\n",
       "«q122_3: ─┼──────────┼─────────■───────────────────────╫──┼──────────■────────»\n",
       "«         │          │         │                       ║  │          │        »\n",
       "«q122_4: ─┼──────────┼─────────┼─────────■─────────────╫──┼──────────┼────────»\n",
       "«         │          │         │         │       ┌───┐ ║  │          │        »\n",
       "«q122_5: ─┼──────────┼─────────┼─────────┼───────┤ H ├─╫──┼──────────┼────────»\n",
       "«         │          │         │         │P(π/4) └───┘ ║  │          │        »\n",
       "«q122_6: ─┼──────────┼─────────┼─────────■─────────────╫──┼──────────┼────────»\n",
       "«         │          │         │P(π/16)                ║  │          │        »\n",
       "«q122_7: ─┼──────────┼─────────■───────────────────────╫──┼──────────┼────────»\n",
       "«         │          │P(π/64)                          ║  │          │P(π/32) »\n",
       "«q122_8: ─┼──────────■─────────────────────────────────╫──┼──────────■────────»\n",
       "«         │P(π/256)                                    ║  │P(π/128)           »\n",
       "«q122_9: ─■────────────────────────────────────────────╫──■───────────────────»\n",
       "«                                                      ║                      »\n",
       "«c25: 10/══════════════════════════════════════════════╩══════════════════════»\n",
       "«                                                      1                      »\n",
       "«                                                                            »\n",
       "«q122_0: ────────────────────────────────────────────────────────────────────»\n",
       "«                                                                            »\n",
       "«q122_1: ────────────────────────────────────────────────────────────────────»\n",
       "«                          ┌─┐                                               »\n",
       "«q122_2: ──────────────────┤M├───────────────────────────────────────────────»\n",
       "«                          └╥┘                                  ┌─┐          »\n",
       "«q122_3: ───────────────────╫──■────────────────────────────────┤M├──────────»\n",
       "«                           ║  │                                └╥┘          »\n",
       "«q122_4: ─■─────────────────╫──┼─────────■───────────────────────╫──■────────»\n",
       "«         │                 ║  │         │                       ║  │        »\n",
       "«q122_5: ─┼────────■────────╫──┼─────────┼─────────■─────────────╫──┼────────»\n",
       "«         │        │P(π/2)  ║  │         │         │       ┌───┐ ║  │        »\n",
       "«q122_6: ─┼────────■────────╫──┼─────────┼─────────┼───────┤ H ├─╫──┼────────»\n",
       "«         │P(π/8)           ║  │         │         │P(π/4) └───┘ ║  │        »\n",
       "«q122_7: ─■─────────────────╫──┼─────────┼─────────■─────────────╫──┼────────»\n",
       "«                           ║  │         │P(π/16)                ║  │        »\n",
       "«q122_8: ───────────────────╫──┼─────────■───────────────────────╫──┼────────»\n",
       "«                           ║  │P(π/64)                          ║  │P(π/32) »\n",
       "«q122_9: ───────────────────╫──■─────────────────────────────────╫──■────────»\n",
       "«                           ║                                    ║           »\n",
       "«c25: 10/═══════════════════╩════════════════════════════════════╩═══════════»\n",
       "«                           2                                    3           »\n",
       "«                                                                             »\n",
       "«q122_0: ─────────────────────────────────────────────────────────────────────»\n",
       "«                                                                             »\n",
       "«q122_1: ─────────────────────────────────────────────────────────────────────»\n",
       "«                                                                             »\n",
       "«q122_2: ─────────────────────────────────────────────────────────────────────»\n",
       "«                                                                             »\n",
       "«q122_3: ─────────────────────────────────────────────────────────────────────»\n",
       "«                          ┌─┐                                                »\n",
       "«q122_4: ──────────────────┤M├────────────────────────────────────────────────»\n",
       "«                          └╥┘                        ┌─┐                     »\n",
       "«q122_5: ─■─────────────────╫──■──────────────────────┤M├─────────────────────»\n",
       "«         │                 ║  │                      └╥┘                  ┌─┐»\n",
       "«q122_6: ─┼────────■────────╫──┼─────────■─────────────╫──■────────────────┤M├»\n",
       "«         │        │P(π/2)  ║  │         │       ┌───┐ ║  │                └╥┘»\n",
       "«q122_7: ─┼────────■────────╫──┼─────────┼───────┤ H ├─╫──┼────────■────────╫─»\n",
       "«         │P(π/8)           ║  │         │P(π/4) └───┘ ║  │        │P(π/2)  ║ »\n",
       "«q122_8: ─■─────────────────╫──┼─────────■─────────────╫──┼────────■────────╫─»\n",
       "«                           ║  │P(π/16)                ║  │P(π/8)           ║ »\n",
       "«q122_9: ───────────────────╫──■───────────────────────╫──■─────────────────╫─»\n",
       "«                           ║                          ║                    ║ »\n",
       "«c25: 10/═══════════════════╩══════════════════════════╩════════════════════╩═»\n",
       "«                           4                          5                    6 »\n",
       "«                                             \n",
       "«q122_0: ─────────────────────────────────────\n",
       "«                                             \n",
       "«q122_1: ─────────────────────────────────────\n",
       "«                                             \n",
       "«q122_2: ─────────────────────────────────────\n",
       "«                                             \n",
       "«q122_3: ─────────────────────────────────────\n",
       "«                                             \n",
       "«q122_4: ─────────────────────────────────────\n",
       "«                                             \n",
       "«q122_5: ─────────────────────────────────────\n",
       "«                                             \n",
       "«q122_6: ─────────────────────────────────────\n",
       "«                      ┌─┐                    \n",
       "«q122_7: ─■────────────┤M├────────────────────\n",
       "«         │       ┌───┐└╥┘         ┌─┐        \n",
       "«q122_8: ─┼───────┤ H ├─╫──■───────┤M├────────\n",
       "«         │P(π/4) └───┘ ║  │P(π/2) └╥┘┌───┐┌─┐\n",
       "«q122_9: ─■─────────────╫──■────────╫─┤ H ├┤M├\n",
       "«                       ║           ║ └───┘└╥┘\n",
       "«c25: 10/═══════════════╩═══════════╩═══════╩═\n",
       "«                       7           8       9 "
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Run factorization algorithm on 390\n",
    "highest_probable, result, counts, drawing = factorize(390)\n",
    "drawing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 700x500 with 1 Axes>"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plot_histogram(counts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "No Quantum Computers available with 104 qubits.\n",
      "Using Quantum Computer Simulator\n",
      "Your job is number 1 in the queue on qasm_simulator.  Please wait...\n",
      "COMPLETED in 0.004358530044555664 seconds on qasm_simulator\n",
      "The probable result was 1 with 50.5859375% probability\n",
      "Your statement 'Nothing is real' is False.\n"
     ]
    }
   ],
   "source": [
    "answer = alethiometer(statement=\"Nothing is real\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "No Quantum Computers available with 89 qubits.\n",
      "Using Quantum Computer Simulator\n",
      "Your job is number 1 in the queue on qasm_simulator.  Please wait...\n",
      "COMPLETED in 0.004261016845703125 seconds on qasm_simulator\n",
      "The probable result was 1 with 50.537109375% probability\n",
      "Your statement 'I am not real' is False.\n"
     ]
    }
   ],
   "source": [
    "answer = alethiometer(statement=\"I am not real\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "No Quantum Computers available with 110 qubits.\n",
      "Using Quantum Computer Simulator\n",
      "Your job is number 1 in the queue on qasm_simulator.  Please wait...\n",
      "COMPLETED in 0.0043926239013671875 seconds on qasm_simulator\n",
      "The probable result was 0 with 50.5615234375% probability\n",
      "Your statement 'You are not real' is True.\n"
     ]
    }
   ],
   "source": [
    "answer = alethiometer(statement=\"You are not real\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "No Quantum Computers available with 186 qubits.\n",
      "Using Quantum Computer Simulator\n",
      "Your job is number 1 in the queue on qasm_simulator.  Please wait...\n",
      "COMPLETED in 0.0042629241943359375 seconds on qasm_simulator\n",
      "The probable result was 1 with 51.123046875% probability\n",
      "Your statement 'I should eat less chocolate' is False.\n"
     ]
    }
   ],
   "source": [
    "answer = alethiometer(statement=\"I should eat less chocolate\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "False\n"
     ]
    }
   ],
   "source": [
    "print(answer)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "No Quantum Computers available with 96 qubits.\n",
      "Using Quantum Computer Simulator\n",
      "Your job is number 1 in the queue on qasm_simulator.  Please wait...\n",
      "COMPLETED in 0.004370927810668945 seconds on qasm_simulator\n",
      "The probable result was 1 with 50.68359375% probability\n",
      "Your statement 'Is true false?' is False.\n"
     ]
    }
   ],
   "source": [
    "answer = alethiometer(statement=\"Is true false?\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "No Quantum Computers available with 89 qubits.\n",
      "Using Quantum Computer Simulator\n",
      "Your job is number 1 in the queue on qasm_simulator.  Please wait...\n",
      "COMPLETED in 0.00643610954284668 seconds on qasm_simulator\n",
      "The probable result was 0 with 50.2685546875% probability\n",
      "Your statement 'Is true true?' is True.\n"
     ]
    }
   ],
   "source": [
    "answer = alethiometer(statement=\"Is true true?\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Quantum Computer ibmq_lima has 5 qubits, but we need 7 qubits\n",
      "Quantum Computer ibmq_belem has 5 qubits, but we need 7 qubits\n",
      "Quantum Computer ibmq_quito has 5 qubits, but we need 7 qubits\n",
      "Quantum Computer ibmq_manila has 5 qubits, but we need 7 qubits\n",
      "Quantum Computer ibm_nairobi has 33 queued jobs and 7 qubits\n",
      "Quantum Computer ibm_oslo has 16 queued jobs and 7 qubits\n",
      "Using Quantum Computer: ibm_oslo with 16 queued jobs\n",
      "Your job is number 17 in the queue on ibm_oslo.  Please wait...\n",
      "Successful completion in 7.083366870880127 seconds on ibm_oslo\n",
      "The probable result was 0 with 51.46484375% probability\n",
      "Your statement '?' is True.\n"
     ]
    },
    {
     "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",
       "q25_0: ┤ H ├┤ X ├──■──┤ X ├┤ H ├┤M├\n",
       "       ├───┤├───┤  │  ├───┤├───┤└╥┘\n",
       "q25_1: ┤ H ├┤ X ├──┼──┤ X ├┤ H ├─╫─\n",
       "       ├───┤├───┤  │  ├───┤├───┤ ║ \n",
       "q25_2: ┤ H ├┤ X ├──┼──┤ X ├┤ H ├─╫─\n",
       "       ├───┤├───┤  │  ├───┤├───┤ ║ \n",
       "q25_3: ┤ H ├┤ X ├──┼──┤ X ├┤ H ├─╫─\n",
       "       ├───┤├───┤  │  ├───┤├───┤ ║ \n",
       "q25_4: ┤ H ├┤ X ├──┼──┤ X ├┤ H ├─╫─\n",
       "       ├───┤├───┤  │  ├───┤├───┤ ║ \n",
       "q25_5: ┤ H ├┤ X ├──┼──┤ X ├┤ H ├─╫─\n",
       "       └───┘└───┘┌─┴─┐└───┘└───┘ ║ \n",
       "q25_6: ──────────┤ X ├───────────╫─\n",
       "                 └───┘           ║ \n",
       " c5: 1/══════════════════════════╩═\n",
       "                                 0 </pre>"
      ],
      "text/plain": [
       "       ┌───┐┌───┐     ┌───┐┌───┐┌─┐\n",
       "q25_0: ┤ H ├┤ X ├──■──┤ X ├┤ H ├┤M├\n",
       "       ├───┤├───┤  │  ├───┤├───┤└╥┘\n",
       "q25_1: ┤ H ├┤ X ├──┼──┤ X ├┤ H ├─╫─\n",
       "       ├───┤├───┤  │  ├───┤├───┤ ║ \n",
       "q25_2: ┤ H ├┤ X ├──┼──┤ X ├┤ H ├─╫─\n",
       "       ├───┤├───┤  │  ├───┤├───┤ ║ \n",
       "q25_3: ┤ H ├┤ X ├──┼──┤ X ├┤ H ├─╫─\n",
       "       ├───┤├───┤  │  ├───┤├───┤ ║ \n",
       "q25_4: ┤ H ├┤ X ├──┼──┤ X ├┤ H ├─╫─\n",
       "       ├───┤├───┤  │  ├───┤├───┤ ║ \n",
       "q25_5: ┤ H ├┤ X ├──┼──┤ X ├┤ H ├─╫─\n",
       "       └───┘└───┘┌─┴─┐└───┘└───┘ ║ \n",
       "q25_6: ──────────┤ X ├───────────╫─\n",
       "                 └───┘           ║ \n",
       " c5: 1/══════════════════════════╩═\n",
       "                                 0 "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "answer,drawing = alethiometer(statement=\"?\")\n",
    "drawing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Quantum Computer ibmq_lima has 5 qubits, but we need 7 qubits\n",
      "Quantum Computer ibmq_belem has 5 qubits, but we need 7 qubits\n",
      "Quantum Computer ibmq_quito has 5 qubits, but we need 7 qubits\n",
      "Quantum Computer ibmq_manila has 5 qubits, but we need 7 qubits\n",
      "Quantum Computer ibm_nairobi has 19 queued jobs and 7 qubits\n",
      "Quantum Computer ibm_oslo has 16 queued jobs and 7 qubits\n",
      "Using Quantum Computer: ibm_oslo with 16 queued jobs\n",
      "Your job is number 17 in the queue on ibm_oslo.  Please wait...\n",
      "Successful completion in 6.294996500015259 seconds on ibm_oslo\n",
      "The probable result was 0 with 50.9033203125% probability\n",
      "Your statement '?' is True.\n"
     ]
    },
    {
     "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",
       "q30_0: ┤ H ├┤ X ├──■──┤ X ├┤ H ├┤M├\n",
       "       ├───┤├───┤  │  ├───┤├───┤└╥┘\n",
       "q30_1: ┤ H ├┤ X ├──┼──┤ X ├┤ H ├─╫─\n",
       "       ├───┤├───┤  │  ├───┤├───┤ ║ \n",
       "q30_2: ┤ H ├┤ X ├──┼──┤ X ├┤ H ├─╫─\n",
       "       ├───┤├───┤  │  ├───┤├───┤ ║ \n",
       "q30_3: ┤ H ├┤ X ├──┼──┤ X ├┤ H ├─╫─\n",
       "       ├───┤├───┤  │  ├───┤├───┤ ║ \n",
       "q30_4: ┤ H ├┤ X ├──┼──┤ X ├┤ H ├─╫─\n",
       "       ├───┤├───┤  │  ├───┤├───┤ ║ \n",
       "q30_5: ┤ H ├┤ X ├──┼──┤ X ├┤ H ├─╫─\n",
       "       └───┘└───┘┌─┴─┐└───┘└───┘ ║ \n",
       "q30_6: ──────────┤ X ├───────────╫─\n",
       "                 └───┘           ║ \n",
       " c6: 1/══════════════════════════╩═\n",
       "                                 0 </pre>"
      ],
      "text/plain": [
       "       ┌───┐┌───┐     ┌───┐┌───┐┌─┐\n",
       "q30_0: ┤ H ├┤ X ├──■──┤ X ├┤ H ├┤M├\n",
       "       ├───┤├───┤  │  ├───┤├───┤└╥┘\n",
       "q30_1: ┤ H ├┤ X ├──┼──┤ X ├┤ H ├─╫─\n",
       "       ├───┤├───┤  │  ├───┤├───┤ ║ \n",
       "q30_2: ┤ H ├┤ X ├──┼──┤ X ├┤ H ├─╫─\n",
       "       ├───┤├───┤  │  ├───┤├───┤ ║ \n",
       "q30_3: ┤ H ├┤ X ├──┼──┤ X ├┤ H ├─╫─\n",
       "       ├───┤├───┤  │  ├───┤├───┤ ║ \n",
       "q30_4: ┤ H ├┤ X ├──┼──┤ X ├┤ H ├─╫─\n",
       "       ├───┤├───┤  │  ├───┤├───┤ ║ \n",
       "q30_5: ┤ H ├┤ X ├──┼──┤ X ├┤ H ├─╫─\n",
       "       └───┘└───┘┌─┴─┐└───┘└───┘ ║ \n",
       "q30_6: ──────────┤ X ├───────────╫─\n",
       "                 └───┘           ║ \n",
       " c6: 1/══════════════════════════╩═\n",
       "                                 0 "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "answer,drawing = alethiometer(statement=\"?\")\n",
    "drawing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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