kakuro.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "e6933078-bc02-471c-b4f5-4829dd33fdcb",
   "metadata": {},
   "source": [
    "# Studies on solving Kakuro puzzle with Grover algorithm"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e37734b6-40c8-4df4-9472-f7475d1235dd",
   "metadata": {},
   "source": [
    "Valter Uotila, PhD student, University of Helsinki"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5913bac1-a4e0-40ee-9842-dde3e0853ade",
   "metadata": {},
   "source": [
    "Note: if you rerun specific cells without restarting the kernel, there might be unexpected behavior and errors in how the notebook handles the variables.\n",
    "\n",
    "As the discussion in Classiq's channel showed, this problem has been somewhat confusing. If we read the problem definition very carefully, it does not describe what kind of Grover algorithm application it should be. For example, saying ''You are not allowed to solve the puzzle manually'' seems to be meaning actual manual solving by the person. On the other hand, it could also mean that any classical preparation is not allowed. Still, the discussion forum also hints that it is sufficient to encode the constraints in Grover's oracle, which is, in my opinion, a lot easier task than writing an oracle that genuinely solves the puzzles.\n",
    "\n",
    "Not caring so much about the exact problem, I developed various solutions to the challenge. First, I represent the solution that I consider interesting in how it encodes the variables and values. Instead of using a massive number of qubits, we should design algorithms to take advantage of the exponentially growing space of states. Hilbert's space is indeed large, and we should benefit from that. The first solution's downside is that it is not precisely Grover's algorithm that solves the problem. In my opinion, Grover's algorithm instead reads quantumly encoded solution from the black box oracle circuit. That is in line with Grover's original idea of finding the marked element from an unsorted list. Now it depends on the problem definition if this is allowed or not. I developed the solution so that you can input any (there must be some corner cases that fail) Kakuro problem. Then the algorithm classically creates the oracle, and Grover's algorithm ''reads'' the solution from the oracle.\n",
    "\n",
    "The second solution follows the idea that is also represented in Qiskit documentation: [Solving Sudoku using Grover's Algorithm](https://qiskit.org/textbook/ch-algorithms/grover.html#5.-Solving-Sudoku-using-Grover's-Algorithm-). I believe that this is the idea that has been in the developers' minds while designing the Kakuro challenge. Grover's algorithm solves the problem unlike the previous case, but the downside is the circuit's large qubit number and depth. \n",
    "\n",
    "I partly participate in the competitions because they motivate me to study new topics efficiently. I also wanted to consider the third option based on an exciting paper: [Grover Adaptive Search for Constrained Polynomial Binary Optimization](https://arxiv.org/abs/1912.04088)."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "94d05479-07b8-4079-ad9d-732efaa7df9c",
   "metadata": {},
   "source": [
    "Table of contents:\n",
    "- Solution proposal 1: Qubit and gate efficient approach following Grover's original idea\n",
    "- Solution proposal 2: Apply idea from Qiskit documentation\n",
    "- Solution proposal 3: Apply ideas from paper _Grover Adaptive Search for Constrained Polynomial Binary Optimization_"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9139c733-7762-422e-8c50-dd300638b260",
   "metadata": {},
   "source": [
    "## Transforming integer variables into binary variables"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8ddcc3f3-b444-44d3-aa16-c4643b819f03",
   "metadata": {},
   "source": [
    "The notebook utilizes a spesific binary variable encoding. Let us assume that we have $n$ variables $x_i$ for $i = 0, \\ldots, n - 1$. To have consistent notation we start indexing from $0$. Now each of these variables can have a value $0,1,2,3$. In order to express to problem using the binary variables, we define binary variables $x_{i, j}$ so that $i = 0, \\ldots, n - 1$ and $j \\in \\left\\{0,1,2,3 \\right\\}$. The interpretation of the binary variables is simple: $x_{i,j} = 1$ if $x_i = j$. This enables us to encode integers into binary model. We have $4n$ binary variables in the final model."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fe75b82c-f4ca-414f-9191-a8edf636cae0",
   "metadata": {
    "tags": []
   },
   "source": [
    "## Solution proposal 1: Qubit and gate efficient approach following Grover's original idea"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b8c24811-2dfc-4e9a-8e52-38e053360724",
   "metadata": {},
   "source": [
    "The first idea of how to tackle the challenge is to assign a state for each binary variable $x_{i,j}$. In the end, if we measure $x_{i,j} = 1$, we know that $x_i = j$. For example, the variable $x_{0,0}$ can correspond to the state $|0000\\rangle$ in the case $n = 4$. Because the number of states grows exponentially, we would be able to represent large problems with a small number of qubits. If we can transform the constraints into Grover oracle, which favors those states that correspond to the problem's solution, this would solve the problem. Anyway, constructing such an oracle generally seems a complicated task.\n",
    "\n",
    "For me, this exercise shows an exciting and also confusing point about quantum oracles. Usually, quantum oracles encode the problems that we are solving. Oracles are black-box. In that sense, in this solution proposal, the quantum computer is just a machine that we use to read the result from the oracle."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "08b299bf-ed7b-4b90-abde-ac3c8ece9f40",
   "metadata": {},
   "source": [
    "### Studying constraint types"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "853541ad-2716-429b-ba97-0fa3150e5f92",
   "metadata": {},
   "source": [
    "We can divide the constaints into two classes. Because I want to be able to code the solution so that anyone without thinking anything simply inputs Kakuro constraints from the problem, I start by considering the simple example case from the problem definition:\n",
    "$$\n",
    "\\begin{align}\n",
    "x_0 \\neq & x_1 &\\quad x_0 \\neq& x_2 \\\\\n",
    "x_1 \\neq & x_3 &\\quad x_2 \\neq& x_3 \\\\\n",
    "x_0 + x_1 =& 3 &\\quad x_0 + x_2 =& 3 \\\\\n",
    "x_2 + x_3 =& 4 &\\quad x_1 + x_3 =& 4.\n",
    "\\end{align}\n",
    "$$"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "19c5cf80-86d8-4eb5-b496-01c68c761bfe",
   "metadata": {},
   "source": [
    "#### Constraint type 1: inequality between two variables"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "157c52e9-90b7-4f97-b4d1-3a25973a9b7d",
   "metadata": {},
   "source": [
    "Obviously the first class of constraints is\n",
    "$$\n",
    "\\begin{align}\n",
    "x_0 \\neq & x_1 \\\\\n",
    "x_2 \\neq& x_3 \\\\\n",
    "x_0 \\neq& x_2 \\\\\n",
    "x_1 \\neq & x_3.\n",
    "\\end{align}\n",
    "$$\n",
    "\n",
    "Let's focus on the first constraint $x_0 \\neq x_1$. In the binary variable format, this constraint means that if $x_{i,j} = 1$ then $x_{k,j} = 0$ for $i = 0, 1$, $k = 0, 1$, $i \\neq k$ and for $j = 0,1,2,3$. In other words, if we flip the phase of the state corresponding the variable $x_{0,j}$ (meaning it is part of the solution), then we are not allowed to flip variable $x_{1,j}$. If we were to flip the both variables, then it would mean $x_0 = j = x_1$ which is not allowed. This analysis shows that we have multiple options how to flip the states corresponding the variables.\n",
    "\n",
    "So we proceed so that we initially create all the possible correct circuits for the constraint $x_0 \\neq x_1$. Then we proceed to the next constraint $x_2 \\neq x_3$ and append the possible options to the circuits produced in the previous step. At each appending phase, we check if some of the previous constraints are violeted. If a constraint is violeted, we drop the circuit from the process. Finally, we are left with Grover oracles that produce the correct solution to the problem. Note that this process produces all the correct circuit and for this simple example there are two of them."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b6ef5f44-0085-4e80-ac46-70ee0ce19cf1",
   "metadata": {},
   "source": [
    "#### Constraint type 2: sum of variables with equality to constant "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7e2cd1a6-0619-4d43-b6cc-b34f1d9c0b8e",
   "metadata": {},
   "source": [
    "Clearly the second class of constraints is\n",
    "$$\n",
    "\\begin{align}\n",
    "x_0 + x_1 =& 3 \\\\\n",
    "x_2 + x_3 =& 4 \\\\\n",
    "x_0 + x_2 =& 3 \\\\\n",
    "x_1 + x_3 =& 4.\n",
    "\\end{align}\n",
    "$$\n",
    "\n",
    "Again, let's focus on the constraint $x_0 + x_1 = 3$. This means that we have two variables whose values sum up to three. We can divide this into multiple cases:\n",
    "\n",
    "- $x_0 = 0$ and $x_1 = 3$\n",
    "- $x_0 = 3$ and $x_1 = 0$\n",
    "- $x_0 = 1$ and $x_1 = 2$\n",
    "- $x_0 = 2$ and $x_1 = 1$\n",
    "\n",
    "Luckily variables can hold numbers only up to three so maximum value that we can face on the right-hand side of the equality is $6$. Also, it is safe to assume that we do not sum more than three variables at the time and all the variables are different since they appear on the same row or column in the puzzle. This reduces the number of combinations.\n",
    "\n",
    "Let's study the case that we want to encode $x_0 = 0$ and $x_1 = 3$. This means that we want to set $x_{0,0} = 1$ and $x_{1,3} = 1$ as binary variables. If we have encoded some constraints before this constraint, flipping the states of these variables might violate some previous constraints. That allows us to drop certain oracles from the construction process. Now we are only left to concretely code this approach."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "50026cd0-884e-46df-b528-75747b9402dc",
   "metadata": {},
   "source": [
    "### Implementation"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "db05f335-9266-4a64-bedf-df244e687724",
   "metadata": {},
   "source": [
    "This notebook utilizes three large quantum coding packages: Qiskit, Pennylane and Ocean. Besides, we are utilizing Pennylane's Cirq and Braket plugins as well as Braket plugin for Ocean."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "b287e5e1-2e27-431d-b967-08b432cd3221",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Install a pip package in the current Jupyter kernel\n",
    "#import sys\n",
    "#!{sys.executable} -m pip install numpy\n",
    "#!{sys.executable} -m pip install matplotlib\n",
    "\n",
    "#!{sys.executable} -m pip install pennylane\n",
    "#!{sys.executable} -m pip install qiskit\n",
    "#!{sys.executable} -m pip install dwave-ocean-sdk\n",
    "\n",
    "#!{sys.executable} -m pip install amazon-braket-sdk\n",
    "#!{sys.executable} -m pip install amazon-braket-pennylane-plugin\n",
    "#!{sys.executable} -m pip install pennylane-cirq"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "19b60543-172b-4fb8-b777-dfdd538ebd75",
   "metadata": {},
   "outputs": [],
   "source": [
    "from qiskit import QuantumCircuit, QuantumRegister\n",
    "from qiskit.circuit.library.standard_gates import MCXGate, XGate, CCXGate, TGate\n",
    "import numpy as np\n",
    "import copy"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5b2fd9d2-b7d0-49e0-b289-e32aca5aa1e2",
   "metadata": {},
   "source": [
    "If we concentrate the main problem, the constraints are \n",
    "\n",
    "$$\n",
    "\\begin{align}\n",
    "x_0 &\\neq x_1 &\\quad x_0 &\\neq x_2 &\\quad x_1 &\\neq x_3 \\\\\n",
    "x_1 &\\neq x_5 &\\quad x_2 &\\neq x_3 &\\quad x_2 &\\neq x_4 \\\\\n",
    "x_3 &\\neq x_4 &\\quad x_3 &\\neq x_5 &\\quad x_4 &\\neq x_6 \\\\\n",
    "x_5 &\\neq x_6 &&&& \\\\\n",
    "x_0 + x_2 &= 5 &\\quad x_1 + x_3 + x_5 &= 3 &\\quad x_4 + x_6 &= 1 \\\\\n",
    "x_2 + x_3 + x_4 &= 5 &\\quad x_0 + x_1 &= 3 &\\quad x_5 + x_6 &= 1.\n",
    "\\end{align}\n",
    "$$\n",
    "\n",
    "For four variables we have $4*4 = 16$ binary variables which requires $16$ states so we need exactly four query qubits in Grover's algorithm. For six variables we need five query qubits.\n",
    "\n",
    "You can uncomment and comment the cells below depending on which Kakuro puzzle you want to execute."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "31f4b5b2-00a3-453e-ba2d-3dfa712935a3",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Development and debugging purposes\n",
    "\n",
    "#query_qubits = 4\n",
    "#n_variables = 4\n",
    "\n",
    "#inequality_constraints = [(0,1), (2,3), (1,3), (0,2)]\n",
    "#sum_equality_constraints = [{'variables': [0, 1], 'sum': 3}, {'variables': [2, 3], 'sum': 4}, {'variables': [0, 2], 'sum': 3}, {'variables': [1, 3], 'sum': 4}]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "0c21ab74-8627-49e2-b085-4d3b87cb3679",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Classicq's actual Kakuro puzzle\n",
    "\n",
    "query_qubits = 5\n",
    "n_variables = 7\n",
    "\n",
    "inequality_constraints = [(0,1), (0,2), (1,3), (1,5), (2,3), (2,4), (3,4), (3,5), (4,6), (5,6)]\n",
    "sum_equality_constraints = [{'variables': [0, 2], 'sum': 5}, {'variables': [1, 3, 5], 'sum': 3}, {'variables': [4, 6], 'sum': 1}, {'variables': [2, 3, 4], 'sum': 5}, {'variables': [0, 1], 'sum': 3}, {'variables': [5, 6], 'sum': 1}]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2d749f85-2a81-42c5-ba43-2a39892396ae",
   "metadata": {},
   "source": [
    "The following injective function maps the binary variables $x_{i,j}$ to integers whose binary representations correspond to states. We return the binary representation as a list since we will need to access each element in the representation later."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "5da13b1b-614e-4b72-8d1e-138ba52c3e41",
   "metadata": {},
   "outputs": [],
   "source": [
    "def variable_to_state(i,j):\n",
    "    int_repr = 4*i + j\n",
    "    bin_repr = np.binary_repr(int_repr)\n",
    "    return '0'*(query_qubits - len(bin_repr)) + bin_repr\n",
    "\n",
    "\n",
    "def state_to_variable(state):\n",
    "    decimal = int(state, 2)\n",
    "    j = decimal % 4\n",
    "    i = int(np.floor(decimal/4))\n",
    "    return (i, j)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "f183675d-dcce-4fe8-8f6f-bb77fcdbba78",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "01110\n",
      "(3, 2)\n"
     ]
    }
   ],
   "source": [
    "# Encodes if x_3 = 2 i.e. x_3 = 2 is True if |1110> is True after measurement \n",
    "print(variable_to_state(3,2))\n",
    "print(state_to_variable(variable_to_state(3,2)))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f111e828-86bf-4d4f-b322-18f5b081f12e",
   "metadata": {},
   "source": [
    "We proceed with the following idea: Every constraint induces options how to flip phases. Whatever we inlcude in the circuit, we need to exclude something. Consider the example if $x_{0,1} = 1$ then $x_{1,1} = 0$. When we include the gate that encodes the solution $x_{0,1} = 1$, then we cannot use the gate encoding $x_{1,1} = 1$ at any part of the circuit later. This process reduces the correct oracles."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "bfec5800-1162-47f6-bf12-991ede592ab4",
   "metadata": {},
   "outputs": [],
   "source": [
    "def compose_phase_flip_gate(i, j, circuit):\n",
    "    ctrl = variable_to_state(i,j)\n",
    "    circuit.add(ctrl)\n",
    "    return circuit\n",
    "\n",
    "\n",
    "def expand_inequality_oracles(x, y, oracles):\n",
    "    new_oracles = []\n",
    "    for oracle in oracles:\n",
    "        for j in range(4):\n",
    "            oracle_copy = copy.deepcopy(oracle)\n",
    "            bin_table = oracle_copy['bin_table']\n",
    "            if bin_table[x][j] != -1 and bin_table[y][j] != 1:\n",
    "                bin_table[x][j] = 1\n",
    "                for k in range(4):\n",
    "                    if k != j:\n",
    "                        bin_table[x][k] = -1\n",
    "                bin_table[y][j] = -1\n",
    "                circuit = oracle_copy['circuit']\n",
    "                compose_phase_flip_gate(x, j, circuit)\n",
    "                new_oracles.append(oracle_copy)\n",
    "    return new_oracles\n",
    "\n",
    "\n",
    "def initialize_oracles(x, y, oracles):\n",
    "    for j in range(4):\n",
    "        circuit = compose_phase_flip_gate(x, j, set())\n",
    "        bin_table = np.zeros(shape=(n_variables, 4))\n",
    "        bin_table[x][j] = 1\n",
    "        for k in range(4):\n",
    "            if k != j:\n",
    "                bin_table[x][k] = -1\n",
    "        bin_table[y][j] = -1\n",
    "        oracles.append({'circuit': circuit, 'bin_table' : bin_table })\n",
    "    return oracles\n",
    "\n",
    "\n",
    "def create_inequality_oracles(constraints):\n",
    "    oracles = []\n",
    "    x = constraints[0][0]\n",
    "    y = constraints[0][1]\n",
    "    oracles = initialize_oracles(x, y, oracles)\n",
    "    oracles = initialize_oracles(y, x, oracles)\n",
    "    \n",
    "    for constraint in constraints[1:]:\n",
    "        x = constraint[0]\n",
    "        y = constraint[1]\n",
    "        oracles = expand_inequality_oracles(x, y, oracles)\n",
    "        oracles = expand_inequality_oracles(y, x, oracles)\n",
    "    return oracles"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "2a7da0d9-0b69-402f-bcc3-258a0c4117ef",
   "metadata": {},
   "outputs": [],
   "source": [
    "oracles = create_inequality_oracles(inequality_constraints)\n",
    "# print(len(oracles))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2169909e-de59-44c2-b4f2-7e515bc72e11",
   "metadata": {},
   "source": [
    "The following function calculates how many different ways we can sum two or three variables so that we obtained the given sum. We can exclude the cases when there are two equal number in the sum since these cases will always violate some of the inequality constraints."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "e4a336fe-58f3-47b5-b9a8-c043162b14a1",
   "metadata": {},
   "outputs": [],
   "source": [
    "def sum_options(constraint):\n",
    "    variables = constraint['variables']\n",
    "    sum_value = constraint['sum']\n",
    "    result = []\n",
    "    \n",
    "    if len(variables) == 2:\n",
    "        for i in range(4):\n",
    "            if sum_value - i < 4 and sum_value - i > -1:\n",
    "                if i != sum_value - i:\n",
    "                    result.append((i, sum_value - i))\n",
    "    elif len(variables) == 3:\n",
    "        for i in range(4):\n",
    "            if sum_value - i < 4 and sum_value - i > -1:\n",
    "                options = sum_options({'variables': [0, 1], 'sum': i})\n",
    "                for o in options:\n",
    "                    if (sum_value - i) != o[0] and (sum_value - i) != o[1] and o[0] != o[1]:\n",
    "                        result.append((sum_value - i, o[0], o[1]))\n",
    "                        result.append((o[0], sum_value - i, o[1]))\n",
    "                        result.append((o[0], o[1], sum_value - i))\n",
    "                \n",
    "    return list(set(result))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "d827f81d-8f82-49e9-8ff0-f1b86c87e196",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[(0, 2, 1), (1, 2, 0), (2, 1, 0), (2, 0, 1), (0, 1, 2), (1, 0, 2)]\n",
      "[(0, 1), (1, 0)]\n"
     ]
    }
   ],
   "source": [
    "print(sum_options({'variables': [2, 3, 4], 'sum': 3}))\n",
    "#print(sum_options({'variables': [2, 3, 4], 'sum': 4}))\n",
    "#print(sum_options({'variables': [2, 3, 4], 'sum': 5}))\n",
    "#print(sum_options({'variables': [2, 3, 4], 'sum': 6}))\n",
    "#print()\n",
    "print(sum_options({'variables': [0, 1], 'sum': 1}))\n",
    "#print(sum_options({'variables': [0, 1], 'sum': 2}))\n",
    "#print(sum_options({'variables': [0, 1], 'sum': 3}))\n",
    "#print(sum_options({'variables': [0, 1], 'sum': 4}))\n",
    "#print(sum_options({'variables': [0, 1], 'sum': 5}))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "35eb964f-137e-4ad4-b965-d8fc83197456",
   "metadata": {},
   "source": [
    "We then update the binary variable tables and the corresponding circuits based on the different combinations that summing creates."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "242343a1-cc4d-4c55-8377-e7cdcca2a0aa",
   "metadata": {},
   "outputs": [],
   "source": [
    "def limit_oracles_based_on_sum_constraints(sums, constraint, oracles):\n",
    "    correct_oracles = []\n",
    "    variables = constraint['variables']\n",
    "    for oracle in oracles:\n",
    "        for option in sums:\n",
    "            bin_table = oracle['bin_table']\n",
    "            if all([bin_table[variables[k]][option[k]] == 1 for k in range(len(variables))]):\n",
    "                correct_oracles.append(oracle)\n",
    "    return correct_oracles\n",
    "    \n",
    "\n",
    "def build_sum_oracle(constraint, oracles):\n",
    "    sums = sum_options(constraint)\n",
    "    oracles = limit_oracles_based_on_sum_constraints(sums, constraint, oracles)\n",
    "    return oracles"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "bdb095a6-44d2-428a-ac7a-292575d5285f",
   "metadata": {},
   "outputs": [],
   "source": [
    "for constraint in sum_equality_constraints:\n",
    "    oracles = build_sum_oracle(constraint, oracles)\n",
    "    #print(len(oracles))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "fc0786fd-3cb3-4264-a8b1-5eeaf12e0691",
   "metadata": {},
   "outputs": [],
   "source": [
    "def remove_duplicates(oracles):\n",
    "    new_oracles = [oracles[0]]\n",
    "    for oracle in oracles[1:]:\n",
    "        if not any([np.array_equal(oracle['bin_table'], oracle2['bin_table']) for oracle2 in new_oracles]):\n",
    "            new_oracles.append(oracle)\n",
    "    return new_oracles"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "ccaa38d5-eb53-4eee-b92b-c7c62c62f5a6",
   "metadata": {},
   "outputs": [],
   "source": [
    "oracles = remove_duplicates(oracles)\n",
    "\n",
    "#print(oracles)\n",
    "#for b in oracles[0]['circuit']:\n",
    "#    print(state_to_variable(b))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "97e4aa66-bc6d-447d-9e9a-241a6f4c9c65",
   "metadata": {},
   "source": [
    "### Implement Grover search"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "6f0f4d67-b738-4d8e-90b2-3f4f937d4663",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "from qiskit import IBMQ, Aer, assemble, transpile\n",
    "from qiskit import QuantumCircuit, ClassicalRegister, QuantumRegister\n",
    "from qiskit.visualization import plot_histogram"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "dd08931e-3410-4de3-ba17-f988584e5630",
   "metadata": {},
   "outputs": [],
   "source": [
    "def initialize(qc, qubits):\n",
    "    qc.x(qubits[-1])\n",
    "    qc.h(qubits)\n",
    "    return qc"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fa70a43a-5da6-419c-9fda-2b62ba4f59d3",
   "metadata": {},
   "source": [
    "The following function creates the oracle for the correct states."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "477b0609-0ac1-441d-958a-fa8a9be5ddaf",
   "metadata": {},
   "outputs": [],
   "source": [
    "def compose_grover_oracle(qc, qubits, ctrl_char):\n",
    "    not_indices = []\n",
    "    qc.barrier()\n",
    "    for i, b in enumerate(ctrl_char):\n",
    "        if b == '0':\n",
    "            qc.x(len(qubits) - 2 - i)\n",
    "            not_indices.append(len(qubits) - 2 - i)\n",
    "            \n",
    "    qc.mcx(list(qubits[:-1]), len(qubits) - 1)\n",
    "    \n",
    "    for i in not_indices:\n",
    "        qc.x(i)\n",
    "    return qc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "d9942a6a-e0a5-48e6-9cbb-5d63cbaede5a",
   "metadata": {},
   "outputs": [],
   "source": [
    "def diffusion(qc, qubits):\n",
    "    qc.barrier()\n",
    "    qc.h(qubits)\n",
    "    qc.x(qubits[:-1])\n",
    "    qc.mcx(list(qubits[:-1]), len(qubits) - 1)\n",
    "    qc.x(qubits[:-1])\n",
    "    qc.h(qubits)\n",
    "    return qc"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8387daa0-b641-4b57-9b52-1eed0944a81c",
   "metadata": {},
   "source": [
    "The algorithm provides all the possible answers to the puzzle. The main Kakuro seems to have just single solution but the another example has two solutions. The both are run on a noisy simulator. In both cases just one oracle call seems sufficient. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "7c7c253e-29cb-4d43-8590-2e7596cc6df0",
   "metadata": {},
   "outputs": [],
   "source": [
    "final_circuits = []\n",
    "\n",
    "for oracle in oracles:\n",
    "    grover_circuit = QuantumCircuit(query_qubits + 1, query_qubits)\n",
    "    grover_circuit = initialize(grover_circuit, range(query_qubits + 1))\n",
    "    \n",
    "    for ctrl_char in oracle['circuit']:\n",
    "        grover_circuit = compose_grover_oracle(grover_circuit, range(query_qubits + 1), ctrl_char)\n",
    "\n",
    "    grover_circuit = diffusion(grover_circuit, range(query_qubits + 1))\n",
    "    final_circuits.append(grover_circuit)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "86401347-ee9d-4243-b010-3278c5f81475",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<PIL.Image.Image image mode=RGB size=1525x216 at 0x24D2BEC5F90>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_circuits[0].draw(output='latex')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "9a9e1228-b4c7-4b77-b396-1b78bb01b601",
   "metadata": {},
   "outputs": [],
   "source": [
    "solution_counts = []\n",
    "for grover_circuit in final_circuits:\n",
    "    grover_circuit.measure(range(query_qubits), range(query_qubits))\n",
    "    aer_sim = Aer.get_backend('aer_simulator', )\n",
    "    qobj = assemble(grover_circuit)\n",
    "    result = aer_sim.run(qobj, shots=100000).result()\n",
    "    counts = result.get_counts()\n",
    "    solution_counts.append(counts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "e0aa6261-8cc9-4dd0-b8f2-710ffc7d78c0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x864 with 1 Axes>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "legend = [str(i + 1) + '. execution' for i in range(len(solution_counts))]\n",
    "plot_histogram(solution_counts, legend=legend, sort='asc', figsize=(15,12))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "89ac08f9-7bee-494a-9239-2090c89c99cc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Possible solution to the Kakuro puzzle: \n",
      "x_4 = 0\n",
      "x_3 = 2\n",
      "x_1 = 1\n",
      "x_6 = 1\n",
      "x_0 = 2\n",
      "x_2 = 3\n",
      "x_5 = 0\n",
      "\n"
     ]
    }
   ],
   "source": [
    "for counts in solution_counts:\n",
    "    sorted_counts = dict(sorted(counts.items(), key = lambda item: item[1], reverse=True))\n",
    "    counts_iterator = iter(sorted_counts)\n",
    "    print(\"Possible solution to the Kakuro puzzle: \")\n",
    "    for i in range(n_variables):\n",
    "        b = next(counts_iterator)\n",
    "        (i, j) = state_to_variable(b)\n",
    "        print(\"x_\" + str(i) + \" = \" + str(j))\n",
    "    print()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "42a72e6a-bc13-46a4-9688-ab78a920c9ef",
   "metadata": {},
   "source": [
    "### Decompose MCX gates for actual Kakuro puzzle"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "67f352e5-8da4-42ed-b782-9b8cdb532eed",
   "metadata": {},
   "source": [
    "The solution above is easy to understand using multi-control Toffoli gates but since we need to follow the problem definition, we need to decompose the multi-control Toffoli gates into single qubit and CNOT gates. This is something that we already studied in the another challenge. Since the number of ancilla qubits is not restricted now, we can use [this simple decomposition](https://algassert.com/quirk#circuit={%22cols%22:[[%22X%22,%22X%22,%22X%22,%22X%22,%22X%22],[%22%E2%80%A2%22,%22%E2%80%A2%22,1,1,1,1,%22X%22],[1,1,%22%E2%80%A2%22,%22%E2%80%A2%22,1,1,1,%22X%22],[1,1,1,1,%22%E2%80%A2%22,1,1,1,%22X%22],[1,1,1,1,1,1,%22%E2%80%A2%22,1,1,%22X%22],[1,1,1,1,1,1,1,%22%E2%80%A2%22,%22%E2%80%A2%22,1,%22X%22],[1,1,1,1,1,%22X%22,1,1,1,%22%E2%80%A2%22,%22%E2%80%A2%22],[1,1,1,1,1,1,1,%22%E2%80%A2%22,%22%E2%80%A2%22,1,%22X%22],[1,1,1,1,1,1,%22%E2%80%A2%22,1,1,%22X%22],[1,1,1,1,%22%E2%80%A2%22,1,1,1,%22X%22],[1,1,%22%E2%80%A2%22,%22%E2%80%A2%22,1,1,1,%22X%22],[%22%E2%80%A2%22,%22%E2%80%A2%22,1,1,1,1,%22X%22]]}). In the decomposition we still decompose Toffoli-gates. This produces the following code."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "b4dd4305-885c-486a-ab3c-a920fedfafba",
   "metadata": {},
   "outputs": [],
   "source": [
    "def toffoli_decomposed(qc, ctrl_qubits, target):\n",
    "    first = ctrl_qubits[0]\n",
    "    second = ctrl_qubits[1]\n",
    "    \n",
    "    # I took this decomposition from my solution to multi-control Toffoli challenge\n",
    "    qc.h(target)\n",
    "    qc.cx(second, target)\n",
    "    qc.append(TGate().inverse(), [target])\n",
    "    qc.cx(first, target)\n",
    "    qc.append(TGate(), [target])\n",
    "    qc.cx(first, second)\n",
    "    qc.cx(second, target)\n",
    "    qc.cx(first, target)\n",
    "    qc.append(TGate().inverse(), [second])\n",
    "    qc.append(TGate().inverse(), [target])\n",
    "    qc.cx(first, target)\n",
    "    qc.cx(first, second)\n",
    "    qc.append(TGate(), [target])\n",
    "    qc.append(TGate(), [second])\n",
    "    qc.append(TGate(), [first])\n",
    "    qc.h(target)\n",
    "    \n",
    "    return qc\n",
    "\n",
    "# This function decomposes Toffoli gate with 5-control qubits\n",
    "def mcx_5_decomposed(qc, ctrl_qubits, target, ancilla):\n",
    "    c0, c1, c2 = ctrl_qubits[0], ctrl_qubits[1], ctrl_qubits[2]\n",
    "    c3, c4 = ctrl_qubits[3], ctrl_qubits[4]\n",
    "    \n",
    "    qc = toffoli_decomposed(qc, [c0, c1], ancilla[0])\n",
    "    #qc.mcx([c0, c1], ancilla[0])\n",
    "    qc = toffoli_decomposed(qc, [c2, c3], ancilla[1])\n",
    "    #qc.mcx([c2, c3], ancilla[1])\n",
    "    qc.cnot(c4, ancilla[2])\n",
    "    \n",
    "    qc.cnot(ancilla[0], ancilla[3])\n",
    "    qc = toffoli_decomposed(qc, [ancilla[1], ancilla[2]], ancilla[4])\n",
    "    #qc.mcx([ancilla[1], ancilla[2]], ancilla[4])\n",
    "    \n",
    "    qc = toffoli_decomposed(qc, [ancilla[3], ancilla[4]], target[0])\n",
    "    #qc.mcx([ancilla[3], ancilla[4]], target[0])\n",
    "    \n",
    "    qc = toffoli_decomposed(qc, [ancilla[1], ancilla[2]], ancilla[4])\n",
    "    #qc.mcx([ancilla[1], ancilla[2]], ancilla[4])\n",
    "    qc.cnot(ancilla[0], ancilla[3])\n",
    "    qc.cnot(c4, ancilla[2])\n",
    "    qc = toffoli_decomposed(qc, [c2, c3], ancilla[1])\n",
    "    #qc.mcx([c2, c3], ancilla[1])\n",
    "    qc = toffoli_decomposed(qc, [c0, c1], ancilla[0])\n",
    "    #qc.mcx([c0, c1], ancilla[0])\n",
    "     \n",
    "    return qc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "12d99a98-6c83-48f7-b909-76827aa9fa86",
   "metadata": {},
   "outputs": [],
   "source": [
    "def compose_grover_oracle2(qc, query_qubits, target_qubit, ancilla_qubits, ctrl_char):\n",
    "    not_indices = []\n",
    "    qc.barrier()\n",
    "    for i, b in enumerate(ctrl_char):\n",
    "        if b == '0':\n",
    "            qc.x(query_qubits[len(query_qubits) - i - 1])\n",
    "            not_indices.append(len(query_qubits) - i - 1)\n",
    "    \n",
    "    mcx_5_decomposed(qc, query_qubits, target_qubit, ancilla_qubits)\n",
    "    \n",
    "    for i in not_indices:\n",
    "        qc.x(query_qubits[i])\n",
    "        \n",
    "    return qc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "3fb2bc65-0808-4992-84d1-a89909fc53c6",
   "metadata": {},
   "outputs": [],
   "source": [
    "if query_qubits == 5:    \n",
    "    final_circuits = []\n",
    "\n",
    "    for oracle in oracles:\n",
    "        \n",
    "        ancialla_reg = QuantumRegister(5, name = 'ancilla') \n",
    "        query_reg = QuantumRegister(query_qubits, name = 'query')\n",
    "        target_reg = QuantumRegister(1, name = 'target')\n",
    "        classical_reg = ClassicalRegister(query_qubits)\n",
    "        \n",
    "        grover_circuit = QuantumCircuit(query_reg, target_reg, ancialla_reg, classical_reg)\n",
    "        \n",
    "        grover_circuit.h(query_reg)\n",
    "        grover_circuit.x(target_reg)\n",
    "        grover_circuit.h(target_reg)\n",
    "\n",
    "        for ctrl_char in oracle['circuit']:\n",
    "            grover_circuit = compose_grover_oracle2(grover_circuit, query_reg, target_reg, ancialla_reg, ctrl_char)\n",
    "\n",
    "        grover_circuit.barrier()\n",
    "        grover_circuit.h(query_reg)\n",
    "        grover_circuit.h(target_reg)\n",
    "        grover_circuit.x(query_reg)\n",
    "        grover_circuit.mcx(query_reg, target_reg[0])\n",
    "        grover_circuit.x(query_reg)\n",
    "        grover_circuit.h(query_reg)\n",
    "        grover_circuit.h(target_reg)\n",
    "        \n",
    "        final_circuits.append(grover_circuit)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f2a33b86-d273-4c02-8acb-c0ba4a62de4c",
   "metadata": {},
   "source": [
    "Uncomment the line if you want to visualize the circuit. It is quite long now."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "46638331-fdbc-4f35-9c27-d2ad7bb0910e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# final_circuits[0].draw(output = 'mpl')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "941080c8-5288-4fb7-b7d0-dcc27740bfbf",
   "metadata": {},
   "source": [
    "Then we run the code on the simulator and ensure that the decomposition is correct."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "b11eb933-7b64-4353-8e25-21594acbc8b7",
   "metadata": {},
   "outputs": [],
   "source": [
    "solution_counts = []\n",
    "for grover_circuit in final_circuits:\n",
    "    grover_circuit.measure(range(query_qubits), range(query_qubits))\n",
    "    aer_sim = Aer.get_backend('aer_simulator')\n",
    "    qobj = assemble(grover_circuit)\n",
    "    result = aer_sim.run(qobj, shots=100000).result()\n",
    "    counts = result.get_counts()\n",
    "    solution_counts.append(counts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "a0e27101-6095-4031-9b3a-5092cb05575a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x864 with 1 Axes>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "legend = [str(i + 1) + '. execution' for i in range(len(solution_counts))]\n",
    "plot_histogram(solution_counts, legend=legend, sort='asc', figsize=(15,12))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "7b366a31-4091-4b43-b8b0-5e6c36c299ac",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Possible solution to the Kakuro puzzle: \n",
      "x_5 = 0\n",
      "x_6 = 1\n",
      "x_4 = 0\n",
      "x_3 = 2\n",
      "x_0 = 2\n",
      "x_2 = 3\n",
      "x_1 = 1\n",
      "\n"
     ]
    }
   ],
   "source": [
    "for counts in solution_counts:\n",
    "    sorted_counts = dict(sorted(counts.items(), key = lambda item: item[1], reverse=True))\n",
    "    counts_iterator = iter(sorted_counts)\n",
    "    print(\"Possible solution to the Kakuro puzzle: \")\n",
    "    for i in range(n_variables):\n",
    "        b = next(counts_iterator)\n",
    "        (i, j) = state_to_variable(b)\n",
    "        #print(i,j)\n",
    "        print(\"x_\" + str(i) + \" = \" + str(j))\n",
    "    print()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "74638e75-aa2a-4777-aa2c-f5e19f20980a",
   "metadata": {},
   "source": [
    "Statistics about the final decomposed circuit. We can now easily count that there are 371 CNOT gates in the circuit i.e. in the oracle."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "8fe4bb95-9553-42ca-b0e9-b811eba21d57",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Gate counts:  OrderedDict([('cx', 371), ('t', 196), ('tdg', 147), ('h', 116), ('x', 51), ('barrier', 8), ('measure', 5), ('mcx_gray', 1)])\n",
      "Depth:  371\n"
     ]
    }
   ],
   "source": [
    "final_circuits[0].qasm(filename = 'kakuro_qasm')\n",
    "print('Gate counts: ', final_circuits[0].count_ops())\n",
    "print('Depth: ', final_circuits[0].depth())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1500f10a-2611-43ed-8a8d-568057d8410b",
   "metadata": {
    "tags": []
   },
   "source": [
    "## Solution proposal 2: Apply idea from Qiskit documentation"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4133530f-83b7-400b-8cc9-e3086d6c0832",
   "metadata": {},
   "source": [
    "In this case, we encode the problem using the same binary variables. Following the idea in the [Qiskit documentation](https://qiskit.org/textbook/ch-algorithms/grover.html#sudoku), we can note that the fact $x_0 \\neq x_1$ in decimals is equivalent to the fact $x_{0,j} \\neq x_{1,j}$ for all $j = 0,1,2,3$ in binary variables. The documentation gives us the following simple circuit that encodes this inequality for each $j = 0, 1, 2, 3$. The idea is that the ancilla qubit is true if one of the variables is true. If both are true, then the ancilla is false."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "29b623e4-642b-4ab6-ace4-4451107f5905",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<PIL.Image.Image image mode=RGB size=202x86 at 0x24D5C62A0B0>"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "qr = QuantumRegister(2, 'x')\n",
    "anc = QuantumRegister(1, 'ancilla')\n",
    "circuit = QuantumCircuit(qr, anc)\n",
    "\n",
    "circuit.cx(0, 2)\n",
    "circuit.cx(1, 2)\n",
    "\n",
    "circuit.draw(output='latex')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "26637c9f-1310-4464-9a09-98ca261fc5ed",
   "metadata": {},
   "source": [
    "For simplicity, let us concentrate on Classiq's specific Kakuro puzzle and not work as general level as in the first case. In the below case, I used all ten constraints without reducing the number manually. In the ancilla qubits, the first index (0-9) refers to the inequality constraint, and the second index refers to the value that the constraint is restricting. \n",
    "\n",
    "Then we encode the sums. The idea behind sums is the following: for example, variables $x_2$, $x_3$, $x_4$ can be summed to $5$ with $x_2 = 2$, $x_3 = 3$, $x_4 = 0$. This means that we have the valid case where the binary variables $x_{2,2} = 1$, $x_{3,3} = 1$ and $x_{4,0} = 1$. Each of these binary variables corresponds to a qubit in the above circuit. Thus we can encode this constraint by adding a 3-control qubit Toffoli gate controlled by the qubits corresponding to the variables $x_{2,2}$, $x_{3,3}$ and $x_{4,0}$. That process creates the following circuit. When we mirror the gates to the other side of the last multi-control CNOT gate, we obtain the circuit that works as Grover's oracle. Unfortunately, it is again too large that we could execute it. This approach feels a bit of waste of qubits but I cannot come up how to encode similar idea to phases and use states as in the first solution proposal."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "0e39fd02-545a-42f8-b368-37992255471b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 4492.8x1987.2 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pennylane as qml\n",
    "from pennylane import numpy as np\n",
    "\n",
    "sums = []\n",
    "for c in sum_equality_constraints:\n",
    "    sums += sum_options(c)\n",
    "\n",
    "variable_qubits = ['x' + str(i) + str(j) + '  ' for i in range(n_variables) for j in range(4)]\n",
    "inequality_ancillas = ['i_anc' + str(i) + str(j) + '     ' for i in range(len(inequality_constraints)) for j in range(4)]\n",
    "sum_ancillas = ['s_anc' + str(i) + '     ' for i in range(len(sums))]\n",
    "n_ancilla = len(inequality_ancillas) + len(sum_ancillas)\n",
    "workwires_for_pennylane = ['qml_wire' + str(i) + '     ' for i in range(10)]\n",
    "targets = variable_qubits + inequality_ancillas + sum_ancillas+ ['main_anc'] + workwires_for_pennylane\n",
    "\n",
    "# The circuit is so large that Pennylane's built in devices did not seem to work\n",
    "# because they are creating some large matrices. Maybe this can be avoided.\n",
    "# Anyway, Cirq and Braket seemed to work fine\n",
    "\n",
    "dev = qml.device('cirq.simulator', wires = targets)\n",
    "#dev = qml.device('braket.local.qubit', wires = targets)\n",
    "\n",
    "def variables_to_wires(i, j):\n",
    "    return 4*i + j\n",
    "\n",
    "\n",
    "def inequality_constraint_circuit(inequality_constraints):\n",
    "    for i, c in enumerate(inequality_constraints):\n",
    "        x, y = c[0], c[1]\n",
    "        for j in range(4):\n",
    "            x_wire = variables_to_wires(x, j)\n",
    "            y_wire = variables_to_wires(y, j)\n",
    "            target_ancilla = inequality_ancillas[4*i + j]\n",
    "            qml.CNOT(wires = [variable_qubits[x_wire], target_ancilla])\n",
    "            qml.CNOT(wires = [variable_qubits[y_wire], target_ancilla])\n",
    "    \n",
    "    \n",
    "def inequality_constraint_circuit_reverse(inequality_constraints):\n",
    "    for i, c in reversed(list(enumerate(inequality_constraints))):\n",
    "        x, y = c[0], c[1]\n",
    "        for j in reversed(range(4)):\n",
    "            x_wire = variables_to_wires(x, j)\n",
    "            y_wire = variables_to_wires(y, j)\n",
    "            target_ancilla = inequality_ancillas[4*i + j]\n",
    "            qml.CNOT(wires = [variable_qubits[x_wire], target_ancilla])\n",
    "            qml.CNOT(wires = [variable_qubits[y_wire], target_ancilla])\n",
    "\n",
    "            \n",
    "def sum_constraint_circuit(sum_equality_constraints):\n",
    "    ancilla_index = 0\n",
    "    for c in sum_equality_constraints:\n",
    "        sums = sum_options(c)\n",
    "        variables = c['variables']\n",
    "        for sum_option in sums:\n",
    "            controls = [variable_qubits[variables_to_wires(variables[k], sum_option[k])] for k in range(len(variables))]\n",
    "            target_ancilla = sum_ancillas[ancilla_index]\n",
    "            ancilla_index += 1\n",
    "            qml.MultiControlledX(wires = controls + [target_ancilla], work_wires = workwires_for_pennylane[:2])\n",
    "            \n",
    "            \n",
    "def sum_constraint_circuit_reverse(sum_equality_constraints):\n",
    "    ancilla_index = len(sum_ancillas) - 1\n",
    "    for c in reversed(sum_equality_constraints):\n",
    "        sums = sum_options(c)\n",
    "        variables = c['variables']\n",
    "        for sum_option in reversed(sums):\n",
    "            controls = [variable_qubits[variables_to_wires(variables[k], sum_option[k])] for k in range(len(variables))]\n",
    "            target_ancilla = sum_ancillas[ancilla_index]\n",
    "            ancilla_index -= 1\n",
    "            qml.MultiControlledX(wires = controls + [target_ancilla], work_wires = workwires_for_pennylane[2:4])\n",
    "\n",
    "            \n",
    "@qml.qnode(dev)\n",
    "def full_grover_oracle(inequality_constraints, sum_equality_constraints):\n",
    "    inequality_constraint_circuit(inequality_constraints)\n",
    "    sum_constraint_circuit(sum_equality_constraints)\n",
    "    qml.MultiControlledX(wires = inequality_ancillas + sum_ancillas + ['main_anc'])\n",
    "    sum_constraint_circuit_reverse(sum_equality_constraints)\n",
    "    inequality_constraint_circuit_reverse(inequality_constraints)\n",
    "    return qml.state()\n",
    "\n",
    "\n",
    "\n",
    "qml.drawer.use_style('black_white')\n",
    "fig, ax = qml.draw_mpl(full_grover_oracle, show_all_wires = False)(inequality_constraints, sum_equality_constraints)\n",
    "fig.set_size_inches(fig.get_size_inches()[0]*0.3, fig.get_size_inches()[1]*0.3)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7dcef1f9-5941-4d92-b953-d57b973a4d80",
   "metadata": {},
   "source": [
    "Next we can insert the previous Grover oracle in the Grover's algorithm. One solution to the simulation problem might be to note that the oracle does not implement anything which necessarily requires quantum: it is just a sequence of if-then clauses which filps bits. In this sense, we could take some states (possibly those which we consider relevant: the correct solution and a bunch of false solutions) from the initial distribution and then run the oracle with them. If these states indicate that the predicted correct solution has higher probability to be true after a measurement, we could say that the circuit is correct with high probability."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "39a86f23-33df-414b-b96f-ccb8ff17400f",
   "metadata": {},
   "outputs": [],
   "source": [
    "def apply_grover_oracle():\n",
    "    inequality_constraint_circuit(inequality_constraints)\n",
    "    sum_constraint_circuit(sum_equality_constraints)\n",
    "    qml.MultiControlledX(wires = inequality_ancillas + sum_ancillas + ['main_anc'], work_wires = workwires_for_pennylane[4:8])\n",
    "    sum_constraint_circuit_reverse(sum_equality_constraints)\n",
    "    inequality_constraint_circuit_reverse(inequality_constraints)\n",
    "    \n",
    "    \n",
    "def apply_diffusion():\n",
    "    qml.broadcast(qml.Hadamard, variable_qubits + ['main_anc'], pattern = 'single')\n",
    "    qml.MultiControlledX(control_wires = variable_qubits, wires = ['main_anc'], control_values = '0'*(len(variable_qubits)), work_wires = workwires_for_pennylane[8:10])\n",
    "    qml.broadcast(qml.Hadamard, variable_qubits + ['main_anc'], pattern = 'single')\n",
    "\n",
    "    \n",
    "@qml.qnode(dev)\n",
    "def full_grover2():\n",
    "    qml.PauliX(wires = ['main_anc'])\n",
    "    qml.broadcast(qml.Hadamard, variable_qubits + ['main_anc'], pattern = 'single')\n",
    "    apply_grover_oracle()\n",
    "    apply_diffusion()\n",
    "    \n",
    "    return qml.probs(wires = variable_qubits)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8cf430c7-5933-441e-b39b-7b60c527b2a5",
   "metadata": {},
   "source": [
    "The following code draws the whole Grover search circuit:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "699743be-a749-472d-bc26-a3edd5e07563",
   "metadata": {},
   "outputs": [],
   "source": [
    "#qml.drawer.use_style('black_white')\n",
    "#fig, ax = qml.draw_mpl(full_grover2, show_all_wires = False)()\n",
    "#fig.set_size_inches(fig.get_size_inches()[0]*0.3, fig.get_size_inches()[1]*0.3)\n",
    "#print(full_grover2())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b0ca4738-ae4d-4bc7-b5fa-9dd3ac98e76e",
   "metadata": {
    "tags": []
   },
   "source": [
    "## Solution proposal 3: Apply ideas from paper _Grover Adaptive Search for Constrained Polynomial Binary Optimization_"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9806a000-5f3e-4a58-bf49-ff61becc3fef",
   "metadata": {
    "tags": []
   },
   "source": [
    "### Encode constraints as QUBO"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0b19eb19-eeaf-47d6-81aa-d612715203c0",
   "metadata": {},
   "source": [
    "Again, we encode the variables and values using the binary variables $x_{i,j}$. The idea is to define an objective function\n",
    "\n",
    "$$\n",
    "f(x) = \\sum_{i,j = 0}^{n}a_{i,j}x_ix_j + \\sum_{i = 0}^{n}b_{i}x_i + c\n",
    "$$\n",
    "\n",
    "so that we find its minimum and the binary values, which minimize the function. When the objective function is constructed to implement the constraints of the Kakuro problem, it heuristically solves the problem. The easiest method is to use quantum annealing but based on the paper [Grover Adaptive Search for Constrained Polynomial Binary Optimization](https://arxiv.org/abs/1912.04088), we should be able to solve the problem with a novel application of Grover Search. Since QUBOs are strongly connected to quantum annealing, the most convenient framework to use here is D-wave's Ocean package."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eb22095d-b81b-422b-be98-d831172e9803",
   "metadata": {},
   "source": [
    "#### Objective function for inequality constraints"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "09fff0e8-e40d-4c2c-ac8e-926ff8192e51",
   "metadata": {},
   "source": [
    "For example, the constraint $x_0 \\neq x_1$ means that for any $j = 0,1,2,3$, if $x_{0,j} = 1$ then $x_{1,j} = 0$. In this case the objective function reaches the correct minimum when we add term $x_{0,j}x_{1,j}$ for each $j = 0,1,2,3$. Thus we construct of QUBO which consists of terms as $x_{0,j}x_{1,j}$ encoding the inequality constraints."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "f9ef4a4c-bade-4879-950c-e316d4899b9c",
   "metadata": {},
   "outputs": [],
   "source": [
    "import dimod\n",
    "from dimod.generators.constraints import combinations as qubo_combinations\n",
    "from dwave.system import LeapHybridSampler\n",
    "from hybrid.reference import KerberosSampler\n",
    "from dwave.system.composites import EmbeddingComposite\n",
    "\n",
    "from braket.aws import AwsDevice\n",
    "from braket.ocean_plugin import BraketSampler, BraketDWaveSampler\n",
    "\n",
    "# Some helper functions\n",
    "def append_linear_safe(var, val, linear):\n",
    "    if var in linear.keys():\n",
    "        linear[var] = linear[var] + val\n",
    "    else:\n",
    "        linear[var] = val\n",
    "\n",
    "def append_quadratic_safe(var, val, quadratic):\n",
    "    if var in quadratic.keys():\n",
    "        quadratic[var] = quadratic[var] + val\n",
    "    else:\n",
    "        quadratic[var] = val"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "f42bedbd-70b8-4c85-b64a-351c94414bb9",
   "metadata": {},
   "outputs": [],
   "source": [
    "def construct_bqm_constraint1(bqm, inequality_constraints):\n",
    "    vartype = dimod.BINARY\n",
    "    A = 1\n",
    "    linear = dict()\n",
    "    quadratic = dict()\n",
    "    offset = 0.0\n",
    "\n",
    "    for c in inequality_constraints:\n",
    "        x, y = c[0], c[1]\n",
    "        for j in range(4):\n",
    "            var1, var2 = (x,j), (y,j)\n",
    "            append_quadratic_safe((var1, var2), 1, quadratic)\n",
    "\n",
    "    bqm_c1 = dimod.BinaryQuadraticModel(linear, quadratic, offset, vartype)\n",
    "    bqm_c1.scale(A)\n",
    "    bqm.update(bqm_c1)\n",
    "    return bqm"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3c13caa7-5dbe-490d-9e7c-f2835dd9a31b",
   "metadata": {},
   "source": [
    "#### Objective function for sum constraints"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ab0c2da9-5e52-491e-9898-3d162d1a005c",
   "metadata": {},
   "source": [
    "The following formulation follows from the facts on how we can divide the sum constraints based on the `sum_options` function. Designing this objective function requires a bit of manual work because the squared terms need to be opened before we can input them. That is why some constants and coefficients (-11 and 36) appear to come out of the blue."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "00e0a5a4-10f3-46e5-8276-5f4f4ea7e477",
   "metadata": {},
   "outputs": [],
   "source": [
    "from itertools import combinations\n",
    "\n",
    "def construct_bqm_constraint2(bqm, sum_equality_constraints):\n",
    "    vartype = dimod.BINARY\n",
    "    A = 10\n",
    "    linear = dict()\n",
    "    quadratic = dict()\n",
    "    offset = 4\n",
    "\n",
    "    for c in sum_equality_constraints:\n",
    "        bin_variables = []\n",
    "        variables = c['variables']\n",
    "        options = sum_options(c)\n",
    "        \n",
    "        for i, var in enumerate(variables):\n",
    "            for o in options:\n",
    "                bin_variables.append((var, o[i]))\n",
    "        \n",
    "        offset = 4\n",
    "        linear_coefficient = -3   \n",
    "\n",
    "        if len(variables) == 3:\n",
    "            offset = 36\n",
    "            linear_coefficient = -11\n",
    "\n",
    "        for b in bin_variables:\n",
    "            append_linear_safe(b, linear_coefficient, linear)\n",
    "\n",
    "        for comb in combinations(bin_variables, 2):\n",
    "            append_quadratic_safe(comb, 2, quadratic)\n",
    "\n",
    "    bqm_c2 = dimod.BinaryQuadraticModel(linear, quadratic, offset, vartype)\n",
    "    bqm_c2.scale(A)\n",
    "    bqm.update(bqm_c2)\n",
    "    return bqm"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "128f615c-47ac-4ca8-80ad-4665bab07fb2",
   "metadata": {},
   "source": [
    "#### Additional constraints"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fe644395-4e7b-4f60-9c3d-bf6fcd4fb8dd",
   "metadata": {},
   "source": [
    "For every $i$ there is exactly one $j$ so that $x_{i,j} = 1$."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "c0658a7a-5ce7-4040-b02c-2073cc889885",
   "metadata": {},
   "outputs": [],
   "source": [
    "def construct_bqm_constraint3(bqm, n_variables):\n",
    "    strength = 40.0\n",
    "    for x in range(n_variables):\n",
    "        variables = [(x, j) for j in range(4)]\n",
    "        bqm3 = qubo_combinations(variables, 1, strength=strength)\n",
    "        bqm.update(bqm3)\n",
    "    return bqm"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fa1acb39-c483-4fe9-819b-35aec97f1129",
   "metadata": {
    "tags": []
   },
   "source": [
    "### Demonstration on minimizing objective function with Amazon Braket, D-wave Leap and local machine"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "16ca17cd-fd7a-4323-a9b1-d64757e93e93",
   "metadata": {},
   "outputs": [],
   "source": [
    "vartype = dimod.BINARY\n",
    "bqm = dimod.BinaryQuadraticModel({}, {}, 0.0, vartype)\n",
    "\n",
    "bqm = construct_bqm_constraint1(bqm, inequality_constraints)\n",
    "bqm = construct_bqm_constraint2(bqm, sum_equality_constraints)\n",
    "bqm = construct_bqm_constraint3(bqm, n_variables)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "86dcf57b-1650-4381-8e58-74cd385528d5",
   "metadata": {},
   "outputs": [],
   "source": [
    "def solve_bqm_in_Amazon_Braket(bqm, system = \"Advantage\"):\n",
    "    device = None\n",
    "    num_reads = 10000\n",
    "    if system == \"Advantage\":\n",
    "        device = \"arn:aws:braket:::device/qpu/d-wave/Advantage_system4\"\n",
    "    elif system == \"2000Q\":\n",
    "        device = \"arn:aws:braket:::device/qpu/d-wave/DW_2000Q_6\"\n",
    "    sampler = BraketDWaveSampler(device_arn = device)\n",
    "    sampler = EmbeddingComposite(sampler)\n",
    "    sampleset = sampler.sample(bqm, num_reads=num_reads)\n",
    "    sample = sampleset.first.sample\n",
    "    \n",
    "    # print timing info for the previous D-Wave job\n",
    "    print(sampleset.info['additionalMetadata']['dwaveMetadata']['timing'])\n",
    "    \n",
    "    return sample"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "49569f10-729d-4b74-bf86-360a93c1f3ca",
   "metadata": {},
   "outputs": [],
   "source": [
    "def solve_exactly(bqm):\n",
    "    sampler = dimod.ExactSolver()\n",
    "    sampleset = sampler.sample(bqm)\n",
    "    sample = sampleset.first.sample\n",
    "    return sample"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9651bc4e-4e64-482d-8c2c-a9bdb398d6e7",
   "metadata": {},
   "source": [
    "Now we hopefully get the correct result. I run the code on Amazon Braket:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "89b40284-a45c-4f76-8d32-505610d201c0",
   "metadata": {},
   "outputs": [],
   "source": [
    "#result = solve_bqm_in_Amazon_Braket(bqm)\n",
    "\n",
    "#for elem in result:\n",
    "#    if result[elem] == 1:\n",
    "#        print('x_' + str(elem[0]) + ' = ' + str(elem[1]))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "78055dd4-aa0a-43dc-80a9-b47502f42ee3",
   "metadata": {},
   "source": [
    "I got the following result from Amazon Braket:\n",
    "\n",
    "```\n",
    "{'qpuSamplingTime': 1429400, 'qpuAnnealTimePerSample': 20, 'qpuAccessTime': 1444466, 'qpuAccessOverheadTime': 10467, 'qpuReadoutTimePerSample': 102, 'qpuProgrammingTime': 15066, 'qpuDelayTimePerSample': 20, 'postProcessingOverheadTime': 3454, 'totalPostProcessingTime': 29876, 'totalRealTime': None, 'runTimeChip': None, 'annealTimePerRun': None, 'readoutTimePerRun': None}\n",
    "\n",
    "x_0 = 2\n",
    "x_1 = 0\n",
    "x_2 = 3\n",
    "x_3 = 2\n",
    "x_4 = 0\n",
    "x_5 = 1\n",
    "x_6 = 0\n",
    "\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "a13cdbab-248f-46c1-acce-d32cf2f4ece4",
   "metadata": {},
   "outputs": [],
   "source": [
    "def solve_bqm_in_leap(bqm, sampler = \"Kerberos\"):\n",
    "    bqm.normalize()\n",
    "    if sampler == \"Kerberos\":\n",
    "        kerberos_sampler = KerberosSampler().sample(bqm, max_iter=10, convergence=3, qpu_params={'label': 'Kakuro puzzle'})\n",
    "        sample = kerberos_sampler.first.sample\n",
    "    elif sampler == \"LeapHybrid\":\n",
    "        sampler = LeapHybridSampler()\n",
    "        sampleset = sampler.sample(bqm)\n",
    "        sample = sampleset.first.sample\n",
    "    return sample"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c2e58475-4279-41c8-b176-8ca8de0c998a",
   "metadata": {},
   "source": [
    "With D-wave's Leap we obtain the correct result as well:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "e21168ab-8a44-4caa-b270-1503081bda26",
   "metadata": {},
   "outputs": [],
   "source": [
    "#result = solve_bqm_in_leap(bqm, sampler = \"LeapHybrid\")\n",
    "\n",
    "#for elem in result:\n",
    "#    if result[elem] == 1:\n",
    "#        print('x_' + str(elem[0]) + ' = ' + str(elem[1]))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a28069b0-0388-47b0-8927-fddc1cec8ff6",
   "metadata": {},
   "source": [
    "In the case you don't have access to Amazon Braket or D-wave Leap, you can also use the local classical simulated annealing module. Unfortunately this one is usually relatively slow before we start getting good results, for example, we had 10 000 num_reads on the quantum computer and 1000 on the local machine. But it seems that 1000 shots is not enough for the classical simulated annealing. Anyway, the quality depends a lot on the parameters and the parameters are sometimes difficult to find (or I have not learned that yet)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "306a1d4b-0221-4496-a9d1-039eeb03be9c",
   "metadata": {},
   "outputs": [],
   "source": [
    "def solve_with_simulated_annealing(bqm):\n",
    "    sampler = dimod.SimulatedAnnealingSampler()\n",
    "    sampleset = sampler.sample(bqm, num_reads=1000)\n",
    "    sample = sampleset.first.sample\n",
    "    return sample"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "aa682081-4541-4b03-9cee-53c5b8741f23",
   "metadata": {},
   "outputs": [],
   "source": [
    "#result = solve_with_simulated_annealing(bqm)\n",
    "\n",
    "#for elem in result:\n",
    "#    if result[elem] == 1:\n",
    "#        print('x_' + str(elem[0]) + ' = ' + str(elem[1]))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "07b01536-edd1-4f86-8e83-99960108efc9",
   "metadata": {},
   "source": [
    "### Solve QUBO with Grover Adaptive Search"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "14db9813-1b1f-4cf0-8190-a3e61983a523",
   "metadata": {},
   "source": [
    "The previous example showed how easy it is to solve the problem with quantum annealing. We can be sure that the objective function we found is correct. Next, we study if we can perform something similar to the annealing using Grover's Adaptive Search algorithm. A general version of the algorithm is implemented in [Qiskit Optimization package](https://github.com/qiskit/qiskit-optimization), and a concrete example can be found in the [Grover Optimizer](https://qiskit.org/documentation/optimization/tutorials/04_grover_optimizer.html) tutorial. \n",
    "\n",
    "I would like to understand the algorithm better, so I started to open and apply the code from [Source code for qiskit.circuit.library.arithmetic.quadratic_form](https://qiskit.org/documentation/_modules/qiskit/circuit/library/arithmetic/quadratic_form.html#QuadraticForm) and from [grover_optimizer.py](https://github.com/Qiskit/qiskit-optimization/blob/098c167a07e6ed23915ee14c8f3695bf1ced32c4/qiskit_optimization/algorithms/grover_optimizer.py). This code is in kakuro_GAS_draft notebook. I could not finish that part in time. It is easy to use the Qiskit Optimization package to solve the QUBO we previously obtained. Unfortunately, the circuit is large again, see the example in the Kakuro_GAS.txt file. 100 qubit simulator accepts the optimization problem, but the circuit is too deep, so it could not finish with it. I still believe that the method produces a correct result for the Kakuro puzzle because the objective function is correct, as Braket and Leap analysis showed, and Qiskit's GroverOptimizer must work.\n",
    "\n",
    "IBM Quantum produces a sort of QASM file of the circuit that it runs, and this file is `IBM_quantum_GAS_input.json` in this repository. There are 10 827 instructions. GroverOptimizer module does not allow access to the circuit which optimizes the quadratic program."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "00309cf2-cdfd-4176-b011-85965236e205",
   "metadata": {},
   "outputs": [],
   "source": [
    "from qiskit_optimization import QuadraticProgram\n",
    "from qiskit import BasicAer\n",
    "from qiskit_optimization.algorithms import GroverOptimizer\n",
    "from qiskit.providers.ibmq import least_busy\n",
    "\n",
    "TOKEN = \"add your token\"\n",
    "IBMQ.save_account(TOKEN, overwrite=True)\n",
    "IBMQ.load_account()\n",
    "provider = IBMQ.get_provider(hub='ibm-q')\n",
    "backend = provider.get_backend('simulator_mps')\n",
    "\n",
    "qiskit_bqm = QuadraticProgram()\n",
    "\n",
    "for var in bqm.variables:\n",
    "    qiskit_bqm.binary_var(str(var))\n",
    "\n",
    "constant = bqm.offset\n",
    "linear = []\n",
    "quadratic = {}\n",
    "\n",
    "for var in bqm.variables:\n",
    "    linear.append(bqm.linear[var])\n",
    "    \n",
    "for key in bqm.quadratic:\n",
    "    quadratic[(str(key[0]), str(key[1]))] = bqm.quadratic[key]\n",
    "\n",
    "qiskit_bqm.minimize(constant = constant, linear=linear, quadratic=quadratic)\n",
    "\n",
    "grover_optimizer = GroverOptimizer(15, num_iterations=2, quantum_instance=backend)\n",
    "results = grover_optimizer.solve(qiskit_bqm)\n",
    "\n",
    "print(\"x={}\".format(results.x))\n",
    "print(\"fval={}\".format(results.fval))"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.10.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}