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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "9de82aaa-b542-49fa-810c-f008a28a4133",
+   "metadata": {},
+   "source": [
+    "# Optimizing max-XORSAT using the Decoded Quantum Interferometry algorithm"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3d22cc71-740c-459f-a9ee-3a020661a6e1",
+   "metadata": {},
+   "source": [
+    "## Introduction\n",
+    "\n",
+    "The following demonstration will follow the paper \"Optimization by Decoded Quantum Interferometry\" (DQI) [[1](#DQI)], which introduces a quantum algorithm for combinatorial optimization problems.\n",
+    "\n",
+    "The algorithm is focused on finding approximate solutions to the *max-LINSAT* problem, and takes advantage of the sparse Fourier spectrum of certain optimization functions.\n",
+    "\n",
+    "### max-LINSAT problem\n",
+    "* **Input:**  A matrix $B \\in \\mathbb{F}^{m \\times n}$ and $m$ functions $f_i : \\mathbb{F} \\rightarrow \\{+1, -1\\}$ for $i = 1, \\cdots, m $, where $\\mathbb{F}$ is a finite field.\n",
+    "\n",
+    "  Define the objective function $f : \\mathbb{F}^n \\rightarrow \\mathbb{Z}$  to be $f(x) = \\sum_{i=1}^m f_i \\left( \\sum_{j=1}^n B_{ij} x_j \\right)$. \n",
+    "\n",
+    "* **Output:** a vector $x \\in \\mathbb{F}^n$ that best maximizes $f$.\n",
+    "\n",
+    "The paper shows that for the problem of *Optimal Polynomial Intersection (OPI)*, a special case of the the *max-LINSAT*, the algorithm can reach a better approximation ratio than any known polynomial time classical algoritm.\n",
+    "\n",
+    "We will demonstrate the algorithm in the setting of *max-XORSAT*, which is another special case of *max-LINSAT*, but is different from the *OPI* problem. Although in the setting of *max-XORSAT* a quantum advantage haven't been showed in the paper, it will be simpler for demonstration.\n",
+    "\n",
+    "### max-XORSAT problem\n",
+    "\n",
+    "* **Input:**  A matrix $B \\in \\mathbb{F}_2^{m \\times n}$ and a vector $v \\in \\mathbb{F}_2^m$ with $m > n$.\n",
+    "\n",
+    "  Define the objective function $f : \\mathbb{F}_2^n \\rightarrow \\mathbb{Z}$  to be $f(x) = \\sum_{i=1}^m (-1)^{v_i + b_i \\cdot x} = \\sum_{i=1}^m f_i(x)$ (with $b_i$ the columns of $B$),  which represents the number of staisfied constraints minus the number of unsatisfied constraints for the equation $Bx=v$. \n",
+    "\n",
+    "* **Output:** a vector $x \\in \\mathbb{F}_2^n$ that best maximizes $f$.\n",
+    "\n",
+    "\n",
+    "The *max-XORSAT* problem is NP-hard. As an example, the *Max-Cut* problem is a special case of *max-XORSAT* where the number of 1s in each row is exactly 2. The DQI algorithm is focused on finding approximate solutions to the problem. "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "444ee983-d082-4283-bc76-067c3d4f1ae3",
+   "metadata": {},
+   "source": [
+    "## Algorithm description\n",
+    "The strategy is to prepare the following state:\n",
+    "$$\n",
+    "|P(f)\\rangle = \\sum_{x\\in\\mathbb{F}_2^n}P(f(x))|x\\rangle\n",
+    "$$\n",
+    "\n",
+    "Where $P$ is a normalized polynomial. Choosing a good polynomial can bias the sampling of this state towards high $f$ values. The higher the degree $l$ of the polynomial, the better approximation ratio of the optimum we can get. The Hadamard spectrum of $|P(f)\\rangle$ is:\n",
+    "$$\n",
+    "\\sum_{k = 0}^{l} \\frac{w_k}{\\sqrt{\\binom{m}{k}}}\n",
+    "\\sum_{\\substack{y \\in \\mathbb{F}_2^m \\\\ |y| = k}} (-1)^{v \\cdot y} |B^T y\\rangle\n",
+    "$$\n",
+    "where $w_k$ are normalized weights that can be calculated from the coefficients of $P$. So in order to prepare $|P(f)\\rangle$ we will prepare prepare its hadamrd transform, then apply a Hadamard transform over it. It will take the following stages:\n",
+    "\n",
+    "1. Prepare $\\sum_{k=0}^l w_k|k\\rangle$\n",
+    "\n",
+    "2. Translate the binary encoded $|k\\rangle$ to a unary encoded state $|k\\rangle_{unary} = |\\underbrace{1 \\cdots 1}_{k}   \\underbrace{0 \\cdots 0}_{n - k} \\rangle$, resulting with the state $\\sum_{k=0}^l w_k|k\\rangle_{unary}$\n",
+    "\n",
+    "3. Translate each $|k\\rangle_{unary}$ to a Dicke-State [[2](#Dicke)], resulting with the state $\\sum_{k = 0}^{l} \\frac{w_k}{\\sqrt{\\binom{m}{k}}}\n",
+    "\\sum_{\\substack{y \\in \\mathbb{F}_2^m \\\\ |y| = k}} |y\\rangle_m$\n",
+    "\n",
+    "4. For each $|y\\rangle_m$ calculate $(-1)^{v \\cdot y} |y\\rangle_m |B^T y\\rangle_n$, getting $\\sum_{k = 0}^{l} \\frac{w_k}{\\sqrt{\\binom{m}{k}}}\n",
+    "\\sum_{\\substack{y \\in \\mathbb{F}_2^m \\\\ |y| = k}} (-1)^{v \\cdot y} |y\\rangle_m |B^T y\\rangle_n$\n",
+    "\n",
+    "5. Uncompute $|y\\rangle_m$ by decoding  $|B^T y\\rangle_n$.\n",
+    "\n",
+    "6. Apply Hadamard transform to get the desired $|P(f)\\rangle$\n",
+    "\n",
+    "\n",
+    "\n",
+    "Step 5 is the heart of the algorithm. The decoding of $|B^T y\\rangle_n$ is in general an ill-defined problem, but when the hamming weight of $y$ is known to be limited by some integer l (the degree of $P$) , it might be feasible and even efficient, depending on the structure of the matrix $B$. The problem is equivalent to decoding error from syndrome [[3](#SYND)], when $B^T$ is the parity-check matrix.\n",
+    "\n",
+    "Figure 1 shows a layout of the resulting quantum program. Executing the quantum program guarantees that we sample `x` with high $f$ values with high probability (see the last plot in this notebook)."
+   ]
+  },
+  {
+   "attachments": {
+    "9ee2175d-b027-4cc6-85f0-cf67e7cf0e33.png": {
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"
+    }
+   },
+   "cell_type": "markdown",
+   "id": "236a63d2-372e-442e-b31f-7542acf47633",
+   "metadata": {},
+   "source": [
+    "![image.png](attachment:9ee2175d-b027-4cc6-85f0-cf67e7cf0e33.png)\n",
+    "<figcaption align = \"middle\"> Figure 1. The Full DQI circuit for a *MaxCut* problem. The `x` solutions are sampled from the `target` variable after the last Hadamard-Transform.</figcaption>\n",
+    "</center>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "39b24e85-9429-4f25-95e8-56920d9679b4",
+   "metadata": {},
+   "source": [
+    "## Defining The algorithm building-blocks"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cd4fa7a6-54a3-4329-ac2b-a537441f2a91",
+   "metadata": {},
+   "source": [
+    "Next we define the needed building-blocks for all algorithm stages. Step 1 is omitted as we use the built-in `prepare_amplitudes` function."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2469fecb-56eb-43f7-bb13-d1b506bf8d50",
+   "metadata": {},
+   "source": [
+    "### Step 2: Encoding Conversions"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "86e0c7d6-c7c9-483a-99c7-22b71e4ae8c9",
+   "metadata": {},
+   "source": [
+    "We use 3 different encodings here:\n",
+    "- **Binary Encoding**: Represents a number using binary bits, where each qubit corresponds to a binary place value. For example, the number 3 on 4 qubits is: $|1100\\rangle$.\n",
+    "- **One-hot Encoding**: Represents a number by activating a single qubit, with its position indicating the value. For example, the number 3 on 4 qubits is: $|0001\\rangle$.\n",
+    "- **Unary Encoding**: Represents a number by setting the first $k$ qubits to 1 $k$ is the number, and the rest to 0. For example, the number 3 on 4 qubits is $|1110\\rangle$.\n",
+    "\n",
+    "Specifically we will translate a binary (unsigned `QNum`) to one-hot encoding, and show how to convert the one-hot encoding to a unary encoding.\n",
+    "\n",
+    "The conversions will be done inplace, meaning that the same binary encoded quantum variable will be extended to represent the target encoding.\n",
+    "The logic is based on [this post](https://quantumcomputing.stackexchange.com/questions/5526/garbage-free-reversible-binary-to-unary-decoder-construction)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "c3744bd1-8726-4647-aff7-477f580cd373",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "\n",
+    "from classiq import *\n",
+    "\n",
+    "\n",
+    "def get_rewire_list(qvars):\n",
+    "    rewire_list = [qvar for qvar in qvars[int(np.log2(len(qvars))) :]]\n",
+    "    [\n",
+    "        rewire_list.insert(2 ** (i + 1) - 1, qvar)\n",
+    "        for i, qvar in enumerate(qvars[: int(np.log2(len(qvars)))])\n",
+    "    ]\n",
+    "    return rewire_list\n",
+    "\n",
+    "\n",
+    "@qfunc\n",
+    "def binary_to_one_hot(binary: Input[QNum], one_hot: Output[QArray]):\n",
+    "    extension = QArray(\"extension\")\n",
+    "    allocate(2**binary.size - binary.size, extension)\n",
+    "    bind([binary, extension], one_hot)\n",
+    "\n",
+    "    inplace_binary_to_one_hot(one_hot)\n",
+    "\n",
+    "\n",
+    "@qfunc(generative=True)\n",
+    "def inplace_binary_to_one_hot(one_hot: QArray):\n",
+    "    temp_qvars = [QBit(f\"temp_{i}\") for i in range(one_hot.len)]\n",
+    "    bind(one_hot, temp_qvars)\n",
+    "    bind(get_rewire_list(temp_qvars), one_hot)\n",
+    "\n",
+    "    # logic\n",
+    "    X(one_hot[0])\n",
+    "    for i in range(int(np.log2(one_hot.len))):\n",
+    "        index = 2 ** (i + 1) - 1\n",
+    "        for j in range(2**i - 1):\n",
+    "            control(one_hot[index], lambda: SWAP(one_hot[j], one_hot[j + 2**i]))\n",
+    "        for j in range(2**i - 1):\n",
+    "            CX(one_hot[j + 2**i], one_hot[index])\n",
+    "\n",
+    "        CX(one_hot[index], one_hot[index - 2**i])\n",
+    "\n",
+    "\n",
+    "@qfunc\n",
+    "def inplace_one_hot_to_unary(qvar: QArray):\n",
+    "    # fill with 1s after the leading 1 bit\n",
+    "    repeat(qvar.len - 1, lambda i: CX(qvar[qvar.len - i - 1], qvar[qvar.len - i - 2]))\n",
+    "    # clear the 0 bit\n",
+    "    X(qvar[0])\n",
+    "\n",
+    "\n",
+    "@qfunc\n",
+    "def one_hot_to_unary(one_hot: Input[QArray], unary: Output[QArray]):\n",
+    "    inplace_one_hot_to_unary(one_hot)\n",
+    "    lsb = QBit(\"lsb\")\n",
+    "    bind(one_hot, [lsb, unary])\n",
+    "    free(lsb)\n",
+    "\n",
+    "\n",
+    "@qfunc\n",
+    "def binary_to_unary(binary: Input[QNum], unary: Output[QArray]):\n",
+    "    one_hot = QArray(\"one_hot\")\n",
+    "    binary_to_one_hot(binary, one_hot)\n",
+    "    one_hot_to_unary(one_hot, unary)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "aa77eaf3-2dbb-4a18-8e79-7e247a8f3b27",
+   "metadata": {},
+   "source": [
+    "Now test the function on the conversion of the number 8 from binary to unary:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "6baffac8-e121-478a-a2b0-458340990b6b",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[{'one_hot': [1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0]}: 2048]"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "@qfunc\n",
+    "def main(one_hot: Output[QArray]):\n",
+    "    binary = QNum(\"binary\")\n",
+    "    prepare_int(8, binary)\n",
+    "    binary_to_unary(binary, one_hot)\n",
+    "\n",
+    "\n",
+    "qmod = create_model(main)\n",
+    "qprog = synthesize(qmod)\n",
+    "res = execute(qprog).get_sample_result()\n",
+    "res.parsed_counts"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8adfc343-1ba7-48c5-aef8-e4b443b22f48",
+   "metadata": {},
+   "source": [
+    "### Step 3: Dicke State Preparation"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f85c90aa-001c-4869-989c-26f281cb2b32",
+   "metadata": {},
+   "source": [
+    "Transform a unary input quantum variable to a Dicke state, such that:\n",
+    "$$\n",
+    "U|\\underbrace{1 \\cdots 1}_{k}   \\underbrace{0 \\cdots 0}_{n - k} \\rangle = \\sum_{k = 0}^{l} \\frac{1}{\\sqrt{\\binom{n}{k}}}\n",
+    "\\sum_{\\substack{|y| = k}} |y\\rangle_n\n",
+    "$$\n",
+    "This recursive implementation is based on [[2](#Dicke)]. The recursion is working bit by bit."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "dc022ca1-fc44-42a2-8b8a-16059160191d",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "from classiq.qmod.symbolic import acos, min as qmin, sqrt\n",
+    "\n",
+    "\n",
+    "@qfunc(generative=True)\n",
+    "def _dicke_split_cycle_shift(k: CInt, qvar: QArray[QBit]):\n",
+    "    \"\"\"\n",
+    "    internal function, assumes the input is in the form |11..100..0> with up to k ones.\n",
+    "    transforms the state to: sqrt(1/n)*|11..100..0> + sqrt((n-1)/n)*|01..110..0>.\n",
+    "    \"\"\"\n",
+    "    for l in range(k):\n",
+    "        within_apply(\n",
+    "            lambda: CX(qvar[l + 1], qvar[0]),\n",
+    "            lambda: (\n",
+    "                control(\n",
+    "                    qvar[0], lambda: RY(2 * acos(sqrt((l + 1) / qvar.len)), qvar[l + 1])\n",
+    "                )\n",
+    "                if l == 0\n",
+    "                else control(\n",
+    "                    qvar[0] & qvar[l],\n",
+    "                    lambda: RY(2 * acos(sqrt((l + 1) / qvar.len)), qvar[l + 1]),\n",
+    "                )\n",
+    "            ),\n",
+    "        )\n",
+    "\n",
+    "\n",
+    "@qfunc\n",
+    "def prepare_dick_state_unary_input(max_k: CInt, qvar: QArray[QBit]):\n",
+    "    \"\"\"\n",
+    "    assumes the input is encoded in qvar in unary encoding. should work for every value\n",
+    "    smaller than max_k\n",
+    "    \"\"\"\n",
+    "    if_(\n",
+    "        qvar.len > 1,\n",
+    "        lambda: [\n",
+    "            _dicke_split_cycle_shift(max_k, qvar),\n",
+    "            prepare_dick_state_unary_input(\n",
+    "                qmin(max_k, qvar.len - 2), qvar[1 : qvar.len]\n",
+    "            ),\n",
+    "        ],\n",
+    "    )\n",
+    "\n",
+    "\n",
+    "@qfunc\n",
+    "def prepare_dicke_state(k: CInt, qvar: QArray[QBit]):\n",
+    "    apply_to_all(X, qvar[0:k])\n",
+    "    prepare_dick_state_unary_input(k, qvar)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f7c199ac-94d5-48dd-850a-cf3bd3b67cb0",
+   "metadata": {},
+   "source": [
+    "Test the function for Dicke state of 6 qubits with 4 1's:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "07998ea3-7c1f-4a01-b5ff-fb359c9a16bb",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "@qfunc\n",
+    "def main(qvar: Output[QArray]):\n",
+    "    allocate(6, qvar)\n",
+    "    prepare_dicke_state(4, qvar)\n",
+    "\n",
+    "\n",
+    "qmod = create_model(main)\n",
+    "qprog = synthesize(qmod)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "18e7cc06-ab61-47b4-994b-2b2560c708bc",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[{'qvar': [1, 1, 1, 0, 1, 0]}: 155,\n",
+       " {'qvar': [1, 0, 1, 1, 1, 0]}: 147,\n",
+       " {'qvar': [0, 1, 0, 1, 1, 1]}: 146,\n",
+       " {'qvar': [1, 0, 0, 1, 1, 1]}: 144,\n",
+       " {'qvar': [0, 1, 1, 0, 1, 1]}: 140,\n",
+       " {'qvar': [1, 1, 1, 1, 0, 0]}: 139,\n",
+       " {'qvar': [0, 1, 1, 1, 1, 0]}: 137,\n",
+       " {'qvar': [1, 1, 1, 0, 0, 1]}: 136,\n",
+       " {'qvar': [1, 1, 0, 0, 1, 1]}: 135,\n",
+       " {'qvar': [1, 1, 0, 1, 1, 0]}: 133,\n",
+       " {'qvar': [1, 1, 0, 1, 0, 1]}: 132,\n",
+       " {'qvar': [1, 0, 1, 0, 1, 1]}: 128,\n",
+       " {'qvar': [0, 1, 1, 1, 0, 1]}: 127,\n",
+       " {'qvar': [1, 0, 1, 1, 0, 1]}: 126,\n",
+       " {'qvar': [0, 0, 1, 1, 1, 1]}: 123]"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "res = execute(qprog).get_sample_result()\n",
+    "res.parsed_counts"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "30728a21-0863-497f-88e8-e3761e01fcc0",
+   "metadata": {},
+   "source": [
+    "### Step 4: Vector and matrix products"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "1b021eea-bbf6-4554-9fc5-4ff56133d4b6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from functools import reduce\n",
+    "\n",
+    "\n",
+    "@qfunc\n",
+    "def vector_product_phase(v: CArray[CInt], y: QArray):\n",
+    "    repeat(y.len, lambda i: if_(v[i] > 0, lambda: Z(y[i])))\n",
+    "\n",
+    "\n",
+    "@qfunc(generative=True)\n",
+    "def matrix_vector_product(B: CArray[CArray[CInt]], y: QArray, out: Output[QArray]):\n",
+    "    allocate(B.len, out)\n",
+    "    for i in range(B.len):\n",
+    "        out[i] ^= reduce(\n",
+    "            lambda x, y: x ^ y, [int(B[i][j]) * y[j] for j in range(y.len)]\n",
+    "        )"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8ed1df9a-26f0-4327-8f4c-6ebc6470697c",
+   "metadata": {},
+   "source": [
+    "## Assembling the full MAX-XOR-SAT algorithm"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2dc5da56-467b-4c57-ab06-af0a99adbfd8",
+   "metadata": {},
+   "source": [
+    "Here we combine all the building-blocks to the full algorithm. To save qubits, the decoding will be done inplace directly onto the \n",
+    "$|y\\rangle$ register. The only remaining part is the decoding part, that will be treated after choosing the problem to optimize, as it depends on the input structure.\n",
+    "\n",
+    "`dqi_max_xor_sat` is the main quantum function of the algorithm. It expects the following arguments:\n",
+    "- `B`: the (classical) constraints matrix of the optimization problem\n",
+    "- `v`: the (classical) constraints vector of the optimization problem\n",
+    "- `w_k`: a (classical) vector of coefficients $w_k$, corresponds to the polynomial transformation of the target function. The index of the last nonzero element will set the maximal number of errors that the decoder should decode\n",
+    "- `y`: the (quantum) array of the errors to be decoded by the decoder. If the decoder is perfect, should hold only 0's at the output\n",
+    "- `solution`: the (quantum) output array of the solution. Holds $|B^Ty\\rangle$ before the Hadamard-transform. \n",
+    "- `syndrome_decode`: a quantum callable that accept a syndrome quantum array and outputs the decoded error on its second quantum argument"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "c74d9523-9dc2-4c31-af3d-a43469e460c2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "@qfunc\n",
+    "def pad_zeros(total_size: CInt, qvar: Input[QArray], qvar_padded: Output[QArray]):\n",
+    "    \"\"\"\n",
+    "    utility function for padding a quantum variable with 0's at its end. It is used for\n",
+    "    extending a unary encoded variable to be in the size of the optimization array.\n",
+    "    \"\"\"\n",
+    "    extension = QArray(\"extension\")\n",
+    "    allocate(total_size - qvar.len, extension)\n",
+    "    bind([qvar, extension], qvar_padded)\n",
+    "\n",
+    "\n",
+    "@qfunc(generative=True)\n",
+    "def dqi_max_xor_sat(\n",
+    "    B: CArray[CArray[CInt]],\n",
+    "    v: CArray[CInt],\n",
+    "    w_k: CArray[CReal],\n",
+    "    y: Output[QArray],\n",
+    "    solution: Output[QArray],\n",
+    "    syndrom_decode: QCallable[QArray, QArray],\n",
+    "):\n",
+    "    k_num_errors = QNum(\"k_num_errors\")\n",
+    "    prepare_amplitudes(w_k, 0, k_num_errors)\n",
+    "\n",
+    "    k_unary = QArray(\"k_unary\")\n",
+    "    binary_to_unary(k_num_errors, k_unary)\n",
+    "\n",
+    "    # pad with 0's to the size of m\n",
+    "    pad_zeros(B.len, k_unary, y)\n",
+    "\n",
+    "    # Create the Dicke states\n",
+    "    max_errors = int(np.nonzero(w_k)[0][-1]) if np.any(w_k) else 0\n",
+    "    prepare_dick_state_unary_input(max_errors, y)\n",
+    "\n",
+    "    # Apply the phase\n",
+    "    vector_product_phase(v, y)\n",
+    "\n",
+    "    # Compute |B^T*y> to a new register\n",
+    "    matrix_vector_product(np.array(B).T.tolist(), y, solution)\n",
+    "\n",
+    "    # uncompute |y>\n",
+    "    # decode the syndrom inplace directly on y\n",
+    "    syndrom_decode(solution, y)\n",
+    "\n",
+    "    # transform from Hadamard space to function space\n",
+    "    hadamard_transform(solution)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0aedcd1b-0c52-44a9-b3da-c27ee84d91f4",
+   "metadata": {},
+   "source": [
+    "## Example problem: Max Cut for Regular Graphs"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "87d11341-0a03-4588-971c-4a363b821380",
+   "metadata": {},
+   "source": [
+    "Now let's be more specific. We choose to optimize a Max-Cut problem. We also choose specific parameters so that with the resulting $B$ matrix we will be able to decode up to 2 errors on the vector $|y\\rangle$.\n",
+    "\n",
+    "The tranlation between Max-Cut and max-XORSAT is quite straightforward. Every edge is a row, with the nodes as columns. The $v$ vector is all ones, so that if $(v_i, v_j) \\in E$, we get a constraint $x_i \\oplus x_j = 1$, that will be satisfied if $x_i$, $x_j$ are on different sides of the cut."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "58650637-58bb-417c-8597-81c180d63f30",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "B matrix:\n",
+      " [[1. 1. 0. 0. 0. 0.]\n",
+      " [1. 0. 0. 0. 1. 0.]\n",
+      " [0. 1. 1. 0. 0. 0.]\n",
+      " [0. 0. 1. 1. 0. 0.]\n",
+      " [0. 0. 0. 1. 0. 1.]\n",
+      " [0. 0. 0. 0. 1. 1.]]\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 400x200 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import itertools\n",
+    "import warnings\n",
+    "\n",
+    "import matplotlib.pyplot as plt\n",
+    "import networkx as nx\n",
+    "\n",
+    "warnings.filterwarnings(\"ignore\", category=FutureWarning)\n",
+    "\n",
+    "\n",
+    "NUM_NODES = 6\n",
+    "GRAPH_DEGREE = 2\n",
+    "\n",
+    "G = nx.random_regular_graph(d=GRAPH_DEGREE, n=NUM_NODES, seed=1)\n",
+    "\n",
+    "B = nx.incidence_matrix(G).T.toarray()\n",
+    "v = np.ones(B.shape[0])\n",
+    "\n",
+    "plt.figure(figsize=(4, 2))\n",
+    "nx.draw(G)\n",
+    "print(\"B matrix:\\n\", B)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "685ae8b2-148f-4022-8985-33096368216b",
+   "metadata": {},
+   "source": [
+    "### Original sampling statistics"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "be2d4d84-b9d4-4492-bd6c-c39d46f414f2",
+   "metadata": {},
+   "source": [
+    "Let's plot the statistics of $f$ for uniformly sampling $x$, as an histogram. \n",
+    "\n",
+    "We will Later show how we get a better histogram after sampling from the state of the DQI algorithm."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "3e72f830-3b3f-4376-aa9d-88ecec5b3b67",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# plot f statistics\n",
+    "all_inputs = np.array(list(itertools.product([0, 1], repeat=B.shape[1]))).T\n",
+    "f = ((-1) ** (B @ all_inputs + v[:, np.newaxis])).sum(axis=0)\n",
+    "\n",
+    "# plot a histogram of f\n",
+    "plt.hist(f, bins=20, density=True)\n",
+    "plt.xlabel(\"f\")\n",
+    "plt.ylabel(\"density\")\n",
+    "plt.title(\"f Histogram\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "604eadc0-6d83-45f1-97a7-d79149387dc2",
+   "metadata": {},
+   "source": [
+    "### Decodability of the resulting matrix"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "07a47df3-b1cb-459c-a508-1cf778f9b4fc",
+   "metadata": {},
+   "source": [
+    "The transposed matrix of the specific matrix we have chosen can be decoded with up to 2 errors, which corresponds to a polynomial transformation of $f$ of degree 2 in the amplitude, and degree 4 in the sampling probability:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "1d9c6ad3-cdcf-47dd-8a5c-30c84ed30007",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "num errors: 22\n",
+      "num syndromes: 22\n",
+      "B shape: (6, 6)\n"
+     ]
+    }
+   ],
+   "source": [
+    "# set the code length and possible number of errors\n",
+    "MAX_ERRORS = 2  # l in the paper\n",
+    "n = B.shape[0]\n",
+    "\n",
+    "# Generate all vectors in one line\n",
+    "errors = np.array(\n",
+    "    [\n",
+    "        np.array([1 if i in ones_positions else 0 for i in range(n)])\n",
+    "        for num_ones in range(MAX_ERRORS + 1)\n",
+    "        for ones_positions in itertools.combinations(range(n), num_ones)\n",
+    "    ]\n",
+    ")\n",
+    "syndromes = (B.T @ errors.T % 2).T\n",
+    "\n",
+    "print(\"num errors:\", errors.shape[0])\n",
+    "print(\"num syndromes:\", len(set(tuple(x) for x in list((syndromes)))))\n",
+    "print(\"B shape:\", B.shape)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e809f82d-b7e8-4a83-b572-339c368a2bd8",
+   "metadata": {},
+   "source": [
+    "### Step 5: Defining the decoder"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f9b54c3d-e0bf-49c7-971f-a751fdc3fd41",
+   "metadata": {},
+   "source": [
+    "For this basic demonstration, we just use a brute-force decoder, that will use a lookup-table for decoding each syndrome in superposition:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "d97a8607-8977-49da-acc5-cf26ef5ecc55",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def _to_int(binary_array):\n",
+    "    return int(\"\".join(str(int(bit)) for bit in reversed(binary_array)), 2)\n",
+    "\n",
+    "\n",
+    "@qfunc\n",
+    "def syndrome_decode_lookuptable(syndrome: QNum, error: QNum):\n",
+    "    for i in range(len(syndromes)):\n",
+    "        control(\n",
+    "            syndrome == _to_int(syndromes[i]),\n",
+    "            lambda: inplace_xor(_to_int(errors[i]), error),\n",
+    "        )"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "195a755f-ad43-4cb5-bcc4-edd95af9be50",
+   "metadata": {},
+   "source": [
+    "It is also possible to define a decoder that use a local rule of syndrome majority.\n",
+    "This decoder can correct just 1 error."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "f87899c2-b190-49b6-a2e8-640e12905cfc",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "@qfunc\n",
+    "def syndrome_decode_majority(syndrome: QArray, error: QArray):\n",
+    "    for i in range(B.shape[0]):\n",
+    "        # if 2 syndromes are 1, then the decoded bit will be 1, else 0\n",
+    "        synd_1 = np.nonzero(B[i])[0][0]\n",
+    "        synd_2 = np.nonzero(B[i])[0][1]\n",
+    "        error[i] ^= syndrome[synd_1] & syndrome[synd_2]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1d1d19fb-beb3-4325-bdbd-fc73039c3243",
+   "metadata": {},
+   "source": [
+    "### Choosing optimal $w_k$ coefficients\n",
+    "This is done according to the paper [[1](#DQI)] by finding the principal value of a tridiagonal matrix $A$ defined by the follwing code. The optimality is with regards to the expected ratio of satisfied constraints."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "5a78f76f-1660-4758-9d66-0260875e6ec8",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Optimal w_k vector: [0.         0.70710678 0.70710678]\n"
+     ]
+    }
+   ],
+   "source": [
+    "def get_optimal_w(m, n, l):\n",
+    "    # max-xor sat:\n",
+    "    p = 2\n",
+    "    r = 1\n",
+    "    d = (p - 2 * r) / np.sqrt(r * (p - r))\n",
+    "\n",
+    "    # Build A matrix\n",
+    "    diag = np.arange(l + 1) * d\n",
+    "    off_diag = [np.sqrt(i * (m - i + 1)) for i in range(l)]\n",
+    "    A = np.diag(diag) + np.diag(off_diag, 1) + np.diag(off_diag, -1)\n",
+    "\n",
+    "    # get W_k as the principal vector of A\n",
+    "    eigenvalues, eigenvectors = np.linalg.eig(A)\n",
+    "    principal_vector = eigenvectors[:, np.argmax(eigenvalues)]\n",
+    "\n",
+    "    # normalize\n",
+    "    return principal_vector / np.linalg.norm(principal_vector)\n",
+    "\n",
+    "\n",
+    "# normalize\n",
+    "W_k = get_optimal_w(m=B.shape[0], n=B.shape[1], l=MAX_ERRORS)\n",
+    "\n",
+    "print(\"Optimal w_k vector:\", W_k)\n",
+    "# complete W_k to a power of 2 for the usage in prepare_state\n",
+    "W_k = np.pad(W_k, (0, 2 ** int(np.ceil(np.log2(len(W_k)))) - len(W_k)))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cae56e85-e357-4d3d-b191-69bea0494fbe",
+   "metadata": {},
+   "source": [
+    "### Synthesis and Execution of the Full Algorithm"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "4628f4a1-7ead-4c75-b6df-68da252a2a0d",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "from classiq.execution import *\n",
+    "\n",
+    "\n",
+    "@qfunc\n",
+    "def main(y: Output[QArray], solution: Output[QArray]):\n",
+    "    dqi_max_xor_sat(\n",
+    "        B.tolist(),\n",
+    "        v.tolist(),\n",
+    "        W_k.tolist(),\n",
+    "        y,\n",
+    "        solution,\n",
+    "        syndrome_decode_lookuptable,\n",
+    "    )\n",
+    "\n",
+    "\n",
+    "qmod = create_model(\n",
+    "    main,\n",
+    "    constraints=Constraints(optimization_parameter=\"width\"),\n",
+    "    execution_preferences=ExecutionPreferences(num_shots=10000),\n",
+    ")\n",
+    "\n",
+    "write_qmod(qmod, \"dqi_max_xorsat\", decimal_precision=20)\n",
+    "qprog = synthesize(qmod)\n",
+    "show(qprog, display_url=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "90c1c871-9ee7-4ba7-b388-ae76692505f2",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[{'y': [0, 0, 0, 0, 0, 0], 'solution': [0, 1, 0, 1, 1, 0]}: 3093,\n",
+       " {'y': [0, 0, 0, 0, 0, 0], 'solution': [1, 0, 1, 0, 0, 1]}: 3087,\n",
+       " {'y': [0, 0, 0, 0, 0, 0], 'solution': [0, 0, 0, 0, 0, 0]}: 180,\n",
+       " {'y': [0, 0, 0, 0, 0, 0], 'solution': [1, 1, 1, 1, 1, 1]}: 179,\n",
+       " {'y': [0, 0, 0, 0, 0, 0], 'solution': [1, 0, 0, 0, 1, 1]}: 100,\n",
+       " {'y': [0, 0, 0, 0, 0, 0], 'solution': [1, 0, 0, 0, 0, 0]}: 100,\n",
+       " {'y': [0, 0, 0, 0, 0, 0], 'solution': [1, 1, 0, 0, 0, 0]}: 99,\n",
+       " {'y': [0, 0, 0, 0, 0, 0], 'solution': [1, 1, 1, 0, 1, 0]}: 99,\n",
+       " {'y': [0, 0, 0, 0, 0, 0], 'solution': [1, 1, 1, 0, 1, 1]}: 98,\n",
+       " {'y': [0, 0, 0, 0, 0, 0], 'solution': [1, 1, 0, 0, 1, 0]}: 98,\n",
+       " {'y': [0, 0, 0, 0, 0, 0], 'solution': [0, 0, 1, 0, 0, 0]}: 98,\n",
+       " {'y': [0, 0, 0, 0, 0, 0], 'solution': [0, 0, 1, 1, 0, 0]}: 96,\n",
+       " {'y': [0, 0, 0, 0, 0, 0], 'solution': [0, 1, 0, 0, 0, 0]}: 95,\n",
+       " {'y': [0, 0, 0, 0, 0, 0], 'solution': [1, 1, 1, 1, 0, 1]}: 94,\n",
+       " {'y': [0, 0, 0, 0, 0, 0], 'solution': [0, 0, 1, 1, 1, 1]}: 94,\n",
+       " {'y': [0, 0, 0, 0, 0, 0], 'solution': [0, 0, 0, 1, 1, 1]}: 92,\n",
+       " {'y': [0, 0, 0, 0, 0, 0], 'solution': [0, 1, 1, 1, 0, 0]}: 92,\n",
+       " {'y': [0, 0, 0, 0, 0, 0], 'solution': [1, 1, 1, 0, 0, 0]}: 91,\n",
+       " {'y': [0, 0, 0, 0, 0, 0], 'solution': [0, 0, 0, 1, 0, 1]}: 90,\n",
+       " {'y': [0, 0, 0, 0, 0, 0], 'solution': [1, 0, 0, 1, 1, 1]}: 90,\n",
+       " {'y': [0, 0, 0, 0, 0, 0], 'solution': [1, 1, 0, 1, 1, 1]}: 90,\n",
+       " {'y': [0, 0, 0, 0, 0, 0], 'solution': [1, 1, 0, 0, 1, 1]}: 90,\n",
+       " {'y': [0, 0, 0, 0, 0, 0], 'solution': [0, 1, 1, 1, 1, 1]}: 89,\n",
+       " {'y': [0, 0, 0, 0, 0, 0], 'solution': [1, 0, 1, 1, 1, 1]}: 89,\n",
+       " {'y': [0, 0, 0, 0, 0, 0], 'solution': [0, 0, 0, 0, 1, 1]}: 89,\n",
+       " {'y': [0, 0, 0, 0, 0, 0], 'solution': [0, 0, 0, 1, 0, 0]}: 88,\n",
+       " {'y': [0, 0, 0, 0, 0, 0], 'solution': [0, 1, 1, 1, 0, 1]}: 88,\n",
+       " {'y': [0, 0, 0, 0, 0, 0], 'solution': [0, 0, 0, 0, 1, 0]}: 88,\n",
+       " {'y': [0, 0, 0, 0, 0, 0], 'solution': [1, 0, 0, 0, 1, 0]}: 86,\n",
+       " {'y': [0, 0, 0, 0, 0, 0], 'solution': [0, 0, 1, 1, 0, 1]}: 86,\n",
+       " {'y': [0, 0, 0, 0, 0, 0], 'solution': [0, 1, 1, 0, 0, 0]}: 84,\n",
+       " {'y': [0, 0, 0, 0, 0, 0], 'solution': [0, 0, 0, 0, 0, 1]}: 84,\n",
+       " {'y': [0, 0, 0, 0, 0, 0], 'solution': [1, 1, 1, 1, 0, 0]}: 83,\n",
+       " {'y': [0, 0, 0, 0, 0, 0], 'solution': [1, 1, 1, 1, 1, 0]}: 81,\n",
+       " {'y': [0, 0, 0, 0, 0, 0], 'solution': [0, 1, 0, 0, 1, 0]}: 35,\n",
+       " {'y': [0, 0, 0, 0, 0, 0], 'solution': [0, 1, 1, 0, 1, 1]}: 33,\n",
+       " {'y': [0, 0, 0, 0, 0, 0], 'solution': [0, 1, 0, 0, 0, 1]}: 31,\n",
+       " {'y': [0, 0, 0, 0, 0, 0], 'solution': [0, 1, 1, 0, 0, 1]}: 31,\n",
+       " {'y': [0, 0, 0, 0, 0, 0], 'solution': [1, 1, 0, 1, 1, 0]}: 30,\n",
+       " {'y': [0, 0, 0, 0, 0, 0], 'solution': [1, 0, 0, 0, 0, 1]}: 30,\n",
+       " {'y': [0, 0, 0, 0, 0, 0], 'solution': [0, 1, 1, 0, 1, 0]}: 29,\n",
+       " {'y': [0, 0, 0, 0, 0, 0], 'solution': [0, 1, 0, 1, 0, 0]}: 29,\n",
+       " {'y': [0, 0, 0, 0, 0, 0], 'solution': [1, 0, 1, 1, 0, 1]}: 27,\n",
+       " {'y': [0, 0, 0, 0, 0, 0], 'solution': [1, 0, 1, 1, 0, 0]}: 26,\n",
+       " {'y': [0, 0, 0, 0, 0, 0], 'solution': [1, 1, 0, 1, 0, 1]}: 26,\n",
+       " {'y': [0, 0, 0, 0, 0, 0], 'solution': [0, 1, 1, 1, 1, 0]}: 26,\n",
+       " {'y': [0, 0, 0, 0, 0, 0], 'solution': [0, 0, 0, 1, 1, 0]}: 26,\n",
+       " {'y': [0, 0, 0, 0, 0, 0], 'solution': [1, 0, 0, 1, 0, 0]}: 25,\n",
+       " {'y': [0, 0, 0, 0, 0, 0], 'solution': [1, 1, 0, 0, 0, 1]}: 24,\n",
+       " {'y': [0, 0, 0, 0, 0, 0], 'solution': [0, 0, 1, 1, 1, 0]}: 24,\n",
+       " {'y': [0, 0, 0, 0, 0, 0], 'solution': [1, 0, 1, 1, 1, 0]}: 24,\n",
+       " {'y': [0, 0, 0, 0, 0, 0], 'solution': [1, 1, 0, 1, 0, 0]}: 24,\n",
+       " {'y': [0, 0, 0, 0, 0, 0], 'solution': [0, 1, 0, 1, 1, 1]}: 23,\n",
+       " {'y': [0, 0, 0, 0, 0, 0], 'solution': [0, 1, 0, 0, 1, 1]}: 20,\n",
+       " {'y': [0, 0, 0, 0, 0, 0], 'solution': [0, 0, 1, 0, 1, 1]}: 20,\n",
+       " {'y': [0, 0, 0, 0, 0, 0], 'solution': [0, 1, 0, 1, 0, 1]}: 20,\n",
+       " {'y': [0, 0, 0, 0, 0, 0], 'solution': [1, 0, 0, 1, 0, 1]}: 19,\n",
+       " {'y': [0, 0, 0, 0, 0, 0], 'solution': [0, 0, 1, 0, 1, 0]}: 19,\n",
+       " {'y': [0, 0, 0, 0, 0, 0], 'solution': [1, 1, 1, 0, 0, 1]}: 19,\n",
+       " {'y': [0, 0, 0, 0, 0, 0], 'solution': [1, 0, 1, 0, 0, 0]}: 18,\n",
+       " {'y': [0, 0, 0, 0, 0, 0], 'solution': [1, 0, 1, 0, 1, 1]}: 18,\n",
+       " {'y': [0, 0, 0, 0, 0, 0], 'solution': [0, 0, 1, 0, 0, 1]}: 17,\n",
+       " {'y': [0, 0, 0, 0, 0, 0], 'solution': [1, 0, 0, 1, 1, 0]}: 16,\n",
+       " {'y': [0, 0, 0, 0, 0, 0], 'solution': [1, 0, 1, 0, 1, 0]}: 11]"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "res = execute(qprog).get_sample_result()\n",
+    "res.parsed_counts"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "67ea4578-4675-4973-a0cb-a9769de53666",
+   "metadata": {},
+   "source": [
+    "Verify the `y` variable was uncomputed correctly by the decoder:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "f5a1a22a-404f-4c68-a1c4-a2cf47037c45",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "assert sum(sum(sample.state[\"y\"]) for sample in res.parsed_counts) == 0"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ae6c7a6d-d70a-4956-ab8d-1f13118a9038",
+   "metadata": {},
+   "source": [
+    "And we can observe that the `y` vector is indeed clean."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "446d1611-3e63-47bd-aac9-c2092e3da245",
+   "metadata": {},
+   "source": [
+    "### Post Processing"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d658802b-3e8e-4f95-865a-17bac6696e1f",
+   "metadata": {},
+   "source": [
+    "Finally, we plot the histogram of the sampled $f$ values from the algorithm, and compare it to a uniform sampling of $x$ values, and also to sampling weighted by $|f|$ and $|f|^2$ values. We can see the the DQI histogram is biased to higher $f$ values compared to the other sampling methods."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "38b7246b-9433-44d9-8ce5-42edd928874a",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<f_uniform>: 0.0\n",
+      "<f_DQI>: 3.0884\n"
+     ]
+    }
+   ],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "\n",
+    "# Example data initialization\n",
+    "f_sampled = []\n",
+    "shots = []\n",
+    "\n",
+    "# Populate f_sampled and shots based on res.parsed_counts\n",
+    "for sample in res.parsed_counts:\n",
+    "    solution = sample.state[\"solution\"]\n",
+    "    f_sampled.append(((-1) ** (B @ solution + v)).sum())\n",
+    "    shots.append(sample.shots)\n",
+    "f_sampled = np.array(f_sampled)\n",
+    "shots = np.array(shots)\n",
+    "\n",
+    "unique_f_sampled, indices = np.unique(f_sampled, return_inverse=True)\n",
+    "prob_f_sampled = np.array(\n",
+    "    [shots[indices == i].sum() for i in range(len(unique_f_sampled))]\n",
+    ")\n",
+    "prob_f_sampled = prob_f_sampled / prob_f_sampled.sum()\n",
+    "\n",
+    "f_values, f_counts = np.unique(f, return_counts=True)\n",
+    "prob_f_uniform = np.array(f_counts) * np.array(f_values)\n",
+    "prob_f_uniform = f_counts / sum(f_counts)\n",
+    "\n",
+    "prob_f_abs = np.array(f_counts) * np.array(np.abs(f_values))\n",
+    "prob_f_abs = prob_f_abs / prob_f_abs.sum()\n",
+    "\n",
+    "prob_f_squared = np.array(f_counts) * np.array(f_values**2)\n",
+    "prob_f_squared = prob_f_squared / prob_f_squared.sum()\n",
+    "\n",
+    "\n",
+    "# Plot normalized bar plots\n",
+    "bar_width = 0.2\n",
+    "plt.bar(\n",
+    "    unique_f_sampled - 1.5 * bar_width,\n",
+    "    prob_f_sampled,\n",
+    "    width=bar_width,\n",
+    "    alpha=0.7,\n",
+    "    label=\"$f_{DQI}$ sampling\",\n",
+    ")\n",
+    "plt.bar(\n",
+    "    f_values - 0.5 * bar_width,\n",
+    "    prob_f_uniform,\n",
+    "    width=bar_width,\n",
+    "    alpha=0.7,\n",
+    "    label=\"$uniform$ sampling\",\n",
+    ")\n",
+    "plt.bar(\n",
+    "    f_values + 0.5 * bar_width,\n",
+    "    prob_f_abs,\n",
+    "    width=bar_width,\n",
+    "    alpha=0.7,\n",
+    "    label=\"$|f|$ sampling\",\n",
+    ")\n",
+    "plt.bar(\n",
+    "    f_values + 1.5 * bar_width,\n",
+    "    prob_f_squared,\n",
+    "    width=bar_width,\n",
+    "    alpha=0.7,\n",
+    "    label=\"$|f|^2$ sampling\",\n",
+    ")\n",
+    "\n",
+    "plt.title(\"Normalized Bar Plot of $f$\")\n",
+    "plt.xlabel(\"$f$\")\n",
+    "plt.ylabel(\"Probability\")\n",
+    "plt.legend()\n",
+    "plt.show()\n",
+    "\n",
+    "print(\"<f_uniform>:\", np.average(f))\n",
+    "print(\"<f_DQI>:\", np.average(f_sampled, weights=shots))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5426fcd6-7862-4138-b7e8-03c259d72430",
+   "metadata": {},
+   "source": [
+    "## References\n",
+    "\n",
+    "<a id='DQI'>[1]</a>: [Jordan, Stephen P., et al. \"Optimization by Decoded Quantum Interferometry.\" arXiv preprint arXiv:2408.08292 (2024).](https://arxiv.org/abs/2408.08292)\n",
+    "\n",
+    "\n",
+    "<a id='Dicke'>[2]</a>: [Bärtschi, Andreas, and Stephan Eidenbenz. \"Deterministic Preparation of Dicke States.\" In *Fundamentals of Computation Theory*, pp. 126–139. Springer International Publishing, 2019.](http://dx.doi.org/10.1007/978-3-030-25027-0_9)\n",
+    "\n",
+    "<a id='SYND'>[3]</a>: [\"Linear Block Codes: Encoding and Syndrome Decoding\" from MIT's OpenCourseWare](https://ocw.mit.edu/courses/6-02-introduction-to-eecs-ii-digital-communication-systems-fall-2012/resources/mit6_02f12_chap06/)"
+   ]
+  }
+ ],
+ "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": "3.11.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/algorithms/dqi/dqi_max_xorsat.metadata.json b/algorithms/dqi/dqi_max_xorsat.metadata.json
new file mode 100644
index 00000000..bc514844
--- /dev/null
+++ b/algorithms/dqi/dqi_max_xorsat.metadata.json
@@ -0,0 +1,6 @@
+{
+  "friendly_name": "Decoded Quantum Interferometry (DQI) for MaxCut",
+  "description": "Solving the MaxCut Problem using the DQI Algorithm in the setting of max-XORSAT",
+  "qmod_type": ["algorithms"],
+  "level": ["advanced"]
+}
diff --git a/algorithms/dqi/dqi_max_xorsat.qmod b/algorithms/dqi/dqi_max_xorsat.qmod
new file mode 100644
index 00000000..3466f093
--- /dev/null
+++ b/algorithms/dqi/dqi_max_xorsat.qmod
@@ -0,0 +1,593 @@
+qfunc inplace_binary_to_one_hot_expanded___0(one_hot: qbit[4]) {
+  temp_0: qbit;
+  temp_1: qbit;
+  temp_2: qbit;
+  temp_3: qbit;
+  one_hot -> {temp_0, temp_1, temp_2, temp_3};
+  {temp_2, temp_0, temp_3, temp_1} -> one_hot;
+  X(one_hot[0]);
+  CX(one_hot[1], one_hot[0]);
+  one_hot___0_0: qbit;
+  one_hot___1_0: qbit;
+  one_hot___2_0: qbit;
+  one_hot___3_0: qbit;
+  within {
+    one_hot -> {one_hot___0_0, one_hot___1_0, one_hot___2_0, one_hot___3_0};
+  } apply {
+    control (one_hot___3_0) {
+      SWAP(one_hot___0_0, one_hot___2_0);
+    }
+  }
+  CX(one_hot[2], one_hot[3]);
+  CX(one_hot[3], one_hot[1]);
+}
+
+qfunc binary_to_one_hot_expanded___0(input binary: qnum<2, False, 0>, output one_hot: qbit[4]) {
+  extension: qbit[2];
+  allocate(2, extension);
+  {binary, extension} -> one_hot;
+  inplace_binary_to_one_hot_expanded___0(one_hot);
+}
+
+qfunc iteration_lambda___0_0_expanded___0(qvar___3_captured__inplace_one_hot_to_unary__5: qbit, qvar___2_captured__inplace_one_hot_to_unary__5: qbit) {
+  CX(qvar___3_captured__inplace_one_hot_to_unary__5, qvar___2_captured__inplace_one_hot_to_unary__5);
+}
+
+qfunc iteration_lambda___0_0_expanded___1(qvar___2_captured__inplace_one_hot_to_unary__5: qbit, qvar___1_captured__inplace_one_hot_to_unary__5: qbit) {
+  CX(qvar___2_captured__inplace_one_hot_to_unary__5, qvar___1_captured__inplace_one_hot_to_unary__5);
+}
+
+qfunc iteration_lambda___0_0_expanded___2(qvar___1_captured__inplace_one_hot_to_unary__5: qbit, qvar___0_captured__inplace_one_hot_to_unary__5: qbit) {
+  CX(qvar___1_captured__inplace_one_hot_to_unary__5, qvar___0_captured__inplace_one_hot_to_unary__5);
+}
+
+qfunc inplace_one_hot_to_unary_expanded___0(qvar: qbit[4]) {
+  iteration_lambda___0_0_expanded___0(qvar[3], qvar[2]);
+  iteration_lambda___0_0_expanded___1(qvar[2], qvar[1]);
+  iteration_lambda___0_0_expanded___2(qvar[1], qvar[0]);
+  X(qvar[0]);
+}
+
+qfunc one_hot_to_unary_expanded___0(input one_hot: qbit[4], output unary: qbit[3]) {
+  inplace_one_hot_to_unary_expanded___0(one_hot);
+  lsb: qbit;
+  one_hot -> {lsb, unary};
+  free(lsb);
+}
+
+qfunc binary_to_unary_expanded___0(input binary: qnum<2, False, 0>, output unary: qbit[3]) {
+  one_hot: qbit[4];
+  binary_to_one_hot_expanded___0(binary, one_hot);
+  one_hot_to_unary_expanded___0(one_hot, unary);
+}
+
+qfunc pad_zeros_expanded___0(input qvar: qbit[3], output qvar_padded: qbit[6]) {
+  extension: qbit[3];
+  allocate(3, extension);
+  {qvar, extension} -> qvar_padded;
+}
+
+qfunc _dicke_split_cycle_shift_expanded___0(qvar: qbit[6]) {
+  within {
+    CX(qvar[1], qvar[0]);
+  } apply {
+    qvar___0_0: qbit;
+    qvar___1_0: qbit;
+    qvar___2_0: qbit;
+    qvar___3_0: qbit;
+    qvar___4_0: qbit;
+    qvar___5_0: qbit;
+    within {
+      qvar -> {qvar___0_0, qvar___1_0, qvar___2_0, qvar___3_0, qvar___4_0, qvar___5_0};
+    } apply {
+      control (qvar___0_0) {
+        RY(2.300523983021863, qvar___1_0);
+      }
+    }
+  }
+  within {
+    CX(qvar[2], qvar[0]);
+  } apply {
+    qvar___0_1: qbit;
+    qvar___1_1: qbit;
+    qvar___2_1: qbit;
+    qvar___3_1: qbit;
+    qvar___4_1: qbit;
+    qvar___5_1: qbit;
+    within {
+      qvar -> {qvar___0_1, qvar___1_1, qvar___2_1, qvar___3_1, qvar___4_1, qvar___5_1};
+    } apply {
+      result__temp___0: qbit;
+      within {
+        result__temp___0 = qvar___0_1 & qvar___1_1;
+      } apply {
+        control (result__temp___0) {
+          RY(1.9106332362490186, qvar___2_1);
+        }
+      }
+    }
+  }
+}
+
+qfunc _dicke_split_cycle_shift_expanded___1(qvar: qbit[5]) {
+  within {
+    CX(qvar[1], qvar[0]);
+  } apply {
+    qvar___0_2: qbit;
+    qvar___1_2: qbit;
+    qvar___2_2: qbit;
+    qvar___3_2: qbit;
+    qvar___4_2: qbit;
+    within {
+      qvar -> {qvar___0_2, qvar___1_2, qvar___2_2, qvar___3_2, qvar___4_2};
+    } apply {
+      control (qvar___0_2) {
+        RY(2.214297435588181, qvar___1_2);
+      }
+    }
+  }
+  within {
+    CX(qvar[2], qvar[0]);
+  } apply {
+    qvar___0_3: qbit;
+    qvar___1_3: qbit;
+    qvar___2_3: qbit;
+    qvar___3_3: qbit;
+    qvar___4_3: qbit;
+    within {
+      qvar -> {qvar___0_3, qvar___1_3, qvar___2_3, qvar___3_3, qvar___4_3};
+    } apply {
+      result__temp___1: qbit;
+      within {
+        result__temp___1 = qvar___0_3 & qvar___1_3;
+      } apply {
+        control (result__temp___1) {
+          RY(1.7721542475852274, qvar___2_3);
+        }
+      }
+    }
+  }
+}
+
+qfunc _dicke_split_cycle_shift_expanded___2(qvar: qbit[4]) {
+  within {
+    CX(qvar[1], qvar[0]);
+  } apply {
+    qvar___0_4: qbit;
+    qvar___1_4: qbit;
+    qvar___2_4: qbit;
+    qvar___3_4: qbit;
+    within {
+      qvar -> {qvar___0_4, qvar___1_4, qvar___2_4, qvar___3_4};
+    } apply {
+      control (qvar___0_4) {
+        RY(2.0943951023931957, qvar___1_4);
+      }
+    }
+  }
+  within {
+    CX(qvar[2], qvar[0]);
+  } apply {
+    qvar___0_5: qbit;
+    qvar___1_5: qbit;
+    qvar___2_5: qbit;
+    qvar___3_5: qbit;
+    within {
+      qvar -> {qvar___0_5, qvar___1_5, qvar___2_5, qvar___3_5};
+    } apply {
+      result__temp___2: qbit;
+      within {
+        result__temp___2 = qvar___0_5 & qvar___1_5;
+      } apply {
+        control (result__temp___2) {
+          RY(1.5707963267948968, qvar___2_5);
+        }
+      }
+    }
+  }
+}
+
+qfunc _dicke_split_cycle_shift_expanded___3(qvar: qbit[3]) {
+  within {
+    CX(qvar[1], qvar[0]);
+  } apply {
+    qvar___0_6: qbit;
+    qvar___1_6: qbit;
+    qvar___2_6: qbit;
+    within {
+      qvar -> {qvar___0_6, qvar___1_6, qvar___2_6};
+    } apply {
+      control (qvar___0_6) {
+        RY(1.9106332362490186, qvar___1_6);
+      }
+    }
+  }
+  within {
+    CX(qvar[2], qvar[0]);
+  } apply {
+    qvar___0_7: qbit;
+    qvar___1_7: qbit;
+    qvar___2_7: qbit;
+    within {
+      qvar -> {qvar___0_7, qvar___1_7, qvar___2_7};
+    } apply {
+      result__temp___3: qbit;
+      within {
+        result__temp___3 = qvar___0_7 & qvar___1_7;
+      } apply {
+        control (result__temp___3) {
+          RY(1.2309594173407747, qvar___2_7);
+        }
+      }
+    }
+  }
+}
+
+qfunc _dicke_split_cycle_shift_expanded___4(qvar: qbit[2]) {
+  within {
+    CX(qvar[1], qvar[0]);
+  } apply {
+    qvar___0_8: qbit;
+    qvar___1_8: qbit;
+    within {
+      qvar -> {qvar___0_8, qvar___1_8};
+    } apply {
+      control (qvar___0_8) {
+        RY(1.5707963267948968, qvar___1_8);
+      }
+    }
+  }
+}
+
+qfunc prepare_dick_state_unary_input_expanded___0(qvar: qbit[1]) {
+}
+
+qfunc prepare_dick_state_unary_input_expanded___1(qvar: qbit[2]) {
+  _dicke_split_cycle_shift_expanded___4(qvar);
+  prepare_dick_state_unary_input_expanded___0(qvar[1:2]);
+}
+
+qfunc prepare_dick_state_unary_input_expanded___2(qvar: qbit[3]) {
+  _dicke_split_cycle_shift_expanded___3(qvar);
+  prepare_dick_state_unary_input_expanded___1(qvar[1:3]);
+}
+
+qfunc prepare_dick_state_unary_input_expanded___3(qvar: qbit[4]) {
+  _dicke_split_cycle_shift_expanded___2(qvar);
+  prepare_dick_state_unary_input_expanded___2(qvar[1:4]);
+}
+
+qfunc prepare_dick_state_unary_input_expanded___4(qvar: qbit[5]) {
+  _dicke_split_cycle_shift_expanded___1(qvar);
+  prepare_dick_state_unary_input_expanded___3(qvar[1:5]);
+}
+
+qfunc prepare_dick_state_unary_input_expanded___5(qvar: qbit[6]) {
+  _dicke_split_cycle_shift_expanded___0(qvar);
+  prepare_dick_state_unary_input_expanded___4(qvar[1:6]);
+}
+
+qfunc iteration_lambda___0_0_expanded___3(y___0_captured__vector_product_phase__3: qbit) {
+  Z(y___0_captured__vector_product_phase__3);
+}
+
+qfunc iteration_lambda___0_0_expanded___4(y___1_captured__vector_product_phase__3: qbit) {
+  Z(y___1_captured__vector_product_phase__3);
+}
+
+qfunc iteration_lambda___0_0_expanded___5(y___2_captured__vector_product_phase__3: qbit) {
+  Z(y___2_captured__vector_product_phase__3);
+}
+
+qfunc iteration_lambda___0_0_expanded___6(y___3_captured__vector_product_phase__3: qbit) {
+  Z(y___3_captured__vector_product_phase__3);
+}
+
+qfunc iteration_lambda___0_0_expanded___7(y___4_captured__vector_product_phase__3: qbit) {
+  Z(y___4_captured__vector_product_phase__3);
+}
+
+qfunc iteration_lambda___0_0_expanded___8(y___5_captured__vector_product_phase__3: qbit) {
+  Z(y___5_captured__vector_product_phase__3);
+}
+
+qfunc vector_product_phase_expanded___0(y: qbit[6]) {
+  iteration_lambda___0_0_expanded___3(y[0]);
+  iteration_lambda___0_0_expanded___4(y[1]);
+  iteration_lambda___0_0_expanded___5(y[2]);
+  iteration_lambda___0_0_expanded___6(y[3]);
+  iteration_lambda___0_0_expanded___7(y[4]);
+  iteration_lambda___0_0_expanded___8(y[5]);
+}
+
+qfunc matrix_vector_product_expanded___0(y: qbit[6], output out: qbit[6]) {
+  allocate(6, out);
+  y___0_0: qbit;
+  y___1_0: qbit;
+  y___2_0: qbit;
+  y___3_0: qbit;
+  y___4_0: qbit;
+  y___5_0: qbit;
+  within {
+    y -> {y___0_0, y___1_0, y___2_0, y___3_0, y___4_0, y___5_0};
+  } apply {
+    out[0] ^= ((((y___0_0 ^ y___1_0) ^ 0) ^ 0) ^ 0) ^ 0;
+  }
+  y___0_1: qbit;
+  y___1_1: qbit;
+  y___2_1: qbit;
+  y___3_1: qbit;
+  y___4_1: qbit;
+  y___5_1: qbit;
+  within {
+    y -> {y___0_1, y___1_1, y___2_1, y___3_1, y___4_1, y___5_1};
+  } apply {
+    out[1] ^= ((((y___0_1 ^ 0) ^ y___2_1) ^ 0) ^ 0) ^ 0;
+  }
+  y___0_2: qbit;
+  y___1_2: qbit;
+  y___2_2: qbit;
+  y___3_2: qbit;
+  y___4_2: qbit;
+  y___5_2: qbit;
+  within {
+    y -> {y___0_2, y___1_2, y___2_2, y___3_2, y___4_2, y___5_2};
+  } apply {
+    out[2] ^= (((0 ^ y___2_2) ^ y___3_2) ^ 0) ^ 0;
+  }
+  y___0_3: qbit;
+  y___1_3: qbit;
+  y___2_3: qbit;
+  y___3_3: qbit;
+  y___4_3: qbit;
+  y___5_3: qbit;
+  within {
+    y -> {y___0_3, y___1_3, y___2_3, y___3_3, y___4_3, y___5_3};
+  } apply {
+    out[3] ^= ((0 ^ y___3_3) ^ y___4_3) ^ 0;
+  }
+  y___0_4: qbit;
+  y___1_4: qbit;
+  y___2_4: qbit;
+  y___3_4: qbit;
+  y___4_4: qbit;
+  y___5_4: qbit;
+  within {
+    y -> {y___0_4, y___1_4, y___2_4, y___3_4, y___4_4, y___5_4};
+  } apply {
+    out[4] ^= ((((0 ^ y___1_4) ^ 0) ^ 0) ^ 0) ^ y___5_4;
+  }
+  y___0_5: qbit;
+  y___1_5: qbit;
+  y___2_5: qbit;
+  y___3_5: qbit;
+  y___4_5: qbit;
+  y___5_5: qbit;
+  within {
+    y -> {y___0_5, y___1_5, y___2_5, y___3_5, y___4_5, y___5_5};
+  } apply {
+    out[5] ^= (0 ^ y___4_5) ^ y___5_5;
+  }
+}
+
+qfunc syndrome_decode_lookuptable_expanded___0(syndrome: qnum<6, False, 0>, error: qnum<6, False, 0>) {
+  syndrome___array_cast_0: qbit[6];
+  within {
+    real_xor_constant(63, syndrome);
+    syndrome -> syndrome___array_cast_0;
+  } apply {
+    control (syndrome___array_cast_0) {
+    }
+  }
+  syndrome___array_cast_1: qbit[6];
+  within {
+    real_xor_constant(60, syndrome);
+    syndrome -> syndrome___array_cast_1;
+  } apply {
+    control (syndrome___array_cast_1) {
+      real_xor_constant(1, error);
+    }
+  }
+  syndrome___array_cast_2: qbit[6];
+  within {
+    real_xor_constant(46, syndrome);
+    syndrome -> syndrome___array_cast_2;
+  } apply {
+    control (syndrome___array_cast_2) {
+      real_xor_constant(2, error);
+    }
+  }
+  syndrome___array_cast_3: qbit[6];
+  within {
+    real_xor_constant(57, syndrome);
+    syndrome -> syndrome___array_cast_3;
+  } apply {
+    control (syndrome___array_cast_3) {
+      real_xor_constant(4, error);
+    }
+  }
+  syndrome___array_cast_4: qbit[6];
+  within {
+    real_xor_constant(51, syndrome);
+    syndrome -> syndrome___array_cast_4;
+  } apply {
+    control (syndrome___array_cast_4) {
+      real_xor_constant(8, error);
+    }
+  }
+  syndrome___array_cast_5: qbit[6];
+  within {
+    real_xor_constant(23, syndrome);
+    syndrome -> syndrome___array_cast_5;
+  } apply {
+    control (syndrome___array_cast_5) {
+      real_xor_constant(16, error);
+    }
+  }
+  syndrome___array_cast_6: qbit[6];
+  within {
+    real_xor_constant(15, syndrome);
+    syndrome -> syndrome___array_cast_6;
+  } apply {
+    control (syndrome___array_cast_6) {
+      real_xor_constant(32, error);
+    }
+  }
+  syndrome___array_cast_7: qbit[6];
+  within {
+    real_xor_constant(45, syndrome);
+    syndrome -> syndrome___array_cast_7;
+  } apply {
+    control (syndrome___array_cast_7) {
+      real_xor_constant(3, error);
+    }
+  }
+  syndrome___array_cast_8: qbit[6];
+  within {
+    real_xor_constant(58, syndrome);
+    syndrome -> syndrome___array_cast_8;
+  } apply {
+    control (syndrome___array_cast_8) {
+      real_xor_constant(5, error);
+    }
+  }
+  syndrome___array_cast_9: qbit[6];
+  within {
+    real_xor_constant(48, syndrome);
+    syndrome -> syndrome___array_cast_9;
+  } apply {
+    control (syndrome___array_cast_9) {
+      real_xor_constant(9, error);
+    }
+  }
+  syndrome___array_cast_10: qbit[6];
+  within {
+    real_xor_constant(20, syndrome);
+    syndrome -> syndrome___array_cast_10;
+  } apply {
+    control (syndrome___array_cast_10) {
+      real_xor_constant(17, error);
+    }
+  }
+  syndrome___array_cast_11: qbit[6];
+  within {
+    real_xor_constant(12, syndrome);
+    syndrome -> syndrome___array_cast_11;
+  } apply {
+    control (syndrome___array_cast_11) {
+      real_xor_constant(33, error);
+    }
+  }
+  syndrome___array_cast_12: qbit[6];
+  within {
+    real_xor_constant(40, syndrome);
+    syndrome -> syndrome___array_cast_12;
+  } apply {
+    control (syndrome___array_cast_12) {
+      real_xor_constant(6, error);
+    }
+  }
+  syndrome___array_cast_13: qbit[6];
+  within {
+    real_xor_constant(34, syndrome);
+    syndrome -> syndrome___array_cast_13;
+  } apply {
+    control (syndrome___array_cast_13) {
+      real_xor_constant(10, error);
+    }
+  }
+  syndrome___array_cast_14: qbit[6];
+  within {
+    real_xor_constant(6, syndrome);
+    syndrome -> syndrome___array_cast_14;
+  } apply {
+    control (syndrome___array_cast_14) {
+      real_xor_constant(18, error);
+    }
+  }
+  syndrome___array_cast_15: qbit[6];
+  within {
+    real_xor_constant(30, syndrome);
+    syndrome -> syndrome___array_cast_15;
+  } apply {
+    control (syndrome___array_cast_15) {
+      real_xor_constant(34, error);
+    }
+  }
+  syndrome___array_cast_16: qbit[6];
+  within {
+    real_xor_constant(53, syndrome);
+    syndrome -> syndrome___array_cast_16;
+  } apply {
+    control (syndrome___array_cast_16) {
+      real_xor_constant(12, error);
+    }
+  }
+  syndrome___array_cast_17: qbit[6];
+  within {
+    real_xor_constant(17, syndrome);
+    syndrome -> syndrome___array_cast_17;
+  } apply {
+    control (syndrome___array_cast_17) {
+      real_xor_constant(20, error);
+    }
+  }
+  syndrome___array_cast_18: qbit[6];
+  within {
+    real_xor_constant(9, syndrome);
+    syndrome -> syndrome___array_cast_18;
+  } apply {
+    control (syndrome___array_cast_18) {
+      real_xor_constant(36, error);
+    }
+  }
+  syndrome___array_cast_19: qbit[6];
+  within {
+    real_xor_constant(27, syndrome);
+    syndrome -> syndrome___array_cast_19;
+  } apply {
+    control (syndrome___array_cast_19) {
+      real_xor_constant(24, error);
+    }
+  }
+  syndrome___array_cast_20: qbit[6];
+  within {
+    real_xor_constant(3, syndrome);
+    syndrome -> syndrome___array_cast_20;
+  } apply {
+    control (syndrome___array_cast_20) {
+      real_xor_constant(40, error);
+    }
+  }
+  syndrome___array_cast_21: qbit[6];
+  within {
+    real_xor_constant(39, syndrome);
+    syndrome -> syndrome___array_cast_21;
+  } apply {
+    control (syndrome___array_cast_21) {
+      real_xor_constant(48, error);
+    }
+  }
+}
+
+qfunc dqi_max_xor_sat_expanded___0(output y: qbit[6], output solution: qbit[6]) {
+  k_num_errors: qnum<2, False, 0>;
+  prepare_amplitudes([
+    0.0,
+    0.7071067811865475,
+    0.7071067811865477,
+    0.0
+  ], 0, k_num_errors);
+  k_unary: qbit[3];
+  binary_to_unary_expanded___0(k_num_errors, k_unary);
+  pad_zeros_expanded___0(k_unary, y);
+  prepare_dick_state_unary_input_expanded___5(y);
+  vector_product_phase_expanded___0(y);
+  matrix_vector_product_expanded___0(y, solution);
+  syndrome_decode_lookuptable_expanded___0(solution, y);
+  hadamard_transform(solution);
+}
+
+qfunc main(output y: qbit[6], output solution: qbit[6]) {
+  dqi_max_xor_sat_expanded___0(y, solution);
+}
diff --git a/algorithms/dqi/dqi_max_xorsat.synthesis_options.json b/algorithms/dqi/dqi_max_xorsat.synthesis_options.json
new file mode 100644
index 00000000..f67ca8cb
--- /dev/null
+++ b/algorithms/dqi/dqi_max_xorsat.synthesis_options.json
@@ -0,0 +1,43 @@
+{
+  "constraints": {
+    "max_gate_count": {},
+    "optimization_parameter": "width"
+  },
+  "preferences": {
+    "machine_precision": 8,
+    "custom_hardware_settings": {
+      "basis_gates": [
+        "ry",
+        "u2",
+        "u",
+        "sx",
+        "tdg",
+        "cz",
+        "sxdg",
+        "x",
+        "u1",
+        "sdg",
+        "z",
+        "t",
+        "id",
+        "p",
+        "s",
+        "y",
+        "cx",
+        "r",
+        "cy",
+        "rz",
+        "rx",
+        "h"
+      ],
+      "is_symmetric_connectivity": true
+    },
+    "debug_mode": true,
+    "synthesize_all_separately": false,
+    "output_format": ["qasm"],
+    "pretty_qasm": true,
+    "transpilation_option": "auto optimize",
+    "timeout_seconds": 300,
+    "random_seed": 2844049115
+  }
+}
diff --git a/tests/resources/timeouts.yaml b/tests/resources/timeouts.yaml
index 63efd220..184887f7 100644
--- a/tests/resources/timeouts.yaml
+++ b/tests/resources/timeouts.yaml
@@ -1,3 +1,5 @@
+algorithms/dqi/dqi_max_xorsat.ipynb: 200
+algorithms/dqi/dqi_max_xorsat.qmod: 200
 algorithms/algebraic/discrete_log/discrete_log.ipynb: 600
 algorithms/algebraic/discrete_log/discrete_log.qmod: 300
 algorithms/algebraic/discrete_log/discrete_log_large.qmod: 600