diff --git a/algorithms/gradient_estimation/gradient_estimation.ipynb b/algorithms/gradient_estimation/gradient_estimation.ipynb
new file mode 100644
index 000000000..6d6099ad7
--- /dev/null
+++ b/algorithms/gradient_estimation/gradient_estimation.ipynb
@@ -0,0 +1,1956 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "0",
+   "metadata": {},
+   "source": [
+    "# Initialization"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from gradient_estimation_helpers import *\n",
+    "\n",
+    "from classiq.execution.functions.util._logging import _logger"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2",
+   "metadata": {},
+   "source": [
+    "# Fast Quantum Algorithm for Numerical Gradient Estimation\n",
+    "> Given a scalar function of $d$ dimensions, $f(x_1, \\ldots, x_d)$, computing its gradient at a specific point is often essential.\n",
+    ">\n",
+    "> On a classical computer, this requires at least $d+1$ queries to $f$.\n",
+    ">\n",
+    "> This notebook presents a quantum algorithm that computes the gradient with a single query, based on the paper by S. P. Jordan [\\[1\\]](#original_paper).\n",
+    "> \n",
+    "> This represents a dramatic speedup for high-dimensional functions, where function evaluation is typically the dominant computational cost.\n",
+    ">\n",
+    "> - **Input:** A black-box function $f$ of $d$ dimensions.\n",
+    "> - **Promise:** The function is smooth and has bounded gradient: $|\\nabla f|<\\nabla f_{\\text{max}}$.\n",
+    "> - **Output:** The gradient $\\nabla f$ at the origin with $n$ bits of precision, encoded on a quantum register.\n",
+    "> \n",
+    "> **Complexity:** In black-box query complexity, the classical algorithm requires at least $d+1$ queries to $f$, while the quantum algorithm requires only one.\n",
+    ">\n",
+    "> ---\n",
+    ">\n",
+    "> **Keywords:** Foundational quantum algorithms, Gradient, Function evaluation, Oracle problem, Quantum Fourier Transform (QFT)\n",
+    ">\n",
+    "\n",
+    "The core idea is an in-place computation: first, the function is encoded directly into the phases of the coordinate superposition state. Then, an inverse QFT is applied to overwrite that exact same coordinate register with the gradient, extracting it as a measurable outcome without needing a separate output register.\n",
+    "\n",
+    "We demonstrate this step by step—first with a simplified version to build intuition, then with refinements for the complete algorithm.\n",
+    "\n",
+    "![Quantum Circuit](quantum_circuit.png)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3",
+   "metadata": {},
+   "source": [
+    "# Introduction"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4",
+   "metadata": {},
+   "source": [
+    "## Simplified explanation of the algorithm\n",
+    "The algorithm has two main steps:\n",
+    "1. Encode the function into the phases of the coordinate register's superposition\n",
+    "2. Apply an inverse QFT to transform the coordinate register directly into the measurable gradient state\n",
+    "\n",
+    "**In detail:**\n",
+    "\n",
+    "1. We define an interval $l$ with $N$ points around the origin where we estimate the gradient, establishing a resolution $\\mathrm{dx}=l/N$.\n",
+    "\n",
+    "To calculate the gradient at an arbitrary point $x_0$ instead of the origin, we can define an auxiliary function $\\tilde{f}(x) = f(x + x_0)$ and evaluate its gradient at the origin.\n",
+    "\n",
+    "For each dimension ($x_i$), we create the superposition $|\\delta_i\\rangle=|-l\\rangle+|-l+\\mathrm{dx}\\rangle+|-l+2\\mathrm{dx}\\rangle\\cdots|l-\\mathrm{dx}\\rangle$ using Hadamard gates on $|0\\rangle^{\\otimes n}$.\n",
+    "\n",
+    "*The actual encoding on qubits is discussed later.*\n",
+    "\n",
+    "For simplicity, we consider a single dimension. For $d > 1$, the state $|\\delta\\rangle$ is a tensor product over all dimensions:\n",
+    "$$|\\delta\\rangle=\\prod_i|\\delta_i\\rangle$$\n",
+    "\n",
+    "2. Using a phase oracle or the phase kickback technique, we encode the function as phases:\n",
+    "$$e^{i\\cdot2\\pi f(-l)}|-l\\rangle + e^{i\\cdot2\\pi f(-l+\\mathrm{dx})}|-l+\\mathrm{dx}\\rangle + e^{i\\cdot2\\pi f(-l+2\\mathrm{dx})}|-l+2\\mathrm{dx}\\rangle \\cdots e^{i\\cdot2\\pi f(l-\\mathrm{dx})}|l-\\mathrm{dx}\\rangle$$\n",
+    "![](step_2.png)\n",
+    "\n",
+    "3. For a sufficiently small interval, we approximate $f(x)\\approx f(0)+x\\cdot f'(0)$, allowing us to factor the state:\n",
+    "$$\\begin{aligned}\n",
+    "& e^{i\\cdot2\\pi f(0)}\\left(e^{i\\cdot2\\pi (-l)\\cdot f'(0)}\\lvert-l\\rangle + e^{i\\cdot2\\pi (-l+\\mathrm{dx})f'(0)}\\lvert-l+\\mathrm{dx}\\rangle + e^{i\\cdot2\\pi (-l+2\\mathrm{dx})f'(0)}\\lvert-l+2\\mathrm{dx}\\rangle \\cdots e^{i\\cdot2\\pi (l-\\mathrm{dx})f'(0)}\\lvert l-\\mathrm{dx}\\rangle\\right) \\\\\n",
+    "&= e^{i\\cdot2\\pi f(0)}\\sum_{-l}^{l-\\mathrm{dx}} e^{i\\cdot2\\pi jf'(0)}|j\\rangle\n",
+    "\\end{aligned}$$\n",
+    "![](step_3.png)\n",
+    "\n",
+    "4. This state is exactly the QFT of the computational basis state $|f'(0)\\rangle$. Applying the inverse QFT yields:\n",
+    "$$|f'(0)\\rangle$$\n",
+    "\n",
+    "5. Measure the register to obtain $f'(0)$ (or $\\nabla f$ for multi-dimensional functions)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5",
+   "metadata": {},
+   "source": [
+    "## Full explanation of the algorithm\n",
+    "The simplified explanation captures the key idea but omits two important details: handling negative values and fractional representation.\n",
+    "\n",
+    "When estimating the gradient, we analyze an interval $l$ around the origin.\n",
+    "\n",
+    "The state $|\\delta_i\\rangle$ represents $N$ equally spaced points in this interval, normalized as:\n",
+    "$$ x=\\frac{l}{N}\\delta $$\n",
+    "\n",
+    "With signed $\\delta$ ranging from $-N/2$ to $N/2-1$: $\\delta=-N/2$ is the leftmost point ($-l/2$), $\\delta=0$ is the origin, and $\\delta=N/2-1$ is the rightmost point ($l/2$)*.\n",
+    "\n",
+    "We also normalize the output. Assuming the gradient is bounded between $-m/2$ and $m/2$, we represent those values using the $N$ states:\n",
+    "$$ \\nabla f=\\frac{m}{N}\\delta_{measured} $$\n",
+    "\n",
+    "When applying the algorithm, we choose $l$ and $m$ based on prior knowledge of $f(x)$. The value $N$ determines the final resolution.\n",
+    "\n",
+    "$l$ must be small enough to keep the function approximately linear, and $m$ must exceed the maximum expected gradient magnitude while maintaining sufficient resolution.\n",
+    "\n",
+    "Using these two normalizations, we modify the algorithm:\n",
+    "1. In step 2, instead of applying $f(\\delta)$, we apply $\\frac{N}{ml}f\\left(\\frac{l}{N}\\delta\\right)$.\n",
+    "2. In step 5, the signed measurement directly gives $\\frac{N}{m}\\nabla f$, so $\\nabla f = \\frac{m}{N}\\delta_{measured}$.\n",
+    "\n",
+    "Using a signed register means the measured $\\delta_{measured}$ is already the correct signed value with no further adjustment needed.\n",
+    "\n",
+    "\\* The rightmost point is actually $l/2-l/N$ rather than $l/2$, because the center is at the origin and $N$ must be even. However, this distinction does not affect the conceptual understanding."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6",
+   "metadata": {},
+   "source": [
+    "## Parameter Selection\n",
+    "We need to select appropriate values for $l$, $m$, and $N$. We use the following notation: \n",
+    "* $\\nabla f_{\\text{max}}$ — bound on the gradient magnitude: $|\\nabla f|<\\nabla f_{\\text{max}}$\n",
+    "* $\\epsilon$ — desired accuracy: $|\\nabla f_{\\text{est}}-\\nabla f| < \\epsilon$\n",
+    "* $d$ — dimensionality of $f$\n",
+    "* $D_2$ — bound on the second derivative of $f$ near the origin\n",
+    "\n",
+    "### Selecting $l$\n",
+    "Choose $l$ to ensure the function remains approximately linear. Similar to classical numerical differentiation, this interval must be sufficiently small.\n",
+    "\n",
+    "To keep gradient variation within accuracy $\\epsilon$, we require $\\nabla f_{\\text{max}} - \\nabla f_{\\text{min}} \\le \\epsilon$.\n",
+    "\n",
+    "For a single dimension, the second derivative is approximately $f''(x) \\approx (\\nabla f_{\\text{max}} - \\nabla f_{\\text{min}}) / l$. This gives $l < \\frac{\\epsilon}{|f''(x)|}$.\n",
+    "\n",
+    "Using $D_2$ as the second derivative bound, we get $l < \\frac{\\epsilon}{D_2}$.\n",
+    "\n",
+    "In multiple dimensions, we sum deviations as root-mean-square across all $d$ dimensions, introducing a factor of $1/\\sqrt{d}$.\n",
+    "\n",
+    "To improve this bound, we scale by the uniform distribution variance, $\\frac{1}{12}$.\n",
+    "\n",
+    "The final bound for $l$ is: \n",
+    "$$l\\leq\\frac{2\\sqrt{3}\\epsilon}{D_2\\sqrt{d}}$$\n",
+    "\n",
+    "For a one-dimensional quadratic function $f(x)=ax^2+bx+c$, we need $l$ smaller than $\\frac{\\sqrt{12}\\epsilon}{2a}$.\n",
+    "\n",
+    "Furthermore, in order to minimize the number of bits of precision to which $f$ must be evaluated, $l$ should be chosen as large as possible, subject to the constraint above. So $l$ should be chosen tightly.\n",
+    "\n",
+    "See Jordan's paper [\\[1\\]](#original_paper) for a full derivation.\n",
+    "\n",
+    "### Selecting $m$\n",
+    "The parameter $m$ bounds the gradient magnitude. Since the gradient is signed, we need $m \\ge 2\\nabla f_{\\text{max}}$.\n",
+    "\n",
+    "The resolution of the result is $\\frac{m}{N}$. For a given register size and accuracy $\\epsilon$, the upper bound is $m \\le 2N\\epsilon$.\n",
+    "\n",
+    "### Selecting $n$\n",
+    "$N=2^n$ defines the result resolution. The step size between possible outcomes is $\\frac{m}{N}$.\n",
+    "\n",
+    "To achieve accuracy $\\epsilon$, we need $N\\geq\\frac{m}{2\\epsilon}$.\n",
+    "\n",
+    "Assuming tight $m$ selection, this gives a lower bound for the number of qubits:\n",
+    "$$n\\geq\\log_2\\left(\\frac{\\nabla f_{\\text{max}}}{\\epsilon}\\right)$$\n",
+    "\n",
+    "### Summary\n",
+    "Given $\\nabla f_{\\text{max}}$ and desired accuracy $\\epsilon$, select parameters as:\n",
+    "\n",
+    "\n",
+    "| Parameter | Role | Constraint |\n",
+    "|---|---|---|\n",
+    "| $l$ | Sampling interval | $l \\leq \\frac{2\\sqrt{3}\\epsilon}{D_2\\sqrt{d}}$ — keeps $f$ approximately linear |\n",
+    "| $m$ | Gradient range | $2\\nabla f_{\\max} \\leq m \\leq 2\\cdot2^n\\epsilon$ |\n",
+    "| $n$ | Qubits / resolution | $n \\geq \\log_2(\\nabla f_{\\max} / \\epsilon)$ |"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7",
+   "metadata": {},
+   "source": [
+    "## Outline\n",
+    "This notebook covers:\n",
+    "1. Theoretical foundation and parameter selection\n",
+    "2. Implementation\n",
+    "    - Phase state preparation (phase kickback and direct methods)\n",
+    "    - Inverse QFT for gradient extraction\n",
+    "    - Examples with linear and non-linear functions\n",
+    "3. Performance analysis\n",
+    "4. Multi-dimensional examples ($d > 1$)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8",
+   "metadata": {},
+   "source": [
+    "# Implementation"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9",
+   "metadata": {},
+   "source": [
+    "## State Preparation"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "10",
+   "metadata": {},
+   "source": [
+    "### Phase Kickback"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "11",
+   "metadata": {},
+   "source": [
+    "The first step is to prepare the state:\n",
+    "$$\\sum_{\\delta}e^{i2\\pi\\frac{N}{ml}f(\\frac{l}{N}\\delta)}|\\delta\\rangle$$\n",
+    "\n",
+    "The paper assumes an oracle $|x\\rangle \\rightarrow |f(x)\\rangle$, which we use with the phase kickback technique to create this state.\n",
+    "\n",
+    "The next example demonstrates how this works."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "12",
+   "metadata": {},
+   "source": [
+    "The phase kickback has three main steps:\n",
+    "1. Apply Hadamard gates on $|\\delta\\rangle$ to create a superposition of all sample points\n",
+    "2. Initialize the ancilla to $|1111...1\\rangle$ (in binary) and apply QFT\n",
+    "3. Add $f(\\delta)$ to the ancilla; the function value is \"kicked back\" as a phase\n",
+    "\n",
+    "See the [appendix](#appendix-1---phase-kickback) for details."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "13",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Submitting job to simulator\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Circuit Width: 7\n",
+      "Circuit Depth: 47\n",
+      "Gate Counts: {'u': 34, 'cx': 28}\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Job: https://platform.classiq.io/jobs/f79b6a72-bc04-4c98-83d3-2af28406d793\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.microsoft.datawrangler.viewer.v0+json": {
+       "columns": [
+        {
+         "name": "index",
+         "rawType": "int64",
+         "type": "integer"
+        },
+        {
+         "name": "x",
+         "rawType": "int64",
+         "type": "integer"
+        },
+        {
+         "name": "amplitude",
+         "rawType": "complex128",
+         "type": "unknown"
+        },
+        {
+         "name": "magnitude",
+         "rawType": "object",
+         "type": "string"
+        },
+        {
+         "name": "phase",
+         "rawType": "object",
+         "type": "string"
+        },
+        {
+         "name": "probability",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "bitstring",
+         "rawType": "object",
+         "type": "string"
+        }
+       ],
+       "ref": "041a0391-4ab7-47e1-bdf8-e5e656a0016d",
+       "rows": [
+        [
+         "7",
+         "-4",
+         "(-0.24999999999999964+0.25000000000000006j)",
+         "0.35",
+         "0.75π",
+         "0.12499999999999986",
+         "0000100"
+        ],
+        [
+         "4",
+         "-3",
+         "(-0.2500000000000003-0.2499999999999997j)",
+         "0.35",
+         "-0.75π",
+         "0.12499999999999997",
+         "0000101"
+        ],
+        [
+         "5",
+         "-2",
+         "(0.24999999999999978-0.2500000000000001j)",
+         "0.35",
+         "-0.25π",
+         "0.12499999999999994",
+         "0000110"
+        ],
+        [
+         "2",
+         "-1",
+         "(0.2500000000000003+0.24999999999999972j)",
+         "0.35",
+         "0.25π",
+         "0.12500000000000003",
+         "0000111"
+        ],
+        [
+         "6",
+         "0",
+         "(-0.24999999999999964+0.2500000000000001j)",
+         "0.35",
+         "0.75π",
+         "0.12499999999999986",
+         "0000000"
+        ],
+        [
+         "3",
+         "1",
+         "(-0.2500000000000003-0.2499999999999997j)",
+         "0.35",
+         "-0.75π",
+         "0.12499999999999997",
+         "0000001"
+        ],
+        [
+         "1",
+         "2",
+         "(0.24999999999999983-0.2500000000000003j)",
+         "0.35",
+         "-0.25π",
+         "0.12500000000000006",
+         "0000010"
+        ],
+        [
+         "0",
+         "3",
+         "(0.2500000000000004+0.24999999999999983j)",
+         "0.35",
+         "0.25π",
+         "0.12500000000000008",
+         "0000011"
+        ]
+       ],
+       "shape": {
+        "columns": 6,
+        "rows": 8
+       }
+      },
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>x</th>\n",
+       "      <th>amplitude</th>\n",
+       "      <th>magnitude</th>\n",
+       "      <th>phase</th>\n",
+       "      <th>probability</th>\n",
+       "      <th>bitstring</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>-4</td>\n",
+       "      <td>-0.25+0.25j</td>\n",
+       "      <td>0.35</td>\n",
+       "      <td>0.75π</td>\n",
+       "      <td>0.125</td>\n",
+       "      <td>0000100</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>-3</td>\n",
+       "      <td>-0.25-0.25j</td>\n",
+       "      <td>0.35</td>\n",
+       "      <td>-0.75π</td>\n",
+       "      <td>0.125</td>\n",
+       "      <td>0000101</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>-2</td>\n",
+       "      <td>0.25-0.25j</td>\n",
+       "      <td>0.35</td>\n",
+       "      <td>-0.25π</td>\n",
+       "      <td>0.125</td>\n",
+       "      <td>0000110</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>-1</td>\n",
+       "      <td>0.25+0.25j</td>\n",
+       "      <td>0.35</td>\n",
+       "      <td>0.25π</td>\n",
+       "      <td>0.125</td>\n",
+       "      <td>0000111</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>0</td>\n",
+       "      <td>-0.25+0.25j</td>\n",
+       "      <td>0.35</td>\n",
+       "      <td>0.75π</td>\n",
+       "      <td>0.125</td>\n",
+       "      <td>0000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>1</td>\n",
+       "      <td>-0.25-0.25j</td>\n",
+       "      <td>0.35</td>\n",
+       "      <td>-0.75π</td>\n",
+       "      <td>0.125</td>\n",
+       "      <td>0000001</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2</td>\n",
+       "      <td>0.25-0.25j</td>\n",
+       "      <td>0.35</td>\n",
+       "      <td>-0.25π</td>\n",
+       "      <td>0.125</td>\n",
+       "      <td>0000010</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>3</td>\n",
+       "      <td>0.25+0.25j</td>\n",
+       "      <td>0.35</td>\n",
+       "      <td>0.25π</td>\n",
+       "      <td>0.125</td>\n",
+       "      <td>0000011</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   x   amplitude magnitude   phase  probability bitstring\n",
+       "7 -4 -0.25+0.25j      0.35   0.75π        0.125   0000100\n",
+       "4 -3 -0.25-0.25j      0.35  -0.75π        0.125   0000101\n",
+       "5 -2  0.25-0.25j      0.35  -0.25π        0.125   0000110\n",
+       "2 -1  0.25+0.25j      0.35   0.25π        0.125   0000111\n",
+       "6  0 -0.25+0.25j      0.35   0.75π        0.125   0000000\n",
+       "3  1 -0.25-0.25j      0.35  -0.75π        0.125   0000001\n",
+       "1  2  0.25-0.25j      0.35  -0.25π        0.125   0000010\n",
+       "0  3  0.25+0.25j      0.35   0.25π        0.125   0000011"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Set the default values:\n",
+    "# l = 0.5, m = 2, n = 3, n0 = 3\n",
+    "p = params()\n",
+    "\n",
+    "# Set the function we want to calculate the gradient of:\n",
+    "# f(x) = 0.5*x + 0.25\n",
+    "# Gradient: f'(0) = 0.5\n",
+    "p.set_function(p.linear, (0.5, 0.25))\n",
+    "\n",
+    "# Unpack the parameters to global variables for easier use\n",
+    "p.unpack(globals())\n",
+    "\n",
+    "\n",
+    "@qfunc\n",
+    "def main(x: Output[QNum[n, SIGNED, 0]]):\n",
+    "    # 1. State preparation\n",
+    "\n",
+    "    # 1.1. Set the coordinates state - Apply Hadamard gate on the coordinates register\n",
+    "    allocate(x)\n",
+    "    hadamard_transform(x)\n",
+    "\n",
+    "    # 1.2. Set the ancilla state - Apply QFT to |1111...1> state\n",
+    "    ancilla = QNum(\"ancilla\", n0, SIGNED, n0)\n",
+    "    prepare_basis_state([True] * n0, ancilla)\n",
+    "    qft(ancilla)\n",
+    "\n",
+    "    # 1.3. Apply the function f on the ancilla, to create the phase kickback\n",
+    "    # Calculate the normalized f and add it to the ancilla\n",
+    "    val = f_normalized(x)\n",
+    "    inplace_add(val, ancilla)\n",
+    "\n",
+    "    # 2. Next step in the algorithm: QFT inverse on the coordinates register\n",
+    "    # invert(lambda: qft(x))\n",
+    "\n",
+    "    # 3. Return the ancilla back to |000...0> state and drop it\n",
+    "    invert(lambda: qft(ancilla))\n",
+    "    apply_to_all(X, ancilla)\n",
+    "    drop(ancilla)\n",
+    "\n",
+    "\n",
+    "# Run using a statevector simulator\n",
+    "qprog_ancilla = synthesize(main)\n",
+    "# show(qprog) # Uncomment to see the circuit\n",
+    "print(\"Circuit Width:\", qprog_ancilla.data.width)\n",
+    "print(\"Circuit Depth:\", qprog_ancilla.transpiled_circuit.depth)\n",
+    "print(\"Gate Counts:\", qprog_ancilla.transpiled_circuit.count_ops)\n",
+    "\n",
+    "df = calculate_state_vector(qprog_ancilla)\n",
+    "df.sort_values(by=\"x\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "14",
+   "metadata": {},
+   "source": [
+    "Let's examine the phase compared to the classical function value:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "15",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.microsoft.datawrangler.viewer.v0+json": {
+       "columns": [
+        {
+         "name": "x",
+         "rawType": "int64",
+         "type": "integer"
+        },
+        {
+         "name": "f_classical",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "phase_over_2pi",
+         "rawType": "float64",
+         "type": "float"
+        }
+       ],
+       "ref": "54976780-5091-49d9-80bd-1600df3a5eac",
+       "rows": [
+        [
+         "-4",
+         "-1.0",
+         "-1.0"
+        ],
+        [
+         "-3",
+         "-0.75",
+         "-0.75"
+        ],
+        [
+         "-2",
+         "-0.5",
+         "-0.5"
+        ],
+        [
+         "-1",
+         "-0.25",
+         "-0.25"
+        ],
+        [
+         "0",
+         "0.0",
+         "0.0"
+        ],
+        [
+         "1",
+         "0.25",
+         "0.25"
+        ],
+        [
+         "2",
+         "0.5",
+         "0.5"
+        ],
+        [
+         "3",
+         "0.75",
+         "0.75"
+        ]
+       ],
+       "shape": {
+        "columns": 2,
+        "rows": 8
+       }
+      },
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>f_classical</th>\n",
+       "      <th>phase_over_2pi</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>x</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>-4</th>\n",
+       "      <td>-1.00</td>\n",
+       "      <td>-1.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>-3</th>\n",
+       "      <td>-0.75</td>\n",
+       "      <td>-0.75</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>-2</th>\n",
+       "      <td>-0.50</td>\n",
+       "      <td>-0.50</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>-1</th>\n",
+       "      <td>-0.25</td>\n",
+       "      <td>-0.25</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0.00</td>\n",
+       "      <td>0.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>0.25</td>\n",
+       "      <td>0.25</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>0.50</td>\n",
+       "      <td>0.50</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>0.75</td>\n",
+       "      <td>0.75</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    f_classical  phase_over_2pi\n",
+       "x                              \n",
+       "-4        -1.00           -1.00\n",
+       "-3        -0.75           -0.75\n",
+       "-2        -0.50           -0.50\n",
+       "-1        -0.25           -0.25\n",
+       " 0         0.00            0.00\n",
+       " 1         0.25            0.25\n",
+       " 2         0.50            0.50\n",
+       " 3         0.75            0.75"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Shift the classical function to match the quantum convention\n",
+    "f_classical = f_normalized(df[\"x\"]) - f_normalized(0)\n",
+    "# Create a simplified dataframe with the relevant information, and sort it by the x values.\n",
+    "phases = np.angle(df[\"amplitude\"]).astype(float)\n",
+    "phases_over_2pi = phases / (2 * np.pi)\n",
+    "simplified_df = pd.DataFrame(\n",
+    "    {\"f_classical\": f_classical, \"phase_over_2pi\": phases_over_2pi.round(5)}\n",
+    ")\n",
+    "simplified_df.index = df[\"x\"]\n",
+    "simplified_df.sort_index(inplace=True)\n",
+    "# Unwrap the phase\n",
+    "simplified_df[\"phase_over_2pi\"] = np.unwrap(simplified_df[\"phase_over_2pi\"], period=1)\n",
+    "# Get rid of the global phase\n",
+    "simplified_df[\"phase_over_2pi\"] -= simplified_df[\"phase_over_2pi\"].iloc[N // 2]\n",
+    "simplified_df[\"f_classical\"] -= simplified_df[\"f_classical\"].iloc[N // 2]\n",
+    "\n",
+    "# Plot the results\n",
+    "plt.figure()\n",
+    "plot_classical()\n",
+    "simplified_df.plot(style=\"o\", ax=plt.gca())\n",
+    "plt.legend()\n",
+    "plt.show()\n",
+    "\n",
+    "# Show the results as a dataframe\n",
+    "simplified_df"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "16",
+   "metadata": {},
+   "source": [
+    "The graph shows the original function (gray), classical values (blue), and quantum phase at sample points (orange). Results are displayed in both normalized coordinates (black axes) and original values (blue axes)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "17",
+   "metadata": {},
+   "source": [
+    "### Direct Phase"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "18",
+   "metadata": {},
+   "source": [
+    "While the phase kickback approach is sometimes well-suited for hardware implementations with a state oracle, it requires significant circuit overhead in simulation. A direct approach provides more efficient state preparation for our purposes."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "19",
+   "metadata": {},
+   "source": [
+    "A simpler approach is to directly prepare the desired state:\n",
+    "$$\\sum_{\\delta}e^{i2\\pi\\frac{N}{ml}f(\\frac{l}{N}\\delta)}|\\delta\\rangle$$\n",
+    "\n",
+    "The example below demonstrates this method. Compare the circuit width, depth, and gate counts to see the efficiency gain. In practice, the choice between methods depends on whether you have access to an efficient oracle for the function."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "20",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Submitting job to simulator\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Circuit Width: 3\n",
+      "Circuit Depth: 1\n",
+      "Gate Counts: {'u': 3}\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Job: https://platform.classiq.io/jobs/deb9fbe3-dadb-4f44-b33c-dbcba464e3fb\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "p = params()\n",
+    "# f(x) = 0.5*x + 0.25\n",
+    "# Gradient: f'(0) = 0.5\n",
+    "p.set_function(p.linear, (0.5, 0.25))\n",
+    "p.unpack(globals())\n",
+    "\n",
+    "\n",
+    "@qfunc\n",
+    "def main(x: Output[QNum[n, SIGNED, 0]]):\n",
+    "    # 1. State preparation\n",
+    "    allocate(x)\n",
+    "    hadamard_transform(x)\n",
+    "    phase(f_normalized(x), 2 * pi)\n",
+    "\n",
+    "    # 2. Next step in the algorithm: QFT inverse on the coordinates register\n",
+    "    # invert(lambda: qft(x))\n",
+    "\n",
+    "\n",
+    "# Run using a statevector simulator\n",
+    "qprog = synthesize(main)\n",
+    "# show(qprog) # Uncomment to see the circuit\n",
+    "print(\"Circuit Width:\", qprog.data.width)\n",
+    "print(\"Circuit Depth:\", qprog.transpiled_circuit.depth)\n",
+    "print(\"Gate Counts:\", qprog.transpiled_circuit.count_ops)\n",
+    "\n",
+    "df = calculate_state_vector(qprog)\n",
+    "df.sort_values(by=\"x\")\n",
+    "\n",
+    "simplified_df = simplify_df(df)\n",
+    "plot_simplified_df(simplified_df)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "21",
+   "metadata": {},
+   "source": [
+    "The state is identical to the phase kickback method. Moving forward, we'll use the direct method for its superior efficiency."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "22",
+   "metadata": {},
+   "source": [
+    "### Quadratic Function"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "23",
+   "metadata": {},
+   "source": [
+    "Non-linear functions are more interesting. In the next example, we explore a quadratic function.\n",
+    "\n",
+    "Note that the critical step occurs in the next phase when we apply the QFT. Here, the phases follow the function exactly without linearization. The QFT will extract the linear (gradient) component from these phase values."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "24",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Submitting job to simulator\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Circuit Width: 3\n",
+      "Circuit Depth: 9\n",
+      "Gate Counts: {'u': 6, 'cx': 6}\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Job: https://platform.classiq.io/jobs/19028135-c695-4b39-a8a0-a30a04eee106\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "p = params()\n",
+    "# f(x) = 0.5*x^2 + 0.25*x + 0.1\n",
+    "# Gradient: f'(x) = x + 0.25, so f'(0) = 0.25\n",
+    "p.set_function(p.quadratic, (0.5, 0.25, 0.1))\n",
+    "p.l = 2\n",
+    "p.unpack(globals())\n",
+    "\n",
+    "\n",
+    "@qfunc\n",
+    "def main(x: Output[QNum[n, SIGNED, 0]]):\n",
+    "    # 1. State preparation\n",
+    "    allocate(x)\n",
+    "    hadamard_transform(x)\n",
+    "    phase(f_normalized(x), 2 * pi)\n",
+    "\n",
+    "    # 2. Next step in the algorithm: QFT inverse on the coordinates register\n",
+    "    # invert(lambda: qft(x))\n",
+    "\n",
+    "\n",
+    "# Run using a statevector simulator\n",
+    "qprog = synthesize(main)\n",
+    "# show(qprog) # Uncomment to see the circuit\n",
+    "print(\"Circuit Width:\", qprog.data.width)\n",
+    "print(\"Circuit Depth:\", qprog.transpiled_circuit.depth)\n",
+    "print(\"Gate Counts:\", qprog.transpiled_circuit.count_ops)\n",
+    "\n",
+    "df = calculate_state_vector(qprog)\n",
+    "df.sort_values(by=\"x\")\n",
+    "\n",
+    "simplified_df = simplify_df(df)\n",
+    "plot_simplified_df(simplified_df)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "25",
+   "metadata": {},
+   "source": [
+    "Decreasing $l$ narrows the sampling interval, making the function appear more linear within that region."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "26",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Submitting job to simulator\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Circuit Width: 3\n",
+      "Circuit Depth: 9\n",
+      "Gate Counts: {'u': 6, 'cx': 6}\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Job: https://platform.classiq.io/jobs/f5e85e56-5921-4e56-b736-bd7f86101e21\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "p = params()\n",
+    "# f(x) = 0.5*x^2 + 0.25*x + 0.1\n",
+    "# Gradient: f'(x) = x + 0.25, so f'(0) = 0.25\n",
+    "p.set_function(p.quadratic, (0.5, 0.25, 0.1))\n",
+    "p.l = 0.2\n",
+    "p.unpack(globals())\n",
+    "\n",
+    "\n",
+    "@qfunc\n",
+    "def main(x: Output[QNum[n, SIGNED, 0]]):\n",
+    "    # 1. State preparation\n",
+    "    allocate(x)\n",
+    "    hadamard_transform(x)\n",
+    "    phase(f_normalized(x), 2 * pi)\n",
+    "\n",
+    "    # 2. Next step in the algorithm: QFT inverse on the coordinates register\n",
+    "    # invert(lambda: qft(x))\n",
+    "\n",
+    "\n",
+    "# Run using a statevector simulator\n",
+    "qprog = synthesize(main)\n",
+    "# show(qprog) # Uncomment to see the circuit\n",
+    "print(\"Circuit Width:\", qprog.data.width)\n",
+    "print(\"Circuit Depth:\", qprog.transpiled_circuit.depth)\n",
+    "print(\"Gate Counts:\", qprog.transpiled_circuit.count_ops)\n",
+    "\n",
+    "df = calculate_state_vector(qprog)\n",
+    "df.sort_values(by=\"x\")\n",
+    "\n",
+    "simplified_df = simplify_df(df)\n",
+    "plot_simplified_df(simplified_df)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "27",
+   "metadata": {},
+   "source": [
+    "## Full Algorithm"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "28",
+   "metadata": {},
+   "source": [
+    "### Implementation\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "29",
+   "metadata": {},
+   "source": [
+    "The final step is to apply the inverse QFT to the coordinates. This extracts the gradient from the phases and produces a measurable state containing the normalized gradient value.\n",
+    "\n",
+    "We will first define a QFunc that preforms the algorithm and calculates the gradient of a given function $f$, for single and multiple dimensions, and another function that transforms a given function (either Callable or QCallable) into a normalized phase oracle."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "30",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "@qfunc\n",
+    "def gradient(f: QCallable[[QNum]], x: QNum) -> None:\n",
+    "    \"\"\"\n",
+    "    Apply the gradient estimation algorithm to estimate the gradient of 1 dimensional function $f$ at $x=0$.\n",
+    "    The function $f$ is given as a quantum oracle that applies the phase kickback.\n",
+    "    \"\"\"\n",
+    "    # 1. State preparation\n",
+    "    hadamard_transform(x)\n",
+    "    f(x)\n",
+    "    # 2. QFT inverse on the coordinates register\n",
+    "    invert(lambda: qft(x))\n",
+    "\n",
+    "\n",
+    "@qfunc\n",
+    "def gradient_nd(f: QCallable[QArray[QNum]], coords: QArray[QNum]) -> None:\n",
+    "    \"\"\"\n",
+    "    Apply the gradient estimation algorithm to estimate the gradient of multi dimensional function $f$ at $\\vec{x}=0$.\n",
+    "    The function $f$ is given as a quantum oracle that applies the phase kickback.\n",
+    "    \"\"\"\n",
+    "    # 1. State preparation\n",
+    "    hadamard_transform(coords)\n",
+    "    f(coords)\n",
+    "    # 2. QFT inverse on the coordinates register\n",
+    "    repeat(coords.len, lambda i: invert(lambda: qft(coords[i])))\n",
+    "\n",
+    "\n",
+    "def make_phase_oracle(\n",
+    "    f: Callable,\n",
+    "    l: float,\n",
+    "    m: float,\n",
+    "    N: int,\n",
+    "    x0: float = 0.0,\n",
+    "    d: int = 1,\n",
+    ") -> QCallable:\n",
+    "    \"\"\"Return a @qfunc phase oracle that applies phase(f_norm(x), 2*pi).\n",
+    "\n",
+    "    The oracle encodes the normalized function value as a phase:\n",
+    "        f_norm(x) = f(l/N * x - x0) * N / (l * m)\n",
+    "\n",
+    "    Args:\n",
+    "        f:   Symbolic Python callable representing the mathematical function.\n",
+    "        l:   Sampling interval half-width.\n",
+    "        m:   Gradient magnitude bound (output range).\n",
+    "        N:   Number of sample points (2^n).\n",
+    "        x0:  Evaluation point; shifts the sampling window.\n",
+    "        d:   Dimensionality of the function.\n",
+    "    \"\"\"\n",
+    "    if d == 1:\n",
+    "\n",
+    "        @qfunc\n",
+    "        def phase_oracle(x: QNum) -> None:\n",
+    "            phase(f(l / N * x - x0) * N / (l * m), 2 * pi)\n",
+    "\n",
+    "    else:\n",
+    "\n",
+    "        @qfunc\n",
+    "        def phase_oracle(coords: QArray[QNum]) -> None:\n",
+    "            args = [l / N * coords[i] - x0 for i in range(d)]\n",
+    "            phase(f(*args) * N / (l * m), 2 * pi)\n",
+    "\n",
+    "    return phase_oracle"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "31",
+   "metadata": {},
+   "source": [
+    "### Linear Functions"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "32",
+   "metadata": {},
+   "source": [
+    "We will see the first example on a linear function.\\\n",
+    "We switch from statevector simulation to standard simulation, which measures the final result rather than examining phase values directly."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "33",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Submitting job to simulator\n",
+      "Job: https://platform.classiq.io/jobs/c2584af9-9b57-43d9-991a-70772ca1c7a4\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Parsed probabilities: {-2: 1.0}\n",
+      "The analytical gradient is: -0.5\n",
+      "The majority gradient is: -0.5\n",
+      "The majority result is correct\n",
+      "####################################################\n",
+      "Success rate: 100.00% (2048/2048 shots)\n",
+      "[\u001b[92m██████████████████████████████████████████████████\u001b[91m\u001b[0m] 100.00%\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 1800x700 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "p = params()\n",
+    "p.set_function(p.linear, (-0.5, 0.25))\n",
+    "p.unpack(globals())\n",
+    "\n",
+    "\n",
+    "@qfunc\n",
+    "def main(x: Output[QNum[n, SIGNED, 0]]):\n",
+    "    allocate(x)\n",
+    "    gradient(make_phase_oracle(f, l=l, m=m, N=N, x0=0), x)\n",
+    "\n",
+    "\n",
+    "qprog_linear = synthesize(main)\n",
+    "# show(qprog_linear) # Uncomment to see the circuit\n",
+    "df = sample(qprog_linear)\n",
+    "\n",
+    "# Translate the majority state to a gradient value\n",
+    "majority_state = dict(df.iloc[0])\n",
+    "value = majority_state.get(\"x\")\n",
+    "# Divide by (N/m) to get the actual gradient value.\n",
+    "majority_gradient = value / (N / m)\n",
+    "analytical_gradient = p.analytical_gradient(0)\n",
+    "\n",
+    "# Or use the helper function:\n",
+    "# majority_gradient = state_to_gradient(majority_state.get('x'), p)\n",
+    "\n",
+    "# Print the results and compute the majority gradient\n",
+    "print(\"Parsed probabilities:\", df.set_index(\"x\").to_dict()[\"probability\"])\n",
+    "print(f\"The analytical gradient is: {analytical_gradient}\")\n",
+    "print(f\"The majority gradient is: {majority_gradient}\")\n",
+    "\n",
+    "# Check if the majority result is correct within the resolution of the algorithm\n",
+    "resolution = m / N\n",
+    "is_correct = abs(majority_gradient - analytical_gradient) < resolution / 2\n",
+    "print(f\"The majority result is\", \"correct\" if is_correct else \"incorrect\")\n",
+    "print(\"####################################################\")\n",
+    "\n",
+    "# Compute the success rate of the algorithm, i.e. the percentage of shots that are correct within the resolution of the algorithm.\n",
+    "success_rate, success_shots, total_shots = compute_success_rate(\n",
+    "    df, analytic_derivatives={\"x\": analytical_gradient}, p=p\n",
+    ")\n",
+    "print(f\"Success rate: {success_rate:.2%} ({success_shots}/{total_shots} shots)\")\n",
+    "show_bar(success_rate)\n",
+    "\n",
+    "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(18, 7))\n",
+    "\n",
+    "x_array = np.arange(-N // 2, N // 2)\n",
+    "f_classical = f_normalized(x_array)\n",
+    "f_classical -= f_classical[N // 2]\n",
+    "\n",
+    "plt.sca(ax1)\n",
+    "plot_classical()\n",
+    "ax1.plot(x_array, f_classical, \"o\", label=\"Theoretical values\", markersize=8)\n",
+    "xmin, xmax = -N, N\n",
+    "ymin, ymax = -N // 2, N // 2\n",
+    "ax1.set_xlim(xmin, xmax)\n",
+    "ax1.set_ylim(ymin, ymax)\n",
+    "ax1.vlines(-N // 2, ymin, ymax, colors=\"lightgray\", linestyles=\"dashed\")\n",
+    "ax1.vlines(N // 2 - 1, ymin, ymax, colors=\"lightgray\", linestyles=\"dashed\")\n",
+    "ax1.hlines(-N / 4 + 0.5, xmin, xmax, colors=\"lightgray\", linestyles=\"dashed\")\n",
+    "ax1.hlines(N / 4, xmin, xmax, colors=\"lightgray\", linestyles=\"dashed\")\n",
+    "ax1.legend(fontsize=12)\n",
+    "ax1.set_title(\"Theoretical Phases\", fontsize=14, fontweight=\"bold\")\n",
+    "ax1.tick_params(labelsize=11)\n",
+    "ax1.set_xlabel(\"x (index)\", fontsize=12)\n",
+    "ax1.set_ylabel(\"f (normalized)\", fontsize=12)\n",
+    "\n",
+    "plt.sca(ax2)\n",
+    "percentage = df[\"counts\"] / df[\"counts\"].sum() * 100\n",
+    "ax2.bar(df[\"x\"], percentage, color=\"lightblue\", label=\"Measurement counts\")\n",
+    "ax2.set_xlabel(\"x (index)\", fontsize=12)\n",
+    "ax2.set_ylabel(\"Percentage of shots (%)\", fontsize=12)\n",
+    "ax2.set_xlim(-N // 2 - 1, N // 2)\n",
+    "ax2.set_ylim(0, 100)\n",
+    "ax2.set_title(\"Measurement Histogram\", fontsize=14, fontweight=\"bold\")\n",
+    "ax2.tick_params(labelsize=11)\n",
+    "if analytical_gradient is not None:\n",
+    "    x_analytic = analytical_gradient * (N / m)\n",
+    "    ax2.axvline(\n",
+    "        x=x_analytic,\n",
+    "        color=\"green\",\n",
+    "        linestyle=\"dashed\",\n",
+    "        label=\"Analytical gradient\",\n",
+    "        linewidth=2,\n",
+    "    )\n",
+    "ax2.legend(fontsize=12)\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "34",
+   "metadata": {},
+   "source": [
+    "The measured gradient is always a multiple of the resolution $m/N$. In the previous example, the exact gradient -0.5 was a multiple of the resolution 0.25, so the success rate was 100%.\n",
+    "\n",
+    "When the gradient is not a multiple of the resolution, the result becomes a superposition of nearby states. The algorithm still provides a good approximation, as shown in the next example."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "35",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Submitting job to simulator\n",
+      "Job: https://platform.classiq.io/jobs/3f1c1fa2-1576-484e-98d3-244f107a2326\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Parsed probabilities: {2: 0.89697265625, 3: 0.044921875, 1: 0.01953125, -4: 0.01171875, 0: 0.00927734375, -3: 0.0068359375, -1: 0.00634765625, -2: 0.00439453125}\n",
+      "The analytical gradient is: 0.55\n",
+      "The majority gradient is: 0.5\n",
+      "The majority result is correct\n",
+      "####################################################\n",
+      "Success rate: 89.70% (1837/2048 shots)\n",
+      "[\u001b[92m█████████████████████████████████████████████\u001b[91m-----\u001b[0m] 89.70%\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 1800x700 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "0.89697265625"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "p = params()\n",
+    "# f(x) = 0.55*x + 0.25\n",
+    "# Gradient: f'(0) = 0.55\n",
+    "# Pay attention that 0.55 is not a multiple of m/N = 0.25,\n",
+    "# so we expect to get a superposition of multiple states around the correct gradient.\n",
+    "p.set_function(p.linear, (0.55, 0.25))\n",
+    "p.unpack(globals())\n",
+    "\n",
+    "\n",
+    "@qfunc\n",
+    "def main(x: Output[QNum[n, SIGNED, 0]]):\n",
+    "    allocate(x)\n",
+    "    gradient(make_phase_oracle(f, l=l, m=m, N=N, x0=0), x)\n",
+    "\n",
+    "\n",
+    "qprog_linear_2 = synthesize(main)\n",
+    "df = sample(qprog_linear_2).sort_values(\"counts\", ascending=False)\n",
+    "analyze_results(df, p)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "36",
+   "metadata": {},
+   "source": [
+    "### Quadratic Function"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "37",
+   "metadata": {},
+   "source": [
+    "For non-linear functions, the interval $l$ must be chosen carefully. The algorithm requires the function to be approximately linear over the interval, which means $l$ must be sufficiently small. The next example demonstrates this dependency."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "38",
+   "metadata": {},
+   "source": [
+    "With appropriate selection of $l$, the function remains nearly linear over the sampling interval, yielding high success rates."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "39",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Submitting job to simulator\n",
+      "Job: https://platform.classiq.io/jobs/069c0180-ccdd-43f7-bbe3-15f43589d689\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Parsed probabilities: {1: 0.98095703125, 2: 0.00927734375, 0: 0.005859375, -1: 0.00146484375, 3: 0.0009765625, -2: 0.0009765625, -4: 0.00048828125}\n",
+      "The analytical gradient is: 0.25\n",
+      "The majority gradient is: 0.25\n",
+      "The majority result is correct\n",
+      "####################################################\n",
+      "Success rate: 98.10% (2009/2048 shots)\n",
+      "[\u001b[92m█████████████████████████████████████████████████\u001b[91m-\u001b[0m] 98.10%\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 1800x700 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# In this example we will use the quadratic function\n",
+    "p = params()\n",
+    "# f(x) = 0.6*x^2 + 0.25*x + 0.1\n",
+    "# Gradient: f'(x) = 1.2*x + 0.25, so f'(0) = 0.25\n",
+    "p.set_function(p.quadratic, (0.6, 0.25, 0.1))\n",
+    "# Setting l properly, ensuring we are in the linear regime of the function.\n",
+    "p.l = 0.1\n",
+    "p.unpack(globals())\n",
+    "\n",
+    "\n",
+    "@qfunc\n",
+    "def main(x: Output[QNum[n, SIGNED, 0]]):\n",
+    "    allocate(x)\n",
+    "    gradient(make_phase_oracle(f, l=l, m=m, N=N, x0=0), x)\n",
+    "\n",
+    "\n",
+    "qprog_quadratic = synthesize(main)\n",
+    "df = sample(qprog_quadratic).sort_values(\"counts\", ascending=False)\n",
+    "success_rate = analyze_results(df, p)\n",
+    "assert success_rate > 0.9, r\"The success rate should be above 90% for these parameters.\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "40",
+   "metadata": {},
+   "source": [
+    "Conversely, if $l$ is too large and the function deviates significantly from linearity, the success rate drops dramatically."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "41",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Submitting job to simulator\n",
+      "Job: https://platform.classiq.io/jobs/7beb5389-94f2-4c79-b189-c67aea24008c\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Parsed probabilities: {1: 0.2568359375, 2: 0.24169921875, 0: 0.23095703125, -1: 0.08935546875, 3: 0.087890625, -2: 0.0341796875, -4: 0.03369140625, -3: 0.025390625}\n",
+      "The analytical gradient is: 0.25\n",
+      "The majority gradient is: 0.25\n",
+      "The majority result is correct\n",
+      "####################################################\n",
+      "Success rate: 25.68% (526/2048 shots)\n",
+      "[\u001b[92m█████████████\u001b[91m-------------------------------------\u001b[0m] 25.68%\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 1800x700 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# In this example we will use the quadratic function\n",
+    "p = params()\n",
+    "# f(x) = 0.6*x^2 + 0.25*x + 0.1\n",
+    "# Gradient: f'(x) = 1.2*x + 0.25, so f'(0) = 0.25\n",
+    "p.set_function(p.quadratic, (0.6, 0.25, 0.1))\n",
+    "# Setting l to be too big, so we are outside of the linear regime of the function.\n",
+    "p.l = 1\n",
+    "p.unpack(globals())\n",
+    "\n",
+    "\n",
+    "@qfunc\n",
+    "def main(x: Output[QNum[n, SIGNED, 0]]):\n",
+    "    allocate(x)\n",
+    "    gradient(make_phase_oracle(f, l=l, m=m, N=N, x0=0), x)\n",
+    "\n",
+    "\n",
+    "qprog_quadratic_2 = synthesize(main)\n",
+    "df = sample(qprog_quadratic_2).sort_values(\"counts\", ascending=False)\n",
+    "success_rate = analyze_results(df, p)\n",
+    "assert success_rate < 0.5, r\"The success rate should be below 50% for these parameters.\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "42",
+   "metadata": {},
+   "source": [
+    "# Performance Analysis"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "43",
+   "metadata": {},
+   "source": [
+    "We can plot the success rate as a function of parameter choices. Consider a quadratic function $f(x)=0.6x^2+0.25x+0.1$ with $f'(0)=0.25$, and target accuracy $\\epsilon=0.2$.\n",
+    "\n",
+    "To determine valid parameter ranges, we need to use:\n",
+    "- $d=1$ (dimensionality)\n",
+    "- $D_2=1.2$ (second derivative bound)\n",
+    "- $\\nabla f_{\\max}$: With $l \\approx 0.1$, we have $\\nabla f \\in [0.19, 0.31]$, so $\\nabla f_{\\max}=0.31$\n",
+    "\n",
+    "From the theory, the parameters must satisfy (with $n=3$):\n",
+    "- $m\\geq 2 \\cdot 0.31 = 0.62$\n",
+    "- $m\\leq 2 \\cdot N \\cdot \\epsilon = 3.2$\n",
+    "- $l\\leq 0.28$\n",
+    "\n",
+    "In the next graph, we plot success rate versus $m$ with $l=0.1$. The valid range $[0.62, 3.2]$ is highlighted."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "44",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "m=0.05: Success rate = 0.00%\n",
+      "m=0.1: Success rate = 0.00%\n",
+      "m=0.33: Success rate = 0.68%\n",
+      "m=0.5: Success rate = 10.01%\n",
+      "m=0.67: Success rate = 93.31%\n",
+      "m=1.0: Success rate = 99.22%\n",
+      "m=1.33: Success rate = 79.64%\n",
+      "m=1.67: Success rate = 95.21%\n",
+      "m=2.0: Success rate = 98.58%\n",
+      "m=2.33: Success rate = 90.09%\n",
+      "m=2.67: Success rate = 77.73%\n",
+      "m=3.0: Success rate = 64.55%\n",
+      "m=3.33: Success rate = 56.35%\n"
+     ]
+    }
+   ],
+   "source": [
+    "_logger.setLevel(logging.WARNING)  # Disable the logging for the loop\n",
+    "\n",
+    "\n",
+    "def plot_success_rate_vs_m(m_values, function, function_params, l_val=0.1, n_val=3):\n",
+    "    \"\"\"\n",
+    "    Iterate over different m values and plot success rate as a function of m.\n",
+    "\n",
+    "    Args:\n",
+    "        m_values: list of m values to test\n",
+    "        function: function name (\"linear\" / \"quadratic\") or callable\n",
+    "        function_params: parameters for the function\n",
+    "        l_val: l parameter (default 0.1)\n",
+    "        n_val: n parameter (default 3)\n",
+    "    \"\"\"\n",
+    "    success_rates = []\n",
+    "\n",
+    "    for m_val in m_values:\n",
+    "        p = params()\n",
+    "        p.m = m_val\n",
+    "        p.l = l_val\n",
+    "        p.n = n_val\n",
+    "        p.set_function(function, function_params)\n",
+    "        p.unpack(globals())\n",
+    "\n",
+    "        @qfunc\n",
+    "        def main(x: Output[QNum[n, SIGNED, 0]]):\n",
+    "            allocate(x)\n",
+    "            gradient(make_phase_oracle(f, l=l, m=m, N=N, x0=0), x)\n",
+    "\n",
+    "        qprog = synthesize(main)\n",
+    "        df = sample(qprog).sort_values(\"counts\", ascending=False)\n",
+    "        analytic_grad = p.analytical_gradient(0)\n",
+    "        epsilon = 0.2\n",
+    "        success_rate, _, _ = compute_success_rate(\n",
+    "            df,\n",
+    "            analytic_derivatives={\"x\": analytic_grad},\n",
+    "            p=p,\n",
+    "            tolerance=epsilon,\n",
+    "        )\n",
+    "        success_rates.append(success_rate)\n",
+    "        print(f\"m={m_val}: Success rate = {success_rate:.2%}\")\n",
+    "\n",
+    "    # Plot\n",
+    "    plt.figure(figsize=(10, 6))\n",
+    "    plt.plot(m_values, success_rates, \"o-\", linewidth=2, markersize=8)\n",
+    "    plt.xlabel(\"m parameter\", fontsize=12)\n",
+    "    plt.ylabel(\"Success Rate\", fontsize=12)\n",
+    "    plt.title(\"Success Rate vs m Parameter\", fontsize=14)\n",
+    "    plt.grid(True, alpha=0.3)\n",
+    "    plt.ylim([0, 1.05])\n",
+    "    ax = plt.gca()\n",
+    "    theoretical_range = (0.62, 3.2)\n",
+    "    ax.axvspan(theoretical_range[0], theoretical_range[1], color=\"green\", alpha=0.15)\n",
+    "    plt.show()\n",
+    "    return success_rates\n",
+    "\n",
+    "\n",
+    "# Example usage:\n",
+    "m_values = [\n",
+    "    0.05,\n",
+    "    0.1,\n",
+    "    0.33,\n",
+    "    0.5,\n",
+    "    0.67,\n",
+    "    1.0,\n",
+    "    1.33,\n",
+    "    1.67,\n",
+    "    2.0,\n",
+    "    2.33,\n",
+    "    2.67,\n",
+    "    3.0,\n",
+    "    3.33,\n",
+    "    3.67,\n",
+    "    4.0,\n",
+    "    6.0,\n",
+    "    10.0,\n",
+    "]\n",
+    "results = plot_success_rate_vs_m(m_values, \"quadratic\", (0.6, 0.25, 0.1))\n",
+    "_logger.setLevel(logging.INFO)  # TEMP"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "45",
+   "metadata": {},
+   "source": [
+    "Next, we plot success rate versus $l$ with $m=2$. The valid bound $l\\leq 0.28$ is highlighted."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "46",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from classiq.execution.functions.util._logging import _logger\n",
+    "\n",
+    "_logger.setLevel(logging.WARNING)  # Disable the logging for the loop\n",
+    "\n",
+    "\n",
+    "def plot_success_rate_vs_l(l_values, function, function_params, m_val=2.0, n_val=3):\n",
+    "    \"\"\"\n",
+    "    Iterate over different l values and plot success rate as a function of l.\n",
+    "\n",
+    "    Args:\n",
+    "        l_values: list of l values to test\n",
+    "        function: function name (\"linear\" / \"quadratic\") or callable\n",
+    "        function_params: parameters for the function\n",
+    "        m_val: m parameter (default 1.0)\n",
+    "        n_val: n parameter (default 3)\n",
+    "    \"\"\"\n",
+    "    success_rates = []\n",
+    "\n",
+    "    for l_curr in l_values:\n",
+    "        p = params()\n",
+    "        p.m = m_val\n",
+    "        p.l = l_curr\n",
+    "        p.n = n_val\n",
+    "        p.set_function(function, function_params)\n",
+    "        p.unpack(globals())\n",
+    "\n",
+    "        @qfunc\n",
+    "        def main(x: Output[QNum[n, SIGNED, 0]]):\n",
+    "            allocate(x)\n",
+    "            gradient(make_phase_oracle(f, l=l, m=m, N=N, x0=0), x)\n",
+    "\n",
+    "        qprog = synthesize(main)\n",
+    "        df = sample(qprog).sort_values(\"counts\", ascending=False)\n",
+    "        analytic_grad = p.analytical_gradient(0)\n",
+    "        success_rate, _, _ = compute_success_rate(\n",
+    "            df, analytic_derivatives={\"x\": analytic_grad}, p=p\n",
+    "        )\n",
+    "        success_rates.append(success_rate)\n",
+    "        print(f\"l={l_curr}: Success rate = {success_rate:.2%}\")\n",
+    "\n",
+    "    plt.figure(figsize=(10, 6))\n",
+    "    plt.plot(l_values, success_rates, \"o-\", linewidth=2, markersize=8)\n",
+    "    plt.xlabel(\"l parameter\", fontsize=12)\n",
+    "    plt.ylabel(\"Success Rate\", fontsize=12)\n",
+    "    plt.title(\"Success Rate vs l Parameter\", fontsize=14)\n",
+    "    plt.grid(True, alpha=0.3)\n",
+    "    plt.ylim([0, 1.05])\n",
+    "    ax = plt.gca()\n",
+    "    theoretical_range = (0, 0.28)\n",
+    "    ax.axvspan(theoretical_range[0], theoretical_range[1], color=\"green\", alpha=0.15)\n",
+    "\n",
+    "    plt.show()\n",
+    "\n",
+    "    return success_rates\n",
+    "\n",
+    "\n",
+    "# Example usage:\n",
+    "l_values = [0.1, 0.2, 0.3, 0.4, 0.5, 1, 1.5, 2.0, 3.0]\n",
+    "results_l = plot_success_rate_vs_l(\n",
+    "    l_values, \"quadratic\", (0.6, 0.25, 0.1), m_val=1.0, n_val=3\n",
+    ")\n",
+    "_logger.setLevel(logging.INFO)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "47",
+   "metadata": {},
+   "source": [
+    "# Multi-Dimensional Examples"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "48",
+   "metadata": {},
+   "source": [
+    "The algorithm extends to multiple dimensions. In this example we will demonstrate a general quadratic coupled function:\n",
+    "\n",
+    "$$f(x,y)=ax^2+by^2+cxy+dx+ey+f$$"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "49",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "p = params()\n",
+    "p.l = 0.1\n",
+    "p.unpack(globals())\n",
+    "\n",
+    "\n",
+    "def f(x, y):\n",
+    "    a, b, c, d, e, f = 0.6, -0.4, 0.25, 0.25, -0.5, 0.1\n",
+    "    return a * x**2 + b * y**2 + c * x * y + d * x + e * y + f\n",
+    "\n",
+    "\n",
+    "def f_normalized(x, y):\n",
+    "    val = f(l / N * x, l / N * y)\n",
+    "    val *= N / (l * m)\n",
+    "    return val\n",
+    "\n",
+    "\n",
+    "@qfunc\n",
+    "def main(coords: Output[QArray[QNum[n, SIGNED, 0], 2]]):\n",
+    "    allocate(coords)\n",
+    "    gradient_nd(make_phase_oracle(f, l=l, m=m, N=N, x0=0, d=2), coords)\n",
+    "\n",
+    "\n",
+    "qprog_2d = synthesize(main)\n",
+    "df = sample(qprog_2d).sort_values(\"counts\", ascending=False)\n",
+    "print(df)\n",
+    "\n",
+    "gradient_analytical = [0.25, -0.5]\n",
+    "majority_normalized = df.iloc[0][\"coords\"]\n",
+    "gradient_measured = state_to_gradient(majority_normalized, p)\n",
+    "\n",
+    "print(f\"Analytical gradient: (dx, dy) = ({gradient_analytical})\")\n",
+    "print(f\"Majority gradient:   (dx, dy) = ({gradient_measured})\")\n",
+    "\n",
+    "success_rate, success_shots, total_shots = compute_success_rate(\n",
+    "    df, analytic_derivatives={\"coords\": gradient_analytical}, p=p\n",
+    ")\n",
+    "print(f\"Success rate: {success_rate:.2%} ({success_shots}/{total_shots} shots)\")\n",
+    "show_bar(success_rate)\n",
+    "assert success_rate > 0.9, r\"The success rate should be above 90% for these parameters.\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "50",
+   "metadata": {},
+   "source": [
+    "# Summary and Discussion\n",
+    "\n",
+    "This notebook demonstrated Jordan's quantum gradient estimation algorithm, which estimates $\\nabla f$ at the origin using a **single query** to $f$, compared to the classical lower bound of $d+1$ queries.\n",
+    "\n",
+    "## Algorithm\n",
+    "\n",
+    "1. Prepare a uniform superposition over $N = 2^n$ sample points using Hadamard gates.\n",
+    "2. Encode $f$ as phases, either via phase kickback from a state oracle or directly using a phase oracle.\n",
+    "3. Apply the inverse QFT to map the phase encoding onto a measurable computational basis state containing $\\nabla f$.\n",
+    "\n",
+    "## Parameter Selection\n",
+    "\n",
+    "| Parameter | Role | Constraint |\n",
+    "|---|---|---|\n",
+    "| $l$ | Sampling interval | $l \\leq \\frac{2\\sqrt{3}\\epsilon}{D_2\\sqrt{d}}$ — keeps $f$ approximately linear |\n",
+    "| $m$ | Gradient range | $2\\nabla f_{\\max} \\leq m \\leq 2\\cdot2^n\\epsilon$ |\n",
+    "| $n$ | Qubits / resolution | $n \\geq \\log_2(\\nabla f_{\\max} / \\epsilon)$ |\n",
+    "\n",
+    "## Potential Use Cases\n",
+    "\n",
+    "The $d+1 \\to 1$ query reduction is most valuable when $d$ is large and each function evaluation is expensive.\n",
+    "\n",
+    "Potential applications include: Optimization, Root-finding, Functional minimization and PDEs and more.\n",
+    "\n",
+    "## Limitation: Higher-Order Derivatives\n",
+    "\n",
+    "The algorithm **cannot be applied recursively** to compute second or higher-order derivatives of $f$.\n",
+    "\n",
+    "The method requires a quantum oracle that evaluates $f(x)$ coherently as a phase across all $x$ simultaneously. After running the circuit and measuring, the output is a single classical value $\\nabla f(0)$ — not a new quantum oracle for $\\nabla f(x)$ at arbitrary points $x$. Estimating $\\nabla^2 f$ by this approach would require such an oracle for $\\nabla f$, which the algorithm does not construct.\n",
+    "\n",
+    "In classical finite differences, second derivatives are obtained by calling $f$ at multiple points and differencing the first-derivative estimates. Replicating this quantumly would require separate oracle queries for each evaluation point, reducing the query complexity back to the classical $O(d^2)$ regime and eliminating the quantum advantage entirely."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "51",
+   "metadata": {},
+   "source": [
+    "# Appendices\n",
+    "## Appendix 1 - Phase Kickback\n",
+    "\n",
+    "We start with the state $|\\delta\\rangle|0\\rangle$ where $|\\delta\\rangle$ is a superposition created by the Hadamard gate for all the coordinates, hence:\n",
+    "$$\\sum_{\\delta_1=0}^{N-1}\\cdots\\sum_{\\delta_d=0}^{N-1}|\\delta_1\\rangle\\cdots|\\delta_d\\rangle$$\n",
+    "For convenience we will look at the $d=1$ case, but the same procedure can be used for bigger $d$.\n",
+    "\n",
+    "The ancilla starts at the ground state $|a\\rangle=|0\\rangle$.\n",
+    "We first apply bitwise X gate to create the state $|111\\cdots1\\rangle$, which in the signed fractional QNum representation corresponds to the value $-1/N_0$.\n",
+    "\n",
+    "Next, we apply QFT on the ancilla. Using the fact that $e^{i2\\pi a} = 1$ for integer $a$, the QFT of $|N_0-1\\rangle$ simplifies to:\n",
+    "$$ \\frac{1}{\\sqrt{N_0}}\\sum_{a=0}^{N_0-1}e^{i2\\pi a(N_0-1)/N_0}|a\\rangle = \\frac{1}{\\sqrt{N_0}}\\sum_{a=0}^{N_0-1}e^{-i2\\pi a/N_0}|a\\rangle $$\n",
+    "\n",
+    "The full state after both Hadamard (on coordinates) and QFT (on ancilla) is:\n",
+    "$$\\frac{1}{\\sqrt{N \\cdot N_0}}\\sum_{\\delta=0}^{N-1}\\sum_{a=0}^{N_0-1} e^{-i2\\pi a/N_0}\\,|\\delta\\rangle|a\\rangle$$\n",
+    "\n",
+    "As discussed before, we assume that we have an oracle $f$ that applies the function on the state $|x\\rangle \\rightarrow |f(x)\\rangle$.\n",
+    "We apply this function and add the normalized result $f_\\mathrm{norm}(\\delta) = \\frac{N}{ml}f\\!\\left(\\frac{l}{N}\\delta\\right)$ to the ancilla register, mapping $|a\\rangle \\to |a + f_\\mathrm{norm}(\\delta)\\rangle$:\n",
+    "\n",
+    "$$\\frac{1}{\\sqrt{N \\cdot N_0}}\\sum_{\\delta,\\,a} e^{-i2\\pi a/N_0}\\,|\\delta\\rangle\\,|a + f_\\mathrm{norm}(\\delta)\\rangle$$\n",
+    "\n",
+    "Substituting $a' = a + f_\\mathrm{norm}(\\delta)$, i.e. $a = a' - f_\\mathrm{norm}(\\delta)$:\n",
+    "\n",
+    "$$\\frac{1}{\\sqrt{N \\cdot N_0}}\\sum_{\\delta,\\,a'} e^{-i2\\pi (a' - f_\\mathrm{norm}(\\delta))/N_0}\\,|\\delta\\rangle\\,|a'\\rangle$$\n",
+    "\n",
+    "Separating the phase into two factors:\n",
+    "\n",
+    "$$= \\frac{1}{\\sqrt{N}}\\sum_{\\delta=0}^{N-1} e^{i2\\pi f_\\mathrm{norm}(\\delta)/N_0}\\,|\\delta\\rangle \\;\\otimes\\; \\underbrace{\\frac{1}{\\sqrt{N_0}}\\sum_{a'=0}^{N_0-1} e^{-i2\\pi a'/N_0}\\,|a'\\rangle}_{\\text{original ancilla state}}$$\n",
+    "\n",
+    "The ancilla returns to its pre-oracle state and the function value has been \"kicked back\" as a phase onto the coordinate register.\n",
+    "Since the ancilla is a fractional QNum with $n_0$ fractional bits, dividing by $N_0$ converts from the integer index back to the fractional value, so $f_\\mathrm{norm}(\\delta)/N_0 \\to f_\\mathrm{norm}(\\delta)$ in the fractional encoding.\n",
+    "The coordinate register is therefore left in the state:\n",
+    "\n",
+    "$$\\sum_{\\delta=0}^{N-1} e^{i2\\pi f_\\mathrm{norm}(\\delta)}\\,|\\delta\\rangle = \\sum_{\\delta=0}^{N-1} e^{i2\\pi \\frac{N}{ml} f\\!\\left(\\frac{l}{N}\\delta\\right)}\\,|\\delta\\rangle$$\n",
+    "\n",
+    "This is exactly the desired phase state from Step 2 of the algorithm, and the rest of the algorithm proceeds identically to the Direct Phase approach.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "52",
+   "metadata": {},
+   "source": [
+    "# References"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "53",
+   "metadata": {},
+   "source": [
+    "<a id='original_paper'>[1]</a>: [Stephen P. Jordan. Fast Quantum Algorithm for Numerical Gradient Estimation. Physical Review Letters 95 (2005)](https://www.researchgate.net/publication/7669221_Fast_Quantum_Algorithm_for_Numerical_Gradient_Estimation)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "classiq",
+   "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": "3.12.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 9
+}
diff --git a/algorithms/gradient_estimation/gradient_estimation.metadata.json b/algorithms/gradient_estimation/gradient_estimation.metadata.json
new file mode 100644
index 000000000..be6f2f983
--- /dev/null
+++ b/algorithms/gradient_estimation/gradient_estimation.metadata.json
@@ -0,0 +1,10 @@
+{
+  "title": "Gradient Estimation",
+  "subtitle": "Initialization",
+  "description": "Initialization",
+  "friendly_name": "Gradient Estimation",
+  "vertical_tags": [],
+  "problem_domain_tags": [],
+  "qmod_type": [],
+  "level": []
+}
diff --git a/algorithms/gradient_estimation/gradient_estimation_helpers.py b/algorithms/gradient_estimation/gradient_estimation_helpers.py
new file mode 100644
index 000000000..a53df9ae0
--- /dev/null
+++ b/algorithms/gradient_estimation/gradient_estimation_helpers.py
@@ -0,0 +1,375 @@
+from classiq import *
+from typing import List
+import numpy as np
+import pandas as pd
+from dataclasses import dataclass
+from matplotlib import pyplot as plt
+from classiq.qmod.symbolic import pi
+import logging
+from typing import Callable
+
+# %matplotlib widget
+
+# Module-level globals populated by params.unpack(globals()).
+# Defaults match params class defaults.
+l = 0.5
+m = 2
+n = 3
+n0 = 3
+N = 2**n
+N0 = 2**n0
+f = None
+f_prams = None
+f_normalized = None
+
+
+@dataclass
+class params:
+    # Algorithm parameters
+    l = 0.5
+    m = 2
+    n = 3
+    n0 = 3
+
+    @property
+    def N(self):
+        return 2**self.n
+
+    @property
+    def N0(self):
+        return 2**self.n0
+
+    # Function parameters
+    f = None
+    f_prams = None
+    f_name = None
+
+    def set_function(self, f, f_prams):
+        self.f_prams = f_prams
+
+        # Allow passing by name, e.g. "linear" or "quadratic"
+        if isinstance(f, str):
+            self.f_name = f
+            self.f = getattr(self, f)
+            return
+
+        # If a method from another params instance is passed,
+        # re-bind it to this instance to avoid mixed state.
+        if hasattr(f, "__func__"):
+            self.f = f.__func__.__get__(self, self.__class__)
+        else:
+            self.f = f
+
+        self.f_name = getattr(self.f, "__name__", None)
+
+    # Generated functions
+    def f_normalized(self, x):
+        val = self.f(self.l / self.N * x)
+        val *= self.N / (self.l * self.m)
+        return val
+
+    def analytical_gradient(self, x):
+        if self.f_name == "linear":
+            return self.f_prams[0]
+        elif self.f_name == "quadratic":
+            return 2 * self.f_prams[0] * x + self.f_prams[1]
+        else:
+            raise NotImplementedError(
+                "Analytical gradient not implemented for this function."
+            )
+
+    # A few example functions
+    def linear(self, x):
+        return self.f_prams[0] * x + self.f_prams[1]
+
+    def quadratic(self, x):
+        return self.f_prams[0] * x**2 + self.f_prams[1] * x + self.f_prams[2]
+
+    # Unpack function, to be used in the notebook.
+    # Pass globals() from the notebook cell so variables are injected there too:
+    #   p.unpack(globals())
+    def unpack(self, namespace=None):
+        import sys
+
+        vals = dict(
+            l=self.l,
+            m=self.m,
+            n=self.n,
+            n0=self.n0,
+            N=self.N,
+            N0=self.N0,
+            f=self.f,
+            f_prams=self.f_prams,
+            f_normalized=self.f_normalized,
+        )
+        # Update this module's globals so helper functions (state_to_gradient, etc.) work
+        vars(sys.modules[self.__class__.__module__]).update(vals)
+        # Update the notebook's globals if provided
+        if namespace is not None:
+            namespace.update(vals)
+
+
+# ****** Simulation ******
+def run_statevector_simulation(
+    qfunc_to_run, print_circuit_info=False, filter_ancilla=False, show_circuit=False
+):
+    # Run a statevector simulator
+    qprog = synthesize(qfunc_to_run)
+    if show_circuit:
+        show(qprog)
+    if print_circuit_info:
+        print("Circuit Width:", qprog.data.width)
+        print("Circuit Depth:", qprog.transpiled_circuit.depth)
+        print("Gate Counts:", qprog.transpiled_circuit.count_ops)
+
+    if filter_ancilla:
+        df = calculate_state_vector(qprog, filters={"ancilla": 0})
+    else:
+        df = calculate_state_vector(qprog)
+    df.sort_values(by="x", inplace=True)
+
+    return df
+
+
+# ****** Result Processing ******
+def state_to_gradient(value, p):
+    if type(value) == list:
+        result = []
+        for v in value:
+            result.append(state_to_gradient(v, p))
+        return result
+    return value / (p.N / p.m)
+
+
+def simplify_df(df, unwrap=True):
+    # Get the phase from the df
+    phases = np.angle(df["amplitude"]).astype(float)
+    phases_over_2pi = phases / (2 * np.pi)
+    f_classical = f_normalized(df["x"])
+    simplified_df = pd.DataFrame(
+        {"f_classical": f_classical, "phase_over_2pi": phases_over_2pi.round(5)}
+    )
+    simplified_df.index = df["x"]
+    simplified_df.sort_index(inplace=True)
+
+    # Unwrap the phase if requested
+    if unwrap:
+        # Unwrap the phase
+        simplified_df["phase_over_2pi"] = np.unwrap(
+            simplified_df["phase_over_2pi"], period=1
+        )
+        # Get rid of the global phase
+        simplified_df["phase_over_2pi"] -= simplified_df["phase_over_2pi"].iloc[N // 2]
+        simplified_df["f_classical"] -= simplified_df["f_classical"].iloc[N // 2]
+
+    return simplified_df
+
+
+def compute_success_rate(df, analytic_derivatives, p, tolerance=None):
+    total_shots = int(df["counts"].sum())
+    if total_shots == 0:
+        return 0.0, 0, 0
+    if tolerance is None:
+        tolerance = 0.5 * p.m / p.N
+
+    success_shots = 0
+
+    for _, row in df.iterrows():
+        correct = True
+        for name, analytic_val in analytic_derivatives.items():
+            measured_val = row[name]
+
+            if isinstance(analytic_val, list):
+                measured_val = state_to_gradient(measured_val, p)
+                if measured_val is None or any(
+                    abs(m - a) >= tolerance for m, a in zip(measured_val, analytic_val)
+                ):
+                    correct = False
+                    break
+            else:
+                measured_val = state_to_gradient(measured_val, p)
+                if (
+                    measured_val is None
+                    or abs(measured_val - analytic_val) >= tolerance
+                ):
+                    correct = False
+                    break
+
+        if correct:
+            success_shots += int(row["counts"])
+
+    success_rate = success_shots / total_shots
+    return success_rate, success_shots, total_shots
+
+
+def analyze_results(df, p):
+    analytical_gradient = p.analytical_gradient(0)
+
+    print("Parsed probabilities:", df.set_index("x").to_dict()["probability"])
+    print(f"The analytical gradient is: {analytical_gradient}")
+    majority_state = dict(df.iloc[0])
+    majority_gradient = state_to_gradient(majority_state.get("x"), p)
+    print(f"The majority gradient is: {majority_gradient}")
+
+    resolution = m / N
+    is_correct = abs(majority_gradient - analytical_gradient) < resolution / 2
+    print(f"The majority result is", "correct" if is_correct else "incorrect")
+    print("####################################################")
+
+    success_rate, success_shots, total_shots = compute_success_rate(
+        df, analytic_derivatives={"x": analytical_gradient}, p=p
+    )
+    print(f"Success rate: {success_rate:.2%} ({success_shots}/{total_shots} shots)")
+    show_bar(success_rate)
+
+    fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(18, 7))
+
+    x_array = np.arange(-N // 2, N // 2)
+    f_classical = f_normalized(x_array)
+    f_classical -= f_classical[N // 2]
+
+    plt.sca(ax1)
+    plot_classical()
+    ax1.plot(x_array, f_classical, "o", label="Theoretical values", markersize=8)
+    xmin, xmax = -N, N
+    ymin, ymax = -N // 2, N // 2
+    ax1.set_xlim(xmin, xmax)
+    ax1.set_ylim(ymin, ymax)
+    ax1.vlines(-N // 2, ymin, ymax, colors="lightgray", linestyles="dashed")
+    ax1.vlines(N // 2 - 1, ymin, ymax, colors="lightgray", linestyles="dashed")
+    ax1.hlines(-N / 4 + 0.5, xmin, xmax, colors="lightgray", linestyles="dashed")
+    ax1.hlines(N / 4, xmin, xmax, colors="lightgray", linestyles="dashed")
+    ax1.legend(fontsize=12)
+    ax1.set_title("Theoretical Phases", fontsize=14, fontweight="bold")
+    ax1.tick_params(labelsize=11)
+    ax1.set_xlabel("x (index)", fontsize=12)
+    ax1.set_ylabel("f (normalized)", fontsize=12)
+
+    plt.sca(ax2)
+    percentage = df["counts"] / df["counts"].sum() * 100
+    ax2.bar(df["x"], percentage, color="lightblue", label="Measurement counts")
+    ax2.set_xlabel("x (index)", fontsize=12)
+    ax2.set_ylabel("Percentage of shots (%)", fontsize=12)
+    ax2.set_xlim(-N // 2 - 1, N // 2)
+    ax2.set_ylim(0, 100)
+    ax2.set_title("Measurement Histogram", fontsize=14, fontweight="bold")
+    ax2.tick_params(labelsize=11)
+    if analytical_gradient is not None:
+        x_analytic = analytical_gradient * (N / m)
+        ax2.axvline(
+            x=x_analytic,
+            color="green",
+            linestyle="dashed",
+            label="Analytical gradient",
+            linewidth=2,
+        )
+    ax2.legend(fontsize=12)
+
+    plt.tight_layout()
+    plt.show()
+    return success_rate
+
+
+# ****** Plotting ******
+def plot_classical():
+    x_values = np.linspace(-N, N, 1000)
+    f_values = f_normalized(x_values)
+    f_values -= f_normalized(0)
+    plt.plot(x_values, f_values, color="lightgray", label="Original function")
+    ax = plt.gca()
+    ax.set_xticks(np.arange(-N // 2, N // 2))
+    ax.set_xticklabels([str(i) for i in range(-N // 2, N // 2)])
+
+    # Primary-axis labels (normalized coordinates)
+    ax.set_xlabel("x (index)")
+    ax.set_ylabel("f (normalized)")
+
+    # Secondary X axis: signed quantum index -> real x  (x_real = x * l/N)
+    x_to_real = lambda x: x * (l / N)
+    real_to_x = lambda xr: xr * (N / l)
+    secax_x = ax.secondary_xaxis("top", functions=(x_to_real, real_to_x), color="blue")
+    secax_x.set_xlabel("x (real)")
+
+    # Secondary Y axis: normalized f -> unnormalized f
+    f_to_real = lambda y: (y + f_normalized(0)) * m * l / N
+    real_to_f = lambda yr: yr / m / l * N - f_normalized(0)
+    secax_y = ax.secondary_yaxis(
+        "right", functions=(f_to_real, real_to_f), color="blue"
+    )
+    secax_y.set_ylabel("f (real, unnormalized)")
+
+
+def style_subplots(ax1, ax2):
+    for ax in [ax1, ax2]:
+        ax.set_xlabel("x (index)", fontsize=12)
+        ax.tick_params(labelsize=11)
+
+
+def plot_theoretical(show=True):
+    x_array = np.arange(-N // 2, N // 2)
+
+    f_classical = f_normalized(x_array)
+    f_classical -= f_classical[N // 2]  # index N//2 in [-N/2..N/2-1] is x=0
+
+    if show:
+        plt.figure()
+    plot_classical()
+    plt.plot(x_array, f_classical, "o", label="Theoretical values")
+    xmin, xmax = -N, N
+    ymin, ymax = -N // 2, N // 2
+    plt.xlim(xmin, xmax)
+    plt.ylim(ymin, ymax)
+    plt.vlines(-N // 2, ymin, ymax, colors="lightgray", linestyles="dashed")
+    plt.vlines(N // 2 - 1, ymin, ymax, colors="lightgray", linestyles="dashed")
+    plt.hlines(-N / 4 + 0.5, xmin, xmax, colors="lightgray", linestyles="dashed")
+    plt.hlines(N / 4, xmin, xmax, colors="lightgray", linestyles="dashed")
+    plt.legend()
+    if show:
+        plt.show()
+
+
+def plot_histogram(df, analytical_gradient=None, show=True):
+    plt.figure()
+    percentage = df["counts"] / df["counts"].sum() * 100
+    plt.bar(df["x"], percentage, color="lightblue", label="Measurement counts")
+    plt.xlabel("x (index)")
+    plt.ylabel("Percentage of shots (%)")
+    plt.xlim(-N // 2 - 1, N // 2)
+    plt.ylim(0, 100)
+    plt.title("Measurement Histogram")
+    plt.legend()
+    # plot the analytical gradient as a vertical line if provided
+    if analytical_gradient is not None:
+        x_analytic = analytical_gradient * (N / m)
+        plt.axvline(
+            x=x_analytic, color="green", linestyle="dashed", label="Analytical gradient"
+        )
+        plt.legend()
+    if show:
+        plt.show()
+
+
+def plot_simplified_df(simplified_df, show=True):
+    plt.figure()
+    plot_theoretical(show=False)
+    plt.plot(
+        simplified_df.index,
+        simplified_df["phase_over_2pi"],
+        ".",
+        label="Measured phases",
+    )
+    plt.legend()
+    if show:
+        plt.show()
+
+
+def show_bar(success_rate):
+    bar_length = 50
+    filled = int(round(bar_length * success_rate))
+    GREEN = "\033[92m"
+    RED = "\033[91m"
+    RESET = "\033[0m"
+
+    bar = f"{GREEN}{'█' * filled}{RED}{'-' * (bar_length - filled)}{RESET}"
+
+    print(f"[{bar}] {success_rate * 100:.2f}%")
diff --git a/algorithms/gradient_estimation/quantum_circuit.png b/algorithms/gradient_estimation/quantum_circuit.png
new file mode 100644
index 000000000..6c7dbc457
Binary files /dev/null and b/algorithms/gradient_estimation/quantum_circuit.png differ
diff --git a/algorithms/gradient_estimation/step_2.png b/algorithms/gradient_estimation/step_2.png
new file mode 100644
index 000000000..a0869baf7
Binary files /dev/null and b/algorithms/gradient_estimation/step_2.png differ
diff --git a/algorithms/gradient_estimation/step_3.png b/algorithms/gradient_estimation/step_3.png
new file mode 100644
index 000000000..8af0b6857
Binary files /dev/null and b/algorithms/gradient_estimation/step_3.png differ
diff --git a/tests/notebooks/test_gradient_estimation.py b/tests/notebooks/test_gradient_estimation.py
new file mode 100644
index 000000000..60a831c09
--- /dev/null
+++ b/tests/notebooks/test_gradient_estimation.py
@@ -0,0 +1,54 @@
+from tests.utils_for_testbook import (
+    validate_quantum_program_size,
+    wrap_testbook,
+)
+from testbook.client import TestbookNotebookClient
+
+
+@wrap_testbook("gradient_estimation", timeout_seconds=200)
+def test_notebook(tb: TestbookNotebookClient) -> None:
+    # test quantum programs
+    validate_quantum_program_size(
+        tb.ref_pydantic("qprog_ancilla"),
+        expected_width=16,  # Expected 8
+        expected_depth=63,  # Expected 42
+        expected_cx_count=41,  # Expected 27
+    )
+
+    validate_quantum_program_size(
+        tb.ref_pydantic("qprog_linear"),
+        expected_width=6,  # Expected 3
+        expected_depth=23,  # Expected 15
+        expected_cx_count=18,  # Expected 9
+    )
+
+    validate_quantum_program_size(
+        tb.ref_pydantic("qprog_linear_2"),
+        expected_width=6,  # Expected 3
+        expected_depth=23,  # Expected 15
+        expected_cx_count=18,  # Expected 9
+    )
+
+    validate_quantum_program_size(
+        tb.ref_pydantic("qprog_quadratic"),
+        expected_width=6,  # Expected 3
+        expected_depth=35,  # Expected 23
+        expected_cx_count=23,  # Expected 15
+    )
+
+    validate_quantum_program_size(
+        tb.ref_pydantic("qprog_quadratic_2"),
+        expected_width=6,  # Expected 3
+        expected_depth=36,  # Expected 24
+        expected_cx_count=23,  # Expected 15
+    )
+
+    validate_quantum_program_size(
+        tb.ref_pydantic("qprog_2d"),
+        expected_width=12,  # Expected 6
+        expected_depth=68,  # Expected 45
+        expected_cx_count=72,  # Expected 48
+    )
+
+    # test notebook content
+    pass  # Todo