algorithms/hamiltonian_simulation/hamiltonian_simulation_with_block_encoding/hamiltonian_simulation_with_block_encoding.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "0",
   "metadata": {},
   "source": [
    "# Hamiltonian Simulation with Quantum Signal Processing and Qubitization"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1",
   "metadata": {},
   "source": [
    "> **Simulating physical and chemical systems** was among the original motivations for quantum computing, as first envisioned by Richard Feynman in 1982, and remains one of its most impactful applications. Beyond its foundational goal, this quantum routine now serves as a key subroutine in many higher-level quantum algorithms, such as Quantum Linear Systems Solvers [(HHL)](https://github.com/Classiq/classiq-library/blob/main/algorithms/quantum_linear_solvers/hhl/hhl.ipynb), and applications such as [Quantum Gibbs State Sampling](https://github.com/Classiq/classiq-library/blob/main/algorithms/quantum_state_preparation/gibbs/quantum_thermal_state_preparation.ipynb).\n",
    ">\n",
    "> Time-independent Hamiltonian simulation refers to the task of approximately implementing the unitary evolution operator $e^{-iHt}$ for a Hermitian matrix $H$. When access to the Hamiltonian is provided via block-encoding, its time evolution can be realized by applying an appropriate polynomial transformation of the encoded operator, within a desired precision $\\epsilon$. This polynomial transformation can be achieved via **Quantum Signal Value Transform (QSVT)** [\\[1\\]](#ref-grand), **Generalized QSP (GQSP)** [\\[2\\]](#ref-gqsp), and a direct block encoding of Chebyshev Polynomials [\\[3\\]](#ref-bck) (referred to as the **Qubitization** technique hereafter). These methods represent the most powerful family of known algorithms for simulating the dynamics of quantum systems whose Hamiltonians can be efficiently block-encoded, having optimal scaling in the time $t$ and error $\\epsilon$.\n",
    ">\n",
    "> Qubitization provides a direct approach that avoids classical preprocessing but generally requires more qubits, may involve amplitude amplification, and includes controlled operations over the block-encoding unitary. QSVT, in contrast, omits such controlled operations, uses two auxiliary qubits, and requires a classical preprocessing stage to determine the signal-processing angles. The GQSP method, like Qubitization, employs controlled block-encoding calls and, like QSVT, relies on classical angle computation, but it requires only one auxiliary qubit and eliminates the need for amplification.\n",
    ">\n",
    "> - **Input:**\n",
    ">   - A Hermitian operator (Hamiltonian) $H$, given through an oracle that implements a block-encoding of $H/\\alpha$, where $\\alpha \\ge ||H||$ and $||\\cdot||$ is the spectral norm.\n",
    ">   - The evolution time $t$.\n",
    ">   - The target simulation error $\\epsilon$.\n",
    ">\n",
    "> - **Output:** A unitary $U$ that approximates the time evolution $e^{-iHt}$, such that $||U - e^{-iHt}|| < \\epsilon$ ($||\\cdot||$ is the spectral norm).\n",
    ">\n",
    "> **Complexity:** All three methods require $O\\!\\left(\\alpha t + \\frac{\\log \\epsilon^{-1}}{\\log \\!\\left(e + \\log(\\epsilon^{-1}) / \\alpha t \\right)}\\right)$ calls to the block-encoding unitary $U_H$. The methods differ by their classical preprocessing routines, depth, and number of auxiliary qubits.\n",
    ">\n",
    "> ---\n",
    ">\n",
    "> **Keywords:** Hamiltonian Simulation, Block Encoding, Quantum Singular Value Transformation (QSVT), Qubitization, Generalized Quantum Signal Processing, Oracle/Query complexity.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2",
   "metadata": {},
   "source": [
    "A block-encoded Hamiltonian refers to its embedding within a larger unitary matrix. \n",
    "\n",
    "**Definition**: A $(s, m, \\epsilon)$-encoding of a $2^n\\times 2^n$ matrix $H$ refers to completing it into a $2^{n+m}\\times 2^{n+m}$ unitary matrix $U_{(s,m,\\epsilon)-H}$:\n",
    "$$\n",
    "U_{(s,m,\\epsilon)-H} = \\begin{pmatrix}\n",
    "H/s & * \\\\\n",
    "* & *\n",
    "\\end{pmatrix},\n",
    "$$\n",
    "with some functional error $\\left|\\left|\\left(U_{(s,m,\\epsilon)-H}\\right)_{0:2^n-1,0:2^n-1}-H/s \\right|\\right|\\leq \\epsilon$. The idea is that typically we would like to apply operations on non-unitary matrices. Since quantum circuits can only implement unitary operations, block-encoding embeds a generally non-unitary matrix into a larger unitary one. The scaling factor $s$ ensures that the overall operator remains unitary. For the most basic use of block-encoding, see the first item in the technical notes at the end of this notebook.\n",
    "\n",
    "In all three methods presented in this notebook, we assume to have an exact $(s, m, 0)$-encoding of some Hamiltonian as an input, where the output of each method implements an approximated encoding of its Hamiltonian evolution\n",
    "$$\n",
    "U_{(s,m,\\epsilon)-H} \\rightarrow U_{(\\tilde{s},\\tilde{m},\\epsilon)-\\exp{(-iHt)}} = \\begin{pmatrix}\n",
    "\\exp{(-iHt)}/\\tilde{s} & * \\\\\n",
    "* & *\n",
    "\\end{pmatrix}.\n",
    "$$\n",
    "This will be acheived by implementing a block-encoding for the polynomial approximation of a function of the Hamiltonian, $P(H)\\approx \\frac{1}{\\tilde{s}}e^{-iHt}$, with $\\tilde{s}$ being some scaling factor. Note that to get the approximated unitary of $e^{-iHt}$, we have to apply amplitude amplification for $\\tilde{s}\\rightarrow 1$, see for example the [Oblivious Amplitude Amplification noteook](https://github.com/Classiq/classiq-library/blob/main/algorithms/amplitude_amplification_and_estimation/oblivious_amplitude_amplification/oblivious_amplitude_amplification.ipynb), or apply some post-selection. The amplification step is beyond the scope of the current example, and we simply employ projected statevector simulation.\n",
    "\n",
    "\n",
    "**<div class=\"alert alert-success\"> This notebook demonstrates how to implement Hamiltonian simulation with all three methods: GQSP, Qubitization, and QSVT. Each method is explained and defined independently. We start with some preliminary steps, that are common to all three methods. In addition, in the last section we present a summary that compares between the properties of the three methods.</div>**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3",
   "metadata": {},
   "source": [
    "## Preliminaries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "4",
   "metadata": {},
   "outputs": [],
   "source": [
    "import time\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import scipy\n",
    "\n",
    "from classiq import *"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5",
   "metadata": {},
   "source": [
    "### Setting a specific Hamiltonian to Evolve\n",
    "\n",
    "We start with setting some specific hyperparameters for our problem. Those can be easily modified to treat different usecases.\n",
    "We set a specific block-encoded Hamiltonian to evolve, taking a simple example of a Hamiltonian as a sum of Pauli strings, and use the `lcu_paulis` function to block-encode it. The idea of the Linear Combination of Unitaries (LCU) technique, which also appears in the QSVT and Qubitization methods, is the following:\n",
    "\n",
    "**Definition**: LCU refers to $(\\bar{\\alpha}, m, 0)$-block-encoding of a matrix given as a sum of unitaries:\n",
    "$$\n",
    "U_{(\\bar{\\alpha},m,0)-A} =\\begin{pmatrix}\n",
    "A/\\bar{\\alpha} & * \\\\\n",
    "* & *\n",
    "\\end{pmatrix}, \\text{   for     } A = \\sum^{L-1}_{i=0} \\alpha_i U_i, \\,\\,\\,\\,\\, \\alpha_i\\geq 0\n",
    "$$\n",
    "with $\\bar{\\alpha}\\equiv\\sum^{L-1}_{i=0}\\alpha_i$ and $m= \\lceil\\log_2(L) \\rceil$. The LCU is implemented with the so-called \"prepare\" and \"select\" operations. The former prepares a quantum state that corresponds to the probabilities $\\alpha_i /\\sum\\alpha_i$, and the latter acts as a quantum switch operation. See second item at the Technical Details at the end of this notebook, and the [LCU tutorial](https://github.com/Classiq/classiq-library/blob/main/tutorials/basic_tutorials/quantum_primitives/linear_combination_of_unitaries/linear_combination_of_unitaries.ipynb) for more details."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6",
   "metadata": {},
   "source": [
    "We now set the input parameters for the specific problem, which include:\n",
    "1. The time evolution $t$ and the error $\\epsilon$.\n",
    "2. The Hamiltonian $H$ and its block-encoding quantum function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The Hamiltonian to evolve: 0.4*Pauli.I(0) + 0.1*Pauli.Z(1) + 0.05*Pauli.X(0)*Pauli.X(1) + 0.2*Pauli.Z(0)*Pauli.Z(1)\n"
     ]
    }
   ],
   "source": [
    "EVOLUTION_TIME = 22\n",
    "EPS = 0.1\n",
    "\n",
    "HAMILTONIAN = (\n",
    "    0.4 * Pauli.I(0)\n",
    "    + 0.1 * Pauli.Z(1)\n",
    "    + 0.05 * Pauli.X(0) * Pauli.X(1)\n",
    "    + 0.2 * Pauli.Z(0) * Pauli.Z(1)\n",
    ")\n",
    "print(f\"The Hamiltonian to evolve: {HAMILTONIAN}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8",
   "metadata": {},
   "source": [
    "\n",
    "\n",
    "$$\n",
    "H = \\sum^{L-1}_{i=0} \\alpha_i U_i,\n",
    "$$\n",
    "where $U_i$ are Pauli strings."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9",
   "metadata": {},
   "source": [
    "The LCU of the Hamiltonian corresponds to the unitary:\n",
    "$$\n",
    "U_{H} = \\begin{pmatrix}\n",
    "\\frac{1}{s}\\sum^{L-1}_{i=0} \\alpha_i U_i & * \\\\\n",
    "* & *\n",
    "\\end{pmatrix},  \\text{   with  } s = \\sum\\alpha_i.\n",
    "$$"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "10",
   "metadata": {},
   "source": [
    "Let us derive some additional parameters for the implementation, such as the block-encoding size and the scaling factor $s$. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "11",
   "metadata": {},
   "outputs": [],
   "source": [
    "data_size = HAMILTONIAN.num_qubits\n",
    "block_size = (\n",
    "    (len(HAMILTONIAN.terms) - 1).bit_length() if len(HAMILTONIAN.terms) != 1 else 1\n",
    ")\n",
    "be_scaling = np.sum(np.abs([term.coefficient for term in HAMILTONIAN.terms]))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "12",
   "metadata": {},
   "source": [
    "Next, we define the block-encoding quantum function, and a Quantum Struct for its variable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "13",
   "metadata": {},
   "outputs": [],
   "source": [
    "class BlockEncodedState(QStruct):\n",
    "    data: QNum[data_size]\n",
    "    block: QNum[block_size]\n",
    "\n",
    "\n",
    "@qfunc\n",
    "def be_hamiltonian(state: BlockEncodedState):\n",
    "    lcu_pauli(HAMILTONIAN * (1 / be_scaling), state.data, state.block)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "14",
   "metadata": {},
   "source": [
    "Finally, we set the initial state to evolve and calculate classically the expected evolved state for verifying the quantum methods."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "15",
   "metadata": {},
   "outputs": [],
   "source": [
    "state_to_evolve = np.random.rand(2**data_size)\n",
    "state_to_evolve = (state_to_evolve / np.linalg.norm(state_to_evolve)).tolist()\n",
    "matrix = pauli_operator_to_matrix(HAMILTONIAN)\n",
    "expected_state = scipy.linalg.expm(-1j * matrix * EVOLUTION_TIME) @ state_to_evolve"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "16",
   "metadata": {},
   "source": [
    "### Setting Up a State Vector Simulator for Execution"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "17",
   "metadata": {},
   "source": [
    "Working with block-encoding typically requires post-selection of the block variables being at state 0. The success of this process can be amplified via a Quantum Amplitude Amplification routine. I`n this notebook, instead, we simply work with a statevector simulator. We define a function that gets execution results and returns a projected state vector."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "18",
   "metadata": {},
   "outputs": [],
   "source": [
    "## fix the execution preferences for this notebook\n",
    "execution_preferences = ExecutionPreferences(\n",
    "    num_shots=1,\n",
    "    backend_preferences=ClassiqBackendPreferences(\n",
    "        backend_name=ClassiqSimulatorBackendNames.SIMULATOR_STATEVECTOR\n",
    "    ),\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "19",
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_projected_state_vector(res) -> np.ndarray:\n",
    "    \"\"\"\n",
    "    This function returns a reduced statevector from execution results.\n",
    "    Expects a 'data' variable, and a 'block' variable to be filtered out when not in the |0> state.\n",
    "    \"\"\"\n",
    "    state_size = 2 ** len(res.output_qubits_map[\"data\"])\n",
    "    proj_statevector = np.zeros(state_size).astype(complex)\n",
    "\n",
    "    df = res.dataframe\n",
    "    # Filter only the successful states.\n",
    "    filtered_st = df[(df.block == 0) & (np.abs(df.amplitude) > 1e-12)]\n",
    "    proj_statevector[filtered_st.data] = filtered_st.amplitude\n",
    "    return proj_statevector"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "20",
   "metadata": {},
   "source": [
    "#### Defining a Utility Function for State Overlap\n",
    "\n",
    "Below we define a *classical* post-process function that will be used to verify the quantum results, by comparing them to the expected classical result."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "21",
   "metadata": {},
   "outputs": [],
   "source": [
    "def compare_quantum_classical_states(\n",
    "    expected_state: np.ndarray, resulted_state: np.ndarray, post_selection_factor: float\n",
    "):\n",
    "    \"\"\"\n",
    "    Aligns global phase, renormalizes, and computes overlap between the expected classical state\n",
    "    and the one resulting from the quantum program. Since we work with a projected statevector, we need to insert the post-selection by hand.\n",
    "\n",
    "    Parameters\n",
    "    ----------\n",
    "    expected_state : array_like of complex, the classical (reference) statevector.\n",
    "    resulted_state : array_like of complex, the simulated statevector, projected onto the block=0 space.\n",
    "    post_selection_factor : float, the post-selection success probability of the block=0, to be applied uniformly to `resulted_state`.\n",
    "\n",
    "    Returns\n",
    "    -------\n",
    "    renormalized_state : the `resulted_state` after global-phase alignment and multiplication by `post_selection_factor`.\n",
    "    overlap : The absolute value of the normalized inner product between `renormalized_state` and `expected_state`.\n",
    "    \"\"\"\n",
    "\n",
    "    relative_phase = np.angle(expected_state[0] / resulted_state[0])\n",
    "    resulted_state = resulted_state * np.exp(\n",
    "        1j * relative_phase\n",
    "    )  # rotate according to a global phase\n",
    "\n",
    "    renormalized_state = post_selection_factor * resulted_state\n",
    "    overlap = (\n",
    "        np.vdot(renormalized_state, expected_state)\n",
    "        / np.linalg.norm(renormalized_state)\n",
    "        / np.linalg.norm(expected_state)\n",
    "    )\n",
    "\n",
    "    return renormalized_state, abs(overlap)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "22",
   "metadata": {},
   "source": [
    "### The Jacobi–Anger Expansion\n",
    "\n",
    "The core of all the three methods is the polynomial approximation of the evolution, this transformation relies on the Jacobi-Anger expansion [<a href=\"#ref-jawiki\">4</a>]."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "23",
   "metadata": {},
   "source": [
    "The most general form of the Jacobi–Anger expansion gives:\n",
    "<div style=\"margin-bottom:0\">\n",
    "\\begin{align}\n",
    "e^{it\\cos(x)} &= \\sum_{k=-\\infty}^{\\infty} i^k J_{k}(t) e^{ikx}, && (1)\\\\ \n",
    "e^{it\\sin(x)} &= \\sum_{k=-\\infty}^{\\infty} J_{k}(t) e^{ikx}, && (2)\n",
    "\\end{align}\n",
    "</div>\n",
    "from which, we can also get the expansion for the sine and cosine functions using Chebyshev polynomials:\n",
    "<div style=\"margin-bottom:0\">\n",
    "\\begin{align}\n",
    "\\cos(xt) &= J_0(t) + 2\\sum_{k=1}^{d/2} (-1)^k J_{2k}(t)\\, T_{2k}(x),&& (3)\\\\\n",
    "\\sin(xt) &= 2\\sum_{k=0}^{d/2} (-1)^k J_{2k+1}(t)\\, T_{2k+1}(x), && (4)\n",
    "\\end{align}\n",
    "</div>\n",
    "where $J_i(x)$ and $T_i(x)$ are the Bessel function and Chebyshev polynomial of order $i$, respectively. The infinite series can be trimmed at some degree $d$, giving an approximation for the functions, whose relation to the error bound $\\epsilon$ is\n",
    "<div style=\"margin-bottom:0\">\n",
    "$$\n",
    "d = O\\!\\left(t + \\frac{\\log(1/\\epsilon)}{\\log\\!\\Big(e+\\frac{\\log(1/\\epsilon)}{t}\\Big)}\\right).\\quad (5)\n",
    "$$\n",
    "</div>\n",
    "We start with defining a function that gets $\\epsilon$ and the evolution time $t$, and returns the Chebyshev coefficients of the sine and cosine functions, using the `scipy` package."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "24",
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy.special import eval_chebyt, jv\n",
    "\n",
    "\n",
    "def get_jacobi_anger_degree(epsilon, t):\n",
    "    # Equation (5) follows from the asymptotic estimate, taking a factor of 2 as a safety margin\n",
    "    return 2 * int(\n",
    "        np.ceil(\n",
    "            t\n",
    "            + np.log(epsilon ** (-1)) / np.log(np.exp(1) + np.log(epsilon ** (-1)) / t)\n",
    "        )\n",
    "    )\n",
    "\n",
    "\n",
    "def get_exp_cheb_coeffs(epsilon, t):\n",
    "    poly_degree = get_jacobi_anger_degree(epsilon, t)\n",
    "    cos_coeffs = [jv(0, t)] + [\n",
    "        2 * jv(2 * k, t) * (-1) ** k for k in range(1, poly_degree // 2 + 1)\n",
    "    ]\n",
    "    sin_coeffs = [\n",
    "        -2 * jv(2 * k - 1, t) * (-1) ** k for k in range(1, poly_degree // 2 + 1)\n",
    "    ]\n",
    "    exp_coeffs = np.zeros((poly_degree // 2) * 2 + 1).astype(complex)\n",
    "    exp_coeffs[::2] = cos_coeffs\n",
    "    exp_coeffs[1::2] = -1j * np.array(sin_coeffs)\n",
    "    return exp_coeffs"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "25",
   "metadata": {},
   "source": [
    "We can examine the approximation for a specific example:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "26",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "exp_coeffs = get_exp_cheb_coeffs(EPS, EVOLUTION_TIME)\n",
    "\n",
    "xs = np.linspace(0, 1, 100)\n",
    "approx_exp = sum([exp_coeffs[k] * eval_chebyt(k, xs) for k in range(len(exp_coeffs))])\n",
    "\n",
    "plt.plot(xs, np.cos(EVOLUTION_TIME * xs), \"-r\", linewidth=3, label=\"cos\")\n",
    "plt.plot(xs, np.real(approx_exp), \"--k\", linewidth=2, label=\"approx. cos\")\n",
    "plt.plot(xs, np.sin(EVOLUTION_TIME * xs), \"-y\", linewidth=3, label=\"sin\")\n",
    "plt.plot(xs, -np.imag(approx_exp), \"--b\", linewidth=2, label=\"approx sin\")\n",
    "plt.ylabel(r\"$\\cos(x)\\,\\,\\, , \\sin(x)$\", fontsize=16)\n",
    "plt.xlabel(r\"$x$\", fontsize=16)\n",
    "plt.yticks(fontsize=16)\n",
    "plt.xticks(fontsize=16)\n",
    "plt.legend(loc=\"lower left\", fontsize=14)\n",
    "plt.xlim(-0.1, 1.1);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "27",
   "metadata": {},
   "source": [
    "### Defining a Walk Operator\n",
    "\n",
    "This part is relevant to the Qubitization and GQSP methods. **These two methods assumes that the block-encoding unitary $U_H$ is also Hermitian, a generalization is given at the third item in the Technical Details section**."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "28",
   "metadata": {},
   "source": [
    "Given the block-encoding $U_{(s,m,0)-H}$, we can define the following *unitary* operator (usually called the Szegedy quantum walk operator [<a href=\"#ref-szegedy\">5</a>]):\n",
    "$$\n",
    "W\\equiv \\Pi_{|0\\rangle_m} U_{(s,m,0)-H},\n",
    "$$  \n",
    "where $\\Pi_{|0\\rangle_m}$ is a reflection operator about the block state 0. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "29",
   "metadata": {},
   "source": [
    "We start with defining a walk operator for our specific example. For the reflection, we use the `reflect_about_zero` quantum function from the Classiq open library."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "30",
   "metadata": {},
   "outputs": [],
   "source": [
    "from classiq.qmod.symbolic import pi\n",
    "\n",
    "\n",
    "@qfunc\n",
    "def walk_operator(\n",
    "    be_qfunc: QCallable[BlockEncodedState], state: BlockEncodedState\n",
    ") -> None:\n",
    "    be_qfunc(state)\n",
    "    reflect_about_zero(state.block)\n",
    "    phase(pi)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "31",
   "metadata": {},
   "source": [
    "This unitary function has the following important properties (see Section 7 in Ref. [<a href=\"#ref-lin\">6</a>] ) :\n",
    "1. Its powers correspond to a $(1,m,0)$-encoding of the Chebyshev polynomials:\n",
    "$$\n",
    "W^k = \\begin{pmatrix}\n",
    "T_k(H/s) & * \\\\\n",
    "* & *\n",
    "\\end{pmatrix}=U_{(1,m,0)-T_k(H/s)}, \\quad (6)\n",
    "$$\n",
    "with $T_k$ being the k-th Chebyshev polynomial. \n",
    "2. Its specturm has a nice relation to the spectrum of the block-encoded Hamiltonian:\n",
    "  $$\n",
    "  \\text{eigenvalues: } e^{\\pm i \\arccos(\\lambda/s)}, \\text{ with eigenvectors:  } |\\varphi^{\\pm}_{\\lambda}\\rangle \\equiv \\frac{1}{\\sqrt{2}}\\left(|v_{\\lambda}\\rangle |0\\rangle_m \\pm i|\\perp_{\\lambda}\\rangle\\right), \\quad (7)\n",
    "  $$\n",
    "  where $|v_\\lambda\\rangle$ is an eigenstate of the Hamiltonian $H$ with an eigenvalue $\\lambda$."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "32",
   "metadata": {},
   "source": [
    "### Verifying the Block-Encoding"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "33",
   "metadata": {},
   "source": [
    "As a final sanity check of the preliminary definitions, and before moving to the more complex Hamiltonian simulation implementation, let us quickly verify the Hamiltonian block-encoding. For this, we define a model that applies $U_H$ on the state we would like to evolve, and verify that the resulting state, after post-selection, gives $(H/\\bar{\\alpha})\\vec{b}$."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "34",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Quantum program link: https://platform.classiq.io/circuit/38AzuRgj0ekkfOylnboaythq6IO\n"
     ]
    }
   ],
   "source": [
    "@qfunc\n",
    "def main(data: Output[QNum[data_size]], block: Output[QNum[block_size]]):\n",
    "    state = BlockEncodedState()\n",
    "    allocate(state)\n",
    "\n",
    "    inplace_prepare_amplitudes(state_to_evolve, 0.0, state.data)\n",
    "    be_hamiltonian(state)\n",
    "    bind(state, [data, block])\n",
    "\n",
    "\n",
    "qprog_be = synthesize(main)\n",
    "show(qprog_be)"
   ]
  },
  {
   "attachments": {
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EQAAEQAAEQMCiBDJTyykztcKi58TJrIOAGUWRBx3bW0pF+VXW0TO0AgRAAARAAARAAARAAASaScA/2JUGjPCGKGomL3srZlb3OQgie/v1QH9AAARAAARAAARAwDEI4D3WMa6zsV6azVJk7ATIBwEQaB2BHy7uTT96qk/rDsZRdk3goyWn6OOlZ+y6j/bcOfxt2+fVTd6Tz3+Xp0g+kUDAXgnMSHyaZiQ+YzXdS83eRUvXLjBLeyCKzIIRlYCA+Qm0RhC9+Pg28zcENVoVgajYAHrwqUEQRVZ1VZrfmEFJwS0e7MhIvUofLklu/klQskMIjL8jmgaOCIYo6hD6OKklCMR2HWlVgkj6HNt1FLfpaVp/8PU2IzCr+1ybW4MKQAAEQAAETBI4zy/ISCAAAtZHIOPMVRqUFGJ9DUOLQMDOCcSGJ5mlh+1mKXr22WcNNvCvf/2rwXxkggAIgAAIgAAIgAAI2A+B/r1GUkrabvvpEHpi1wRgKbLry2v5zvn5BNPtYx6kFx79D/36oXdpWP+pJhshN8wXH1tusgx2NiTwjz8eJPlBAgEQsC8CD922hvZvz7avTqE3dkWgT4+h9Ni8P9Mfn/iSFt7+K+oc2M1k/+ZPfZrkOe8IKWng7fT7n3xMf/r5apo2+kcmu4x3H5N4Omxnu1mKYBHqsGvaYSd2cnKmyLDeNHHYfDqVvo8iw/vQgtuepbzCbDp38bjBdskNc/mmtvuBGqwcmSAAAiAAAiAAAmYh4OriRtNHPUA1NdWUk5tOk4bPp/LKUtq8+1MqKStsdA558a/lXEewFLm5dqKH5rxIOw6topCArrRo7ivM6CwdTd3ZiItkyLuPsEGyLgLtJoqsq5tojSUI1NbWqJvfr16foU4X33MEPTL3VSWODIkiuWHG80/KW/Mt0TybP4dmHdJGkv/xx7ou/ez3iTbfN3QABByVgPw979+eU999+TsfNi6Hf8L5p2t9PjZAoKMJVFXfoCWfPFnfjF8+8A516xxD3l7+BkWRIw163qiqoEdfHl7PJrHfJIqLHm5UFEnBFRgQrudlLRtwn7OWK2GX7XCiqqpKKizONdo7RxhBMtp57AABEHB4ArrCRwSS9l1XKDk8JACwSgLO7B1SWJJHFTfKDbZPBj0dNTk5udDlgkyD3XckC5pBAFacCUuRFV8cW26ar3cgxUYlUOn1YjqdccBgVxxpFMkggBZmahYhWIhaCA7FQcDKCWh/23WiCBYiK79caB4T6BUxkAL8utDWg19QMQsj/SQv/jLo6YgDnwl9J5KrqzsdOL5RH4v6Lu8+KWf3GNyHzI4lAEtRx/K3y7M7OTnx3KI4GjvkLtqZvJoqKq836idGShohQQYIgAAIgAAIWD0BZ2cXmj3hp3Q5P4OyLqdSdU1VozbLi78juoe5uLjS4/e+Rmu3/4vKyq814qJlnEhHRD6NhTV9wlJkTVfDTtrSnYMtzJ38JGVdOkWb93xqsFdyw8QkQ4NomszURpWbLIgCIAACNkXg/Y2zbKq9aKxjErhr4uPUNbQXffD1C5Sdm2YQgqPOF376vjd5ILicPln3KnNp/JaDAWGDvy5WkwlLkdVcCvtoSGhQJM0et4gkSs1Ha19RnzKqZCg54iiSIQ7IAwEQAAEQAAFbIDAucS6NSZhDa7b9k9IvHCcXftaLd4hu0lzndPMcYXv+bc+QuM794e0FiotYjfSTo1rQ9DlY6/fGV8xaW4p2WT0BH68AGjX4DhWmUxq77Pn9dK3sKm3a8xn9e90r9e3HSEk9CmyAAAiAAAiAgE0QkHlEd4x9WC298fOFS1Sbz15IoXdW/obSspLr+yAv/o621MbguHG0YNpTzMCJ3nquLgz3kdPb6eV37ue8WxYjFXwCUefqf1cMbaTnHKKUjG10vaK4fveUhB9TsF/3+u/ttQFR1F5kHbBeEUCrtrylfkx13xFvmKZ4mNr34uPbTO02ua97156UNHQ65Rfk0Hfff2myrPGdTtS9a4wZ6jF+BuwBAUcjkJF6lT5ccusl0tH6j/7aJoG0rKP06yUzm2y8I7rOiQC6+9lIk2w0C5ojBp8wCUZvZ3rO4XpBdC7nCKWc30YFxRfpnnHP8RpQ0XqlzfsV7nPm5YnamkHAkcN0NgOP2YoEBoTQlHHzKD7u1toJLa1cvCIiusXSvFmP0+Sx97T0cJQHARAAARBwIALai78DdbnZXYXrXB0qsQQdz9hKq3e/TvtPr6kXQBpIsQotmPACDYubRZcK0yg8qCd5dvLVdrfrJyxF7YoXlesT0G6YGCnRJ2P+79VVVWrlcYn+58TrSdw548dUVsbRcFjp+PoEqjWk9h7cSJdyM2no4EksfnrR5StZFBYSwY1xoqyLqbT/yJY6f3HlMu6ktmNjBlGf2ARyd/ek2tpaupiTRnsObOQjnCgmOp4G9htJLq5uVF5eRidTD9DZcynkwvPKxiTNpJDgbuxIUEMFhZdp++41VF3dOGpRHQmOYMjtGTpkElu6LpEIPAlxWlSUR1t3fsV11NLs6dyf69fIy9NHtWPDt/+mXj0GUO+eg0nmsZWVldCOPWvoWmmR0f7tO/yt6tOIxKkU3iWK/cDdmUsFpZzcS2czUmhEwlTqGt6DSq5dJX/fYMrKPqO4DBsyldzcOtGNGxV06OhWzjM82dj8VxU1ggAIgID1EoAniPFr44gWNI3G5kP/ogOn1yqrjxI57n4UEz6YTmRsp6VfPaCET3zUOJo18mnlJifC6W+cP33Yz6iyqryRcNLqNfcnRJG5iaI+kwRwwzSJx6w7fbz9WUx0YZETSc4shObPfpJKWShU86rkXl5+Sqh0Dgqn9z79I40bOZuGDZ7MoiiTAv1DleDJvHCaUs8dbdAmDw8vmjf7cerTK4HKK8pYXAXRhZyzdOzEbnJj0TJv9hMsikYpsSDi6/jpvfS3Zb+kQfGj1XHBgeEsOm6wyCikKhZEO1gYGUpioYqJ7q/anMfuf34s4jp18qSrxXmUz4Lq5JkDal/Z9RLy9PBR5ztybIfKE9EmQlAEm79fMH2+6m9G+3cmPVmVETYi2Cp5EUIPD28WV4Pptbd+TuNH30VD+o9V7fXxDqDN2z+n4Qm3URL/3OCFiaXPPSL70VvvP6ciDhnqC/JAAARAwFEIOPKLv6lrrA0Imypjj/tE3KzZvZTyiy/QlMSHafHcD9nq49eoq1JO5hGJeIoJT6BPvn1OCaLxA++jncdXQBQ1IoYMsxOI7TqSYsOTjNabW3SOcovOGtwf6t+TQv17GNxnKlNumLcX1FKkv/HzmjresfYZZt9cBq5u7mwxcWYx4aUOkShBvj4B9OW6t9mCVKMETFLibUoUeXbyVtYVN9dO9AXvHzVsBkVFxFHCwAl0XWetBVeOpnO9vJT2J29hgVVME0fNpe7hMTzvqBdbdmK5/Hi2PJ2njds+o+GDpyhBFujfmeZMf4RFR3faxPkiiObe8RjNmfaIUVEkDXZT7XdRIm3lmr9TeFgPGj/yTpo8bj6dSjuk2uvN4m7txg/UIsFJQ6dRHIu11PQjdIDbJ+eYNvEHtGHzx3wTNty/xEET6EL2WbqSn00nzuwnT7Y6jRk+k4XQGG6BEx/npc4jAvCb7/6jrFxTJyykkpICWrfpQ5rAokkY7tjzNR1M3qY4W+q/GYlPt/lUqTl1CwimZmPNjDbDRAUgYEUEmnq+S1NTMjcbbXH/yClG9xnbIY8avlWSOe5Nxs5hifxIf3fKDFlv9FStYZM0fCSV8LuPrbMxCsXADhE4n297mRJ63qbEkKlACSKE5GfZuidvWY6ixysBFRM+hMp0gi4YOJXZsmApMhtK26hI/iBFCMV2HdVkg3OL0mnL0X8aLBcaEEOTBjxqcJ+xTPebN8wo/1EUldj0+Y3V4zj5j5u9q+Kutmr9MnJisSRWHXc3j/pziGVF3N1W//c9lRcd0YdGJk6jLTu/qC8jLnFiTeoc1JXdyTorVzwRD8FBYTSEBZTUIeJi3cYP6XzmSfL1DaSKiusU1b2PqkNClIrbmYRwDe8SXV+vqY3Colz6itscFhpB40fdSb3ZEqSlGzcqaeWaN9lSVEmv/m65yhY3Pi/POv9jD35Kd2c3PEmG+pfE/Xsz+bfsYneV3fPclEVKyjrrhFKV446yJezLdf+khXOfYqubTMVkwcQCSpK4Jg4eML4DRNEz6vzm+i81exetP7SEIJDMRRT1gIDlCcgzfkZi8+4NHu7elHs13WAjE3vNoS48+NmSFMSe19fyRRQ17/wtqdvSZVM6Zxk9ZWv6F86PoZzT5FDvPt8efo/uHvNrEmuPIeuQPmA11+j8Vhrd7x46dHYjzzvaRp054pwmmPTLt8d3iKL2oGqFdbbkRtlezfcJrrthtlf9qLdpAuK6JgvLyRwfCRMqL/S6qbqmWuWXszVIXNgCA0Lrd8v3iO69eZ7NbRTgH0J7D21UVh9xtxOLlNtNIVFXB7ErWiWvU+XOrngeam4T234oKDCMxK3vzNnDVFB0pb5uUxu1tTVqd9l1bpMSPHViRDIreU7PdRZqModI3NmkjV5evkpwnTt/XK0oXllZUV+9fv+C2L1w6KCJNGTAOCXexAIW32eEHpdayr6UrkSVsJMk4k4sVzLfSdz7ZP6VrScZKFnMP+sPvsY/r9t6d9B+EHAoAh39jJdBT7EUFRjXEg51PXQ7qw0I6+bZ+7ZYiQazhUiCJTRXEGlziERExbLLnMxBimdrUURIP4vhgiiyGOqOO5Gxm6WMDGvuM8Zal5l3zOAuyS+vLDW4z1jmj+OeoX+ves3YbuTrEYjT+26OrzU3BYbUxQaQBkmsNzJXSAIiiAVF9mdm677sO1Fsj4FssYmiqyxoUk7uof5xI1QdIn5O8/ycQex21i92KI1NmkVjR8ykLqGR9Ma7v6acyxnsjtdHuZ1JMIdAvxAOxNC83x8RYBL5LjS0uzqXzCvSUo0ScXVWILFQxcYMVFajM2ePUCKLHbHyXMm/qIob6t8FDhIhYcu9vfyVsMk4f0KrWs3Dki+CSYkhris5ReYtPcH11lB6xjF210tkMehGF7PTlJCaNe0htW/NN++b+F5La76ps8bVn6wVGyJe2poMWY21UVAIo7bSxfEgYBkCi2d+btD7o6l7hCkX+Q0Hl7TIRT6+VxLlVDjR3oO274ob2Zvd54y8+8gVbYqr/lW//85naMXG15Rrof4+W/tu6Jmh3wcRRGoOUTPXFtINqqBZlSQCnYTj/pbr0oIv6J+nPb5DFLUHVSuq05AgMtdIcOYVw4LJUPe1SYZ40TJEx3De0zTb8I5m5oogaCpJFDfdJJYhCb8tQQeuFl+hLduXkw8HOahLtXSegy+ImJF5SuNHzlFWGbHOhIdFqflBSRytLaJb7/qgCucyT1BuXhZt4gAFP7znl+xmNpYFVR4Lq0g6nrpP99RGt8UKJMEdxCVOAihs3PqZjqKra7/0ddf+9TwX6nbqxnOcxJrVkwM1HDn+fQPxpd+/zdw/ccfrFzdMuQBOm3gf1fJ8K2cXZ1WPxkfyZDuDXQJPsYthbxaPIxKmqXDllzl6X1FJvhJR98z6mbKKiSiS4BbGv5tDFJnPmiPzD2YkPFX/YgVhZPTXETtAwKoI6Asieb7LYGdb3WBb8nwXIEnDl6sFW+0hsuyYLv6UeaXI6HVuCRt59xEL2lc7zHe/NtowC+yQZ4V4FJhKImbiIpPIy0BABf3jDAkirczkhIdUkAaZT8SORhZJEEUWwdwxJ1EvOjq+vWIZWrp2QYc0BlHnLI9dXLpWrH6Tw1/nUA2LhuWr/05V7G4mSb6v4Lk41exypiURFkfYEiIv+eKHJp8pp/YpcaDVc+ZssjpOxEUlC5RDx7ZztDYvZWW5mJOuzhEd2Zd8OACCWHTOseWlqLiAdu5bp6LHSRQ5cT3bsadQhdbWzm38s5Zy2bK05+A3ShRJ5LktO1aoaHPSH5mvVJdYsGWd4f7+nbpwSHERUIeOfUdHj++ujwpnsH8cejv70jnFI8CvM5Vw+O5dBzaoqHLiaifhv48zg/TM4+o0EjFPztuHrWF+PF/q4NHvOFBDGv+kK8vQ1/9dpqxTUlgYm/pe127r+F9eoJbyj+4gCoSRdVwbtAIEjBEwJIg6YuBRXvxleMoeBJEx1m3JdzQu6ZcO0fhBTc8jMiWIhLfMJZJ1iq5bKMiCnBOiSCjYaZKRXy11pCCSNthCmM74niMoPKQnHTm9jfIK61yuNH62+ClR1VayKNKS7ra4f2nfZT5QXeIgCaf3sej4QgVikKAKkvTrESuIrA0kLmX66wztO7SZDvDaRhIVTtb20ZKsj7T2mw84sIO7EkUyD0jmM825/RH+LvOb9BILijK2SPGHWtNo5Zq3OPS2t1pzSCuptV/7Lq50W3euUvOLpH2yPpGkpvqXX3CZvlr3jprrJBH1dC1s0nfdJPtkDSP5EVEkfdTKC9Mv1rxdX7yp7/UFrWhDe6HSBJF8anlW1Ew0BQQcnoCKLqczYm8uD5DWgO0XM7LZh3l5+NLQfpOpmu+XOw+vbvZxtlqwJQPC3UJ7Ue+oIZR9JZ1OZxy0yS6fu3SEwgJ7NTmPqClBpHU+JkyE0Vk1r6g5c5O041r7CVHUWnJWflyjGyZHlerIdPezHJamhSm6az/q0S2ejqXtZpFyweTRiXyTlahgp84doJKyQpNlje2MiRhEA3uNofPZJ0yKInOcy1gbOiJfhMeZc8nKkiPzfbRgAqbaIoumGksS7ltXEOmWk+ALJD+cZI7PwL4jVSAG3TKyLXWs3fQeB2Q4wmslJSvxpYkc/bL630Uc6ZZtbv90j9Gv09D34pLW/Z4Zqsua8jQRdEsYPQ1hZE0XCG0BASagP+ip/d12BJzlPF+mucmjkw8l9J2srP2mRJGPVwCNGDCdsnJO0ZnMI82t3urKPf/W/Ga3qUtwBA2Nv42SeWDWlCiK5vei7qE9KS3rKF3Ky2h2/ZYomHEpmcKCYpp0nVu2/sn6dYgsIXaa23eIouaSsrFyujdM5WNsA+uQyEuyNuouuAf2Hkv9e46iC5dTmxRFowbN4rVqiiiDb6C6oki/TnNcRmPnMkfdHVNHLc8HWk2neEHUjKxTFmuCWFK+2vCOshw1OikLtYysk+wO94aK7tZof4syOqZ/LWqiFReGtciKLw6a5pAErG3Qsz0ugh+Hq50++kFau32ZTYui9mATGzmEhsSN5wiul6xOFEWHDaKkvnc1aSn6yYw3KDyoaYuSzCtqTjlzcYYoMhdJK6tHdx2ipiLMdXTTPXhhzUVzX6GkgTN4rRh3Wr31Hbp6LZcmD19IXUN60IDY0fTpf/9Ch09uoXsm/5zEUiMT/dOykundL39PI/rP4JGnieTt6U9TRiykP3/wCGVeOk0/nv0iJcRP5rDQbrR1/wpa+e0blJvP82V0kvhCzxr3E4rvyZMhOW7meVlIgFOAbwg9OOv5Zp2rvKKUFk7/FZu9E5Qr2JZ9y+mrLW9Sro3EJhUhmnP5vPrRQdPum5ormqkTyfpAbU0d1b+2trsjj5dRZ81SJO2Ql7C2TtzuyP7g3CBgrwRUFFkrH/T08vSjiUPn0YwxD1EoL2ZUUVlGu5PXsUu0D41PvJvunfYsb/tSYUkufcbP+uNn99ITC/7KniL96Gfz/8Li6CF66Z/3Giy7df9Km7603bvE0vSRP6SkQTPJn4Vg3tUcZSkK69yDZo97hMYmzlXrCZ44u4dWbF5CAT4hNHv8IgrrHEVD+kxU70vfHVhhsOwJ5mjp1CNscLNOKfOFmpOaW645dTWnDERRcyjZWBl5gdFN1v4yI/7FEeF96S8fPkqpmYfVHBBx4erk6kUDeo2iVdveIvnjrq6pog27PqCP17+qFg5dNPdPFBneh9Zs/yeFd47kyXil9M3ujymTrUWzJiwiX58g+t/3H+YbTWcWU4upW0hMA1EkC5feOfFxZYX65ZI7+IZ7D8WxP6+k4tKCZp9LzPwfrn2J8jmgwchBd/CN/8e059h6mxFFur8r2AYBjYBYmDVhJGFYrf0+orUbnyBg7wTk71FL1j7oKdFDZXBzzsSf0qcb/k+5hT0850XV/Bsc+Cf59HY6lbGfrpUV0cJpv6Bpox6k7Qe+pGVf/Z5+vnApbeRn+o5Dq5S7naGytiyKZBB4eP/p1D2sNy355An1rjJpeF0wrOJrefwO8hG///D7TXAU3TXpCRoUO56F0eu8pEUYDY4bp9gcS9vJUwdcDJbtCFGk/V7a6idEka1euWa2W0aRrD2lZiZz2ORr9Ojdf6KNez6hLfs/VyNJN6orqLKqgkr5Zll5o1x149r1Yp6kOZVCAyPUqIorRzKTRTtvVPOipDeus+vcVari7V7dB5GMwDw06wXlktc5IEyNSrmw1aia90sSK5GvVyCt4ZtODk9sLC2vO1b2qXkpzTxXEd+85KY/kW9mPXgelAdbsWSRUSQQAAEQAAEQMDcBWxJFMiiZ0GcSFVy9pDw2gvzD2dW9LgiPPKuLSvNpWPRUigzrS50Du5ELv+DLenqy/ILsLysvYcFUV95QWXOztWR90TyoGxUWp4RiStou5fFSzlY0SdLvAJ4fO6TPBIrmQWM/HuSV4ESyAHtlVTm/9/C7EU8ZKK+oK2+orCX7Yi/ngiiylytpw/24nH+eXd5+TBPYUjNj7I+pc0A4u5/9o1GPxEXuBzN+S4XsR3vu4nF1w5SbpsxN0U9urp3oyKnvaG/KN+rGIfvPswVJE0Ty3cvDj61L15Soku+6qSXnmjflaRo/9G5KZ3c+EXEStllb30a3TmyDAAiAAAiAgCMREPd1Ca4gg4f6qXNgd3p8/v+jLkGRdPDkJjWw6c5u7IZSS8oaOt4a8zq5e6pmlfF7iH6SIBP3TFmsRM/F3FT1bmHoXUeOa0lZ/fPge0MCWizehrn4BgIWJODnHaRExIad77OQ2aJukD7eAWqkyJPnG7m6cBhnNsH3imDXNp7/8vW2t+nw6S0kIyrV1dVKFMnoia9nAHnwDVVGU66yb3LX0Fh1Iz51bj/l8yiVhI/29Q5UozGB/l3oJOeLaT+sczSvq+PPi40lslteiIqK1jOC/WKbea5INn1fuHyaVny7lAM9nFRufroBIyyIEqcCARAAARAAAashUMyWoBPpe/g524NCWAT5s8WjX88kteyDiAJ5/m49uJJWbX1bucjV1lartssztLamluf3hqrntDzbjZW1ms62sCFZl9MoryhHucfJPGYJyR3Rpbd6hwlmi5oM+q7f+R7tO/6NshBpokg8WdxdPXjpiwA13SDYv6vRsi1sksMXh6XI4X8FOh7AqMEzafaEx5SFSG6E/173J577k6Uc0CYNn08v/mw57Tj4Jd8YNtIg9qP9w6OfKdOylJVFOsUqlJp5iOZOeZL+mvANvbfqBeVr+8OZv6eXf7ZS3WCyOILd2yt+yXOPetC8256h979+gQ5x4AbxUV4w/Rf02Lz/o4u56cok7eLsSvtSNrDP7vhmnevo2Z0crGERvfbsJrp45Ry75PmrG1XHk0ULQAAEQAAEQKDjCIgbXMZFXubi6kV687nvlfCRQEienXx5sexCOnhiMy2Y9jTNmfAoXeJASDU3RdF1dqnP5EHGH878LU0afi+9s/JXRst2XO/adubia/m8jMh+7v8v6J3n91Nh8RUe0L2iAiucOX+QAz1NoWd/+BYVFOcq0SPu/5IucxhudxaUv3pwGW3jQBMHeTBZ3O0MlW1bCx3vaKfw8HBZiLjNacwMf/p+fd1iiW2uDBW0iYAEWlg8c7mqo6MXbW1uRyQKjZ93sLpJXq8oYSNN3a+ljA5JVLiy8mLl5iaWHU82xUv0GvE7VqNJN93nJPqchOCWm6kEZZAkYT1rec0buWFInuyXxeMqeI5SFc9FkuTu7sGWKFeq5OAObB6qr7cl53LjCZNi0dLmPtW1rbFbnzphM//bdG42vfj4tmaWRjFHIvDCm+Npao/2XfhwRuLT9YEWOnJhSHu7roOSgumJl+LpwyXJ9tY1h+9PVGwADUnqTr9YuLNdWSye+TlHhBylzrF07XybCIIi6whK1Fh5HotQkudr3TPZWYkAmYUr84LlWSwWEkmyLYGMKjlfAimJF4gESDJUVh1gpv8s/T7r6uqmBnfF40Vz8Zd3CBeeMy2DvuLlUl0jTJzUfGfppljZhIW8C1VywApTZc2ERVWj+35pznrbWpe53nVhKWrrlcDxZiEgQkZ+9JO4yGkTD2WfmI1lcqGhZChfRmJ0k4ioUg6goJvqxJBuTt12S86lgj3cFFmNa0IOCIAACIAACDguAXnJ133GawOX4hImL/Za0gSRfJcyunORTJXVjrfFTxFD8qOflBhiQWQoibCUHy2ZKquVwWfTBDCnqGlGKAECIAACIAACIAACIAACIGDHBCCK7PjiomsgAAIgAAIgAAIgAAIgAAJNE4AoapoRSoBAhxEQH3UkENAlgN8JXRrYBgEQAAEQAAHzEMCcIvNwRC0gYHYCHy05RQ8+Ncjs9aJC2ycgvxtIIAACIAACIGBJAqnZu0kC78xIfMaSp23yXOsPLWmyTHMK2L0oktjvz3BIw+f/cY9RHv17jaR5U5+mF96ab7QMdoCApQl8vPQMyQ8SCDgqAYk8OT5xrooyuWnPJ0YxvPTYclq+6XVKSdtttAx2gAAIgAAItJ3A+oOvszB6ve0VWWENdu8+9z+LPqW+PYaZRD+fBRESCIAACICAdREYPWQ2D1g9pRZ9NNWyeB7YgiAyRQj7QAAEQAAEmiJg15aiuyY9QdFd+zTFQO1fwaOMSCAAAiAAAtZBIC46kUYPmklB/l3U+mLGWiWWfggiY3SQDwIgAAIg0FwCdmspigiLox/c/kvatPsTXtvG+CKa8kCVZULxUG3urwzKgQAIgED7EvDzDqKRA2fygs6d6cz5I/WLORs6q1j6MahliAzyQAAEQAAEWkLALkWRrOz7/KOf0MGT39K67983yUMeqMfP7jFZBjtBAARAAAQsQ8DZ2ZnGJNxF/XqOoO2HvqTTGQdMnhiucybxYCcIgAAIgEAzCdilKFo091UK8Amhy3nn6c4JPyV5yN5/x+/Yla6fQSwn0jE51yAYZIIACICAhQkk9p1M41gU1VRXU9fOPSi+ZxINiB1NE4fNowDf0AatgetcAxz4AgIgAAIg0AYCdjmnqOhaPm07+AV5e/mTn0+wwhPgG0yd3D0boILrXAMc+AICIAAC9QRSc25Z0GPDk+rz23ujorKMUs8fJE8PX/LxDuT7the5VN8gLw9/Ei8A3SSWfok6hwQCIAACIAACbSXQ8AnT1tqs5PhPN/y5viUyt2hw3Hj6+2eNY6rb6wN1RsJT9f03V+z2+gqxAQIg4HAEYruOslifj6buJPmRJHOL5k5+kipvlNO6He82aIMMainXOSyl0IALvtg3gdiuI0n371HWjUECARAwD4EOFUUzEutCYesuApWavYvkRd4Sf+jyQCU7HGXUvWGa59cEtYAACDgaAf17sNyvrW1tCgTIcbTfSvRX12or70tIIAAC5iPQYaJIHrC6YkjrkrzQL+Yf+WNfunaBlt3qz6xLp2neL6MaHa/5otvbQ3XxzM/r+yoM9V9s6ndiAwRAAASaIKC7crncry0tiopLC+iD1S8abKW9WvoNdlYn09PTm3r3HEzREX0pI/MEJR9v+4uxr08AJQwcz+6K/nT81F7KyDqtc0bzbAYHdaGJo++mK/kX6dDRbTRk4Djy9Q6g46f3Uc7lDIrsHked3DpRyql9rT6h9EO33ozMU62uy1oP1H1vgieItV4ltMtWCXRIoAV5cdf9wzYET4kjnRd8Q2XakicPVHsL4ypCU9dKhBtmW35DcCwIgIC+CNKs+x1Npt51Ls3xXIc8PbxpcP+xdOftP6FB/ceY5VL4+vjThFF30R1TH6ToyL5mqVO/En+e1zth9F00oN8oJb7kfDP5fLE9BiqBN/eOn1LS0On6h7Xou0cnT4qPG0Gjh99BnYPCW3SsLRTGoKctXCW00ZYJWNxSpP/ibgqevOBLef0Hs6ljmrvP3nzRhZOu0ISVqLm/CSgHAiBgioC+tUjKtsc92VQbDO2zNyu/oT4aynO6menq4kZR3fvQ3DsW8Vp7tZR9KYMOJn9Hzk7OFBXRh+L7jODAFC5UXFxAR0/sostXLhiqrkFeJ3cP6hObSMGBYVRdXUXnL5ymw8d2kIimwQPGUQgLDSeO5nrtWpGy8EjdiYMnsMgJoJJrV0msQWXXr1HOpXPULbwneXh4UX7BJdp3aLNqY3XVDaqqquTFeG+dNiiwC7c1iQbHj6EL2Wdp5LBpdODIdxTZLZb6cR86uXnQ9fJSOptxjE6lHqKgwFAamzSbyitKycXZjbw8faioOI/buZ1qamvoxo0K1XY5j7F2F169QgPjR3F7w5hPPnUO7srrGVZT1sVUZrjtVuOsaEv/3QmDnlZ0cdAUuyHQAaKoccADUzTbw2VDc50zdV5b2mdIEJnD9dCWGKCtIAAC7UNAE0DaoIt8yrwGS839NNQrR3Wd02Xhxq5mvdjKIpYWF1c3Onf+OBWXFChxcs+sx9liMpyqa6qoouI6de7clVau/gcLhkrdKhpte7LASBgwXkX58+jkRcdO7WExlUURXXvRPK4zwK9zneDgekN2dqVtu1bR9In3U9ewaLpalEc+LJ4kZbMoCguNIqkjryCH21FNl3IzKZjFR2BeQ3Hm6xNIA9l6JJEFw0IjafiQqexOd57u5vP1Z1Ek4k+EjrSliqMQiuibN/sJKi+/phb19fTwoWulReTp6cviayNbiPgcASEseLqqiLOG2r15xwq2Ws2lvrEJVFB4maPUBlEnFnAnT++nTBZGV/KyG7HpqAz957u0QwYq4BrfUVcE57VnAhYVRRI1pTVJTMa64WFbU4fuMUnDR1JJQa2yQunm29q29pKi225zzcXSrRPbIAACjk1AXxhpcz+FirJK64Tv1ieVW3SOcovO6mer76H+PSnUv4fBfcYy+T2bevGzJNJ/N0UmWi5UuLH2tDTfq6vM17nW0sMalRdry9WiXNqxdx3Fxgyk3jGD2IIyS1l3BvQdSRdzztLRk7soKXEaDR88hQ4f3a4sLY0q0smoralR4uXEmX08/2cudesSTT2i+qk1o0QspPB8oxAWNgP7jVbzmrbvWU3u7p2UOMni813iuUF3TH2AQjt3p72HNlGAfwgN5LaIu983Wz4lNxZv7vyjm0TIHTm+g0YmTqfsy+co+cRONb9pUPxousiWo0PHtlJcr0QawNakwquXaceeteTOgrDkWiF99/0XLIBClVueWMYOHNms2uPi7MqCyIPKnF2UyNFv9xY+TvZLuSI+/14WUzLfqQuLsh48V6stoqhX12H8bjFct4ut2paBB113eK0SEUTa36OWh08QAAHzELCsKGrlWhdyYzB0c2gtgvBeRDn8XIpKtFyY2da2tSXH4WbZElooCwIg0BIC2ouY/mBMU/fnlPObKSVzk8FT9Y+cSv2jphjcZywzKKJuj347jJW3tnyv6FNUTS+3uVlV7IqWkXWKvt6wjIYOnsjiYxT1YfHgxu5mTqyYnFkQiKXGif9JAIXePYc0KYqus/VFBMregxvV3ByZlyOWmIzMk8qFTcSIF1tkRJBJ/VoSS83uAxtYFJ2n6ZPup3y2vqz+77vUt/dQ5RYn7TCWSkqvUt7JHBohFqLc83SaXeQeue95tgSV0sGj39Gq9ctoROJU6vfw/1JEt943q6llt7wcWsV9F0vZpDF3s1Byb3CKSrYuXcnPNtlucctLPv79zf4mKfc/Hw7W0NbUXr+beMa39crgeBAwTaBDAi2YblL77uV1AKmirH3PgdpBAARAAATajwCvz+3wSeYRVbNlR6w7lZXlSqSIS13dnB0nNZ9HLDU5bL05zm5hl69kNsnsBgutIp5jU3OzThFX/r5BlDhoAg1ha4/MVVLucNXVDeq6wfN3ioquqLxantcjbRC3NJlfxJpMibQGBxj5IgJOzunK1iSZHyTrU4kLoKqH9zmLGuNUW1tLcs7Kygr+vKHy5Fjd5OPt17x23+xvReV1Obtyz9Otx1q2m7LIWks70Q4QsGUCFrUUtRaUjI6YK91/5zO0YuNrNi+MtLUKdC1oMjrV0b7+5rpOqAcEQMC6CBia2yAtbOr+bMp9rvxGGWXmHW12R+N7JVG+ixPt3We7UecGuQdSTLN7bLygq4s7RbHl5LaJC6lnjwFqvs0ltrScOXtEzZcpLy9TgQOiI/spcXEl76LxyrQ9LDYk4ADLDiU8JFssJ3FsZXJjS4wIrOxL6TdL3xIQIoSq9YSSCBeu5GZZ4x9SToI6iOVJLFMRHGAhnUONy3ypQf3GUFnZNRU0QuYV5XHQBkmq6ppbdd/aunUemavUN3aowXY78bykunpEwImo4v6yEDRHSsveT1vM8M6i7z4nz3pZrgTWInNcJdQBAoYJWFQUiftFa8zKmtuG4S40P1cCLIgV/6sdrzf/ICsvKfO0ZiQ8Ve9eKDfOGdzmpVjl2sqvHJoHArZDwJAgMsfLWeaVYy2CkDR8OS3nBbdtOfLcRbdgemJ2fIv6rVv4lgCo5UhsXTj63KMqoEAer/+zbffXLITOUNaFM2p+zPAhUygooAulnN7LwQiu6lZjcFvqVmLm5l75fp2tPSJSJNhCFK8lJJ9Sxp3n5IR07lZfz6121Wex1DCcdLWSiJLiknyRJRyxLoaGDZ7M/VilhJgIo7AuUeTt5UeZ3KcdPIfJYLp5It16r18vNdruUA48YUiwSTXG2mzwvEYyzfXOItXrP+O1dyhznsNIN5ANAg5HwMXX1/cP5uh1ZKwHZaZWNKuqlgRckAevuYIshLIzekhgd9p6YEWz2mkLhQpKLtDeMytVUzWuwb51Tvfm4mYLHNBGEACB9iGgL4jknrx07Xyz3Zdb0uonFr6m7t+5BRdacphVlQ3r7kXDJ4ZS8p7LrWsXe4mJ61idW9w+DqiQpl7+JbDB/sOblYi5UnCR8gpzeG7PJRWqej8HIDhnciFTJxL3sfM8RymVLU1Xi/Kpgl3XzvMirid4cdXU9GQV4e1KQbYKjZ3CkeBS04+owAjiJpfG4bLPnjtKpddLeA7PNTpx5oA6n1iQSkoK6SR/z8o+o9zgTpzZr4JBaOc7k3aYctmKVcEugBIcIo3rOXp8J+dlKVc9mWd07OQe2nd4Ix1J+Z61TLXqowSDkPaRU636LovASghxrV5pt9Ql850Mtvtqbl1/uW+6/ZV+Fd50BWzpBQoI9qDw7n608Yuslh5qtLz2jBfLkfZs1571eMYbxdamHS15n23TiXCw1RFwCg8PN8fACI2Z4U/fry9qVgclmpyu25exg8wdSe2lx2x/lNEYK8k39PKC0SRTxLAPBECgKQJ/X3TrBc8c1qGmzmdsv1j65/Gi2y+8Nd9YEZvIH5TElqKX4unDJcltbq/MgXF1dSVXdm0Ty4hukhDXsphpKbufuXEZcUsbPGCsbhG1Le5yEnZ79/5vGu3TzZD6VEQ3dR6zvDboVs+ht7l+duWQ+UMipiS5sEtdJ+6DhBWXsN6tSe3dbt02RcUG0JCk7vSLhTt1s822rf/u9MQ7dQOgZjsBKlIEWvI+C2T2RcCi7nMaOllDR/+PW9unfbbHw/f5VjxMQwK7UWzkECooyqFTGQe15hn87NEtnrqF9KS0C0fpUl6GwTLtmakJIM28rs07as9zom4QAAH7JSADLVqSQSrtHqPlWfJTXOZa4zbXL2Y4RYTF0ZZ9y9nCYtqbYfLweynv6kVKObtbzXOxZP9acy5xOZNAA1qwAd06ZJ5OaVmJypL5OrLo6ZD+43SLqG0JinAq7VCTokjqqwt40KgKs2RUcf1VZcUN6hIh1NZztne7GzS4nb/Iu5PuIIX8fXbk32Q7dxfVg4DFCXSIKJJeyh+3vq+s5IsYqvvsuHk/Ev1G86uWh+mYwXPo8OmtTYqi2KgEGtR7HOUXX2okinTrVB1sp//kBlkvinh+ERIIgAAItJaA7sCKLNZqC0n/Xiv377DOMbT94FdNiqIZ4x6h7w+tohPp+zhsdlV9d/XrrN9hIxsimtLPn6CVa//RqMUSaa6oOK9RPjKsk4C8I2nPeOtsIVoFArZLoMNEkSCTFZmtJSCArL8wNek+ds94iid1+tPBE9/S4VNbaWi/KTQ4bjwl9ptMfaKH0tod79JAFj6zxy8iX68Ausy+7V98+zcODXqdZo37CT98oyihz0T6euvbtGbbMpo+6gGaM2GRWm37eNoeWsGThI+f3dOuvzG6N02MJLUralQOAnZLQAatdN2c5X5tzcnb049e/OlyjsjWVw1qLfvyd+Tt6U/jh97Da9h40L/+cIj++vFjdOFyGj37w7fZehTLLltudOTMNvrrR4/Rk/e+ThGhPWnh7b+ge6Y8SYv+OJx8+PhfP/QeRXLZGp7Fv3LT3+ir794kiYRmS0lc5AoKc9WPLbUbbW1MQHfgU8QRLEWNGSEHBFpLoENFUWsb3R7HxXQfQPE9R9I3uz+hVVveVCtmq7UXSurWXjiWtpM27flEhTYt5wmoR9hyJBM675r4GMVFJtBnG//Kx35Mg2LH0kb+PJa6k/rHjqa+McNo5bdvUDq71M2Z8BgN6TOp3UVRe/BBnSAAAo5LQFznrD1NSfoBR1oLo0f+kKiaWsMT8uUe3Y2tRGEhPeiNz56m/Ks5vK+WPvvmL3SSrUExEQPotw+/R12CI+nNz5+lV3++mvYe+6+6h5ddL6YHZv6eyiuu0aOvjKSRA++ghdN+Qeu/f8/mRJG1Xzu0r/UEZPDC2gcsWt87HAkCliUAUXSTd07eebqcn0W38YM1wCeI1u98n85nn6JK9kGXUUGJqCM/kioqSqlnt/480tiHuoXG0sXctLoF9FgsSflr14uovLKMugRFUa+IQfzAjeKH81wK8Atht7pzatRSFqVrr6QbkUbX/aW9zod6QQAE7I+A7r1D955irT0Vy/7cSU/Qy49/QV9ueYPd4L5Wa/fIPKIbNyqp5FoBzxOqW+izpLSApo9+gPy8g8mFrUVuru4sfkrVZH75LObVYcWFWrwCXFxc6DcPvqesSuJRINana2VNh7e2Vk5ol+0TkEEKXSuu7fcIPQAB6yAAUXTzOhSxRejzb/4fzxvaR7PGP0oPzHqBlrP1Rz9Fde3LrhWL2U0umlJSd3HY0jJe7K7KYGQciQqUeekMbdn/OYcErYvglMcjle0piPTbi+8gAAIg4AgEMnNO0c//byK7Nj9Ki+a+SoF+XWjttncbdX3CsHn08J0v0v6UjTxIdV6JHxFOhuKpdWK3u43sPXD87C6+x9fNMcorbMYiqI3OigwQAAEQAAFrJ1C3rLO1t9IC7fP08OHF6Dzp0Mkt9O2+/5Abjx52CY7mkcZKtdK2RydvcuZVsLuGxKiH7a7kNbR536dUVJLHvuY1ahVw8dt2d/VQfujOzs4cNaeEvDx81cJ6p87tZ4vSWQgiC1xLnAIEQMDxCAT5h6n767/XvcLuyinKrVnCMZex5cePrf8SLMHF2ZUGs/XnavEV+mD1i3Qu+xhb+TmkQnVduGfxCgj2C+Uw126qfC4LoH49k7i+YyT38JKyQhZPhuST4/FGj0EABEDA3gjAUnTzisZFJdLdU35OfXoMZYFTQzsPr2af871qb3FpHv1o1u9oWPxt9N2B5ZSZc4J+cPuvaB5bjMTqs+foevLy9FWucR4srH710Lu0df8K2rDzQ5K5SA/NfoEeufNlyi+6RF9t+Tt9s+tje/s9Qn9AAARAoEMJzBq3iKaP+ZESPuL69uf3H1auz8k8/3P66B/SBy8f4/mib9F3+5fz3M6J9MFLR9lNjl3qWBQF+YZQzpV0OnLqO7pz4k/pttE/opfevpfe/fK3HGjhfXqfy0pKzTxCLy+7r96VukM7jJODAAiAAAiYlQBE0U2cR05v4zCse9nHPIhHDav4YVlnAZLdy778PX287n9J1nO4zpNut+5fSR+tfZXn69aqVb95AFK5YMgk3tTMH6kgDRU8p6iSRx1lhHH5xtfUPCKxHDW1ToZZry4qAwEQAAEHIfDhmpdUJFCZJ3Sl8EL9vfZY2i5a9NJwvgd7UhHf12Vh0IdeGHRzblAhW4ScVZ4Iqc/YhXoDzyet4rlHZeUl6r7+yIsJFBwQzkKolO//dXkOghTdBAEQAAGHIgBRpHO5xeqTdzVbJ6duU6LQ3agqqM+Xh6pMxtUSP0vrk4gh+dFNukEadPOxDQIgAAIgYD4CEgDBUBAEETjyoyVxdZZgC5JqOUqdbiriIAv6qS5qnX4uvoMACIAACNgTAYgie7qa6AsIgAAIgAAIgAAIgAAItBMBWf/SmhYQlmiMS9cuMEtvEWjBLBhRCQiAAAiAAAiAAAiAAAjYLwFZF8uaBJGQlvD0ItTMkSCKzEERdYAACIAACIAACIAACIAACFicgO66em05OURRW+jhWBAAARAAARAAARAAAYME+vcaaTAfmSBgjQQwp8gar4oNt8nPJ5hGD55Fw+OnUXllqVq4dn/KJqM9khvmvKlP0wtvzTdaBjtAAARAAARAAAQ6noAsWzJx6Dzq1iWWFzXeQ5v2fEKmFjSez893SSlpuzu+8e3cgqSBt9OUET9QkS23HlxJ3+z8yOgZ8e5jFE2H7oClqEPx29fJJbRtZFhvmjhsPmVdOk2enXxowW3PUo9u8UY7KjfMFZteN7ofO0AABEAABEAABDqegCsvaj991APk5tqJcnLTadLw+TQ24U7y9Qo02Dh58ZfgvI4giITJQ3NepIzsk5Sbn0mL5r5CA2NHG+QimZpYNFoAOzqEACxFHYLdPk8qocrl5ver12eoDsb3HEGPzH2VIsP70LmLxxt1Wm6Y8fyTAitRIzbIAAEQAAEQAAFrIiDrdy355Mn6Jv3ygXeoW+cY8vbyp5Kywvp8bUNe/Jc7yKCnrEH56MvDta5TYr9JFBc9nI6m7qzP09/AgLA+kY7/DktRx18DO26Bk1rwtrA412gfHWEEyWjnsQMEQAAEQAAEbJSAM3uHFJbkqUXsDXVBBj0dNTk5udDlgkyD3XckC5pBAFacCVFkxRfHlpvm6x1IsVEJVHq9mE5nHDDYFbjOGcSCTBAAARAAARCwagK9IgZSgF8XSss6QsUsjPSTvPjLoKcjDnwm9J1Irq7udOD4Rn0s6ru8+8h8LCTrIwBRZH3XxOZb5OTkxHOL4mjskLtoZ/Jqqqi83qhPGClphAQZIAACIAACINAiAuYKRdySkzo7u9DsCT+ly/kZlHU5laprqhod7qiDni4urvT4va/R2u3/orLya424aBkn0u0/8ITWV1v6hCiypavVzLamZt/6Y5NFrSydunOwhbmTn+RgC6do855PDZ5ebphIIAAC1ktA92UrNQejmtZ7pdAyRyPQ0X+Pd018nLqG9qLNez+l7Nw0g/jVfGEHiDin3/mn73uTB4LL6ZN1r/IuCTPRMGFAuCEPa/sGUWRtV6Qd2iMrEFsqhQZF0uxxi0ii1Hy09hX1KaNKhhImGRqigjwQAAEQAAEQsE4C4xLn0piEObRm2z8p/cJxcuFnvXiH6CbNdU43zxG259/2DInr3B/eXqC4iNVIPzmqBU2fg7V+b3zFrLWlaFeLCKw/+BrNSHxGHSMjvrrWoxZV1ILCPl4BNGrwHSpMpxy27Pn9dK3sKq9j8Bn9e90r9TVhpKQeBTZAwCoJyECKrpXZEvcPqwSBRoGAFRJYf/D1+ue7POfluyWSzCO6Y+zDaumNny9cok559kIKvbPyNzy3KLm+CfLi7yhR57ROD44bRwumPcVfneit5+oizh05vZ1efud+zrtlMVLBJxwkIp/GpqWf6TmHKCVjG12vKK4/dErCjynYr3v99/bagChqL7JWVK+lbpoigFZteUv9mOq+I94wTfHAPhCwNgIzEuThXpdSs3dpm/gEARCwEgK6A58zEp+2iDBKyzpKv14ys0kCjrjUhgigu5+NNMlGs6A5YvAJk2D0dqbnHK4XROdyjlDK+W1UUHyR7hn3HIUEROuVNu9XuM+Zl6fV1CYjR3LT1NLimZ9rmx3+6chhOjscPhoAAk0QkBcsXSvR+kN1I8JNHIbdIAACHURABj7l79Yakvbibw1tsbY2wHWu7oqIJeh4xlZavft12n96Tb0A0q6XWIUWTHiBhsXNokuFaRQe1JM8O/lqu9v1E5aidsVrPZXLS44Io6VrF3Roo7QbJkZKOvQy4OQgYJCAvFhpbrdSQAZW4DpnEBUyQaBDCWguc9rfq/ap5XdU4+AJYpy8I1rQNBqbD/2LDpxeq6w+SuS4+1FM+GA6kbGdln71gBI+8VHjaNbIp5WbnAinv3H+9GE/o8qq8kbCSavX3J8QReYmakX1aTdH7WYpwujvi7JYGM3vsBcd3DCt6BcETQGBmwS0UWbtXiHZ4jan3UMACgRAwPoIyN+nzBnWLLvy9yvfxbrbUYMZjvzib+o3RBsQNlXGHveJuFmzeynlF1+gKYkP0+K5H7LVx69RV7V5RCKeYsIT6JNvn1OCaPzA+2jn8RUQRY2IIaNVBLSXGt2XncUzl6u65KXHVGjP3KJzlFt01uB5Q/17Uqh/D4P7TGXKDfP2glqK9E8yVQz7QAAELERA996gnVIsRNq9Q8vDJwiAgPUREO8P8QLRhJHyCuEBUG0uoKlnfErmZqMd6h85xeg+Yzs6eRFVlJHVuPIZa2dT+ZH+7pQZst5osdawSRo+kkr43UcbgDJauR3tEIHz+baXKaHnbUoMmQqUIEJIfpate/KW5Sh6vBJQMeFDqEwn6EJ7IoKlqD3pWknd8nIjN0aZPK3dOKVpsq37Xb+5uUXptOXoP/Wz1ffQgBiaNOBRg/uMZbrfvGFG+Y+iqETLr59krF3IBwEQuEUAgugWC2yBgC0QEGGk7/qqPdu1T0P98HD3ptyr6YZ2UWKvOdSFBz9bkoIiiK7liyiqi3zbkmOtrWxK5yyjTWpN/8J7EeWcJod69/n28Ht095hfk1h7DFmH9AGruUbnt9LofvfQobMbed7RNurMEec0waRfvj2+QxS1B1UrrFNM6Uv5R26cuuZ2SzbVJ7juhmnJc+JcIAACzSMAMdQ8TigFAtZIwNjgp6XaKoOeYikqMK4lLNUUqzuPNiBsdQ1rxwaJlWgwW4gkWEJzBZE2h0hEVCy7zMkcpHi2FkWE9GvHljasGqKoIQ+7/6a5xGgLuqo1jNiKJJ+GUmbeMUPZJPnllaUG9xnL/HHcM/TvVbci4hkrh3wQAIH2JyDWY+3vv6PmH7R/L3EGEHAcAtrgp/RY9xlvjIApF/kNB5e063bwSQAACwxJREFUyEU+vlcS5VQ40d6Du42dzmbyI3uz+5yRdx/phAwgtSTdf+cztGLja8q1sCXHWWPZ5gyqiyBSc4iaubaQblAFzaokEegkHPe3XJcWfMESPCCKLEHZCs+hvQTpf7akqZlXDAsmQ3Vokww1UWaoDPJAAAQsS0D7+7fsWXE2EACB9iag/W1rny09X0ue71J30vDlasFWe4gsO6aLP2VeKTKKrCVs5N1HLGhf7bDMArtGG22mHSK2F/PUC1NJxExcZBJ5GQiooH+cIUGklZmc8JAK0iDzidjRyCIJ6xRZBDNOgvj8+B0AARAAARAAAfsjIC/+tdwtexBE7XF1HI1L+qVDFBYoaws1jjKny9eUIJJyMpdI1im6bqEgC3JOiCKhgNTuBFSYzjTbN6u3OyicAARAAARAAARsiEC/mJFGW+vq4kbRXfvR/Xc816iMEzlRaFAkPTDreXJ369Rof3MyfL2DaProB6l/rGnrRXPqao8ypgaEPTp505ghs2lswp2NTu3s5EzD+0+jicPmkzBsTeoZMZAmDV9A3cN6t+bwVh1z7tIRFkS92iyItJPHhIkwOmsxYQRRpJHHZ7sSuPtZDkuDBAIgAAIgAAIgYFcElvN8mRfemm+wT66u7hTeOZoS+01qvN/Jifx9Aimh30Ryc22dKPLy8KWRA++g2IiExvVbQc7zzMWYpUii/8VGDGHR2L9RS52dXSgiLI56dh9ALi6tm+nSLbQXDet3m+Lf6ATtlJFxKZnCgmKadJ1btv7J+nWImrIotVNTDVbbOtIGq0ImCIAACIAACIAACIAACBgm4MRCqLZWnO1an8xRR+vP3n5HmqNf5qijLT2MDhtESX3vatJS9JMZb1B4UNMWJZlX1JxybWmz7rEQRbo0sA0CIAACIAACIAACIGBWAl0796C3f7+X/HyC6FjqTvpo7SuUndtwcXh5oQ/ncovve4NiuvWnsvIS+v7QKvrX1y+QWE7iY5LoiR+8TkG+oXSlMJveW/U8nc85Vd/OkMDu9JO5r1Be4QV658vf1edb+8bUpIXs5jaPampr6Nu9n9N/d35AxbLgk05ydnZmV7s5NP+2X7DLYTe6nJ9Jq777B23Zt5zEWjZ99AM0Z8KjavvQqe9o+Td/1TmaA2EMnEGThi2gPcc28DGfNdhnzi89wgY3qzqZL9Sc1NxyzamrOWUgippDCWVAAARAAARAAARAAARaRaCUBc7XW9+m5DM76NG7/0Szx/2E3l75mwZ1ycv98AHTeH6RB/166UwK8O1Mj97zZzp78Zg67naeO5R58SQ9t3I2Vdwop6qqSpI5RZL8eCHE2eMXUZB/F/rbZ4sb1GvtXy5cTqWVm/9GPl4BNHn4vXSFF3v6bv/yBs3uGtKTBvceTyfP7qa/fPAvGj14Do0dcheLo/Pk0cmHBsaOoQ3ff0gb93xMN6oqqKLyOnVl9zlJcVFDKZJd8bLz0mlX8poG9eJLQwKYU9SQB76BAAiAAAiAAAiAAAiYkUDRtTx+aX+fCosu0c7k1RTdLb5R7V6e/jSg52hKSf2e0i8co8xLZ+gcf/aNHsbuWD7UrUsv2rz3U7YSXVSWFLEkSZKABSMGTKdBfSbQ3/7zFF0rvdqobmvOOHnuAB06uYVOZxyk7CvpJBYv/SSixtcriI6f28vWsZOUlnVYiR8RS10CIzkQwTU6cGIj5V/NYTYFShRJHSHBERysYR6VlBXS2u3vUnlFy9aX1G+HvX+HKLL3K4z+gQAIgAAIgAAIgIAVEJDACyEB3enGjYpGrXFxdiURRsU3RY1EYHN39yKxMolrney/WnKl0XE8SYlqqqvIg8vGRSU23m8jOb5e/uTD/a+qvtGoxZ24b07sQqeJGjfmKKnixnVycXVlnpWKk/6BtTU1VFNTrYRWVw54gWSaAESRaT7YCwIgAAIgAAIgAAIg0AYCEn67k7snRbHFI4Ej0cm8F0kSdMHZyUVZgioqy9gdLENFqhNXsu5dYtXLvFhAqln0yJwbmTsj4sjTw4f82b1OUjkfJ3NlNnz/Ad01+XGKiRig8m3lP5kvJP3tzy5wPt6BlJF9nNd94n/MRsSPuBMWFOWo7V4RgynQrwtFhfdTAuksW9LEShTBYbeHxI1XZaQucUWUlMdWtQ07P6TC4ss0ecRCCg2OtBUsHdJOiKIOwY6TggAIgAAIgAAIgIB9E6ipqVIv7eEhPeiTV0/T/zz6CZWwe9e3+/7DL/01VFZRRt6efvTq4tU0ctAMWr39HQrkQAofvHSUfvngO3Th8hk6kb6P8lkUfLF5qQoY8Nmfz9Ibv97OLnO334RXS9fKimj7oS8pK+c0PXbP/ymXOmsnK3N/RNDdMeYh1d+7Jz9BqecPcwjvXUoElpYXMZNZ9KsH/0VuvI7T3mP/pSkj7qVlz++nqSN/QGkXkuni5TQ6fPI7On52F6/39D/06Z9S6TcPvUe9IusCHoi4yrp0WrHpEhxN00c90Oo1oaydpzna5xQeHt622Ig3WzFmhj99v77IHG1CHSAAAiAAAiBglwQGJQXTEy/F04dLku2yf47cqajYABqS1J1+sXCnI2Mw2HeJHifWi0oWApUcBEA3iSXE36czz4fJVtYgKStCSdy+Sq8X6xZVlpAAv9Cb82bKGuwz1xdLv89K/8UipAWP0PohLoM+ngHKba6ktEBZjqSsWNwqOdCEBFPQTd7seieL4F4ru8rzjSp1d5ltO7brSFo8s2EQCLNV3oaKUrN30dK1C9pQQ92hiD7XZoSoAARAAARAAARAAARAwBgBETjysm4oyQv+FQ6jrSUpW1JaqH1t8Ckv+1cKbpVtsNNGv0j/5Uc/ifucBEjQTcbKSpnS60X8o1sa2y0lAPe5lhJDeRAAARAAARAAARAAARAAAbsiAFFkV5cTnQEBEAABEAABEAABEAABEGgpAYiilhJDeRAAARAAARBoJYHkPfkUzXNPkOyPQI/euK72d1XRI0cigDlFjnS10VcQAAEQAIEOJ/DRklP0wpvjO7wdaID5CSDIgvmZokbrIZCavZvWH3yNZiQ+Yz2N4pasP7TELO1B9DmzYEQlIAACIAACIAACIAACtk7A0tHnbJ2XPbXfrO5z/sEwPNnTLwf6AgIgAAIgAAIgAAKOQgDvsY5ypQ3302wqJjO1nAaM8DZ8FuSCAAiAAAiAAAiAAAiAgJUTkPdZJMckYDb3OcfEh16DAAiAAAiAAAiAAAiAAAjYOgGzus/ZOgy0HwRAAARAAARAAARAAARAwPEIQBQ53jVHj0EABEAABEAABEAABEAABHQIQBTpwMAmCIAACIAACIAACIAACICA4xGAKHK8a44egwAIgAAIgAAIgAAIgAAI6BCAKNKBgU0QAAEQAAEQAAEQAAEQAAHHIwBR5HjXHD0GARAAARAAARAAARAAARDQIQBRpAMDmyAAAiAAAiAAAiAAAiAAAo5HAKLI8a45egwCIAACIAACIAACIAACIKBDAKJIBwY2QQAEQAAEQAAEQAAEQAAEHI8ARJHjXXP0GARAAARAAARAAARAAARAQIcARJEODGyCAAiAAAiAAAiAAAiAAAg4HgGIIse75ugxCIAACIAACIAACIAACICADgGIIh0Y2AQBEAABEAABEAABEAABEHA8AhBFjnfN0WMQAAEQAAEQAAEQAAEQAAEdAhBFOjCwCQIgAAIgAAIgAAIgAAIg4HgEIIoc75qjxyAAAiAAAiAAAiAAAiAAAjoEIIp0YGATBEAABEAABEAABEAABEDA8Qj8f69sT8dLoXKBAAAAAElFTkSuQmCC"
    }
   },
   "cell_type": "markdown",
   "id": "35",
   "metadata": {},
   "source": [
    "![Screenshot 2025-10-16 at 15.16.18.png](attachment:6f8c87c8-d417-40d4-a1b5-6ec5d0e85724.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "36",
   "metadata": {},
   "outputs": [],
   "source": [
    "with ExecutionSession(qprog_be, execution_preferences=execution_preferences) as es:\n",
    "    res_be = es.sample()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "37",
   "metadata": {},
   "source": [
    "We use the predefined function for the post-selection, and the utility function for examining the result with respect to the expected one:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "38",
   "metadata": {},
   "outputs": [],
   "source": [
    "state_result_be = get_projected_state_vector(res_be)\n",
    "expected_state_be = matrix @ state_to_evolve"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "39",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "expected state: [0.51064942+0.j 0.12407055+0.j 0.071609  +0.j 0.18229694+0.j]\n",
      "resulting state after rescaling: [0.51064942-7.89444035e-17j 0.12407055-8.14717419e-17j\n",
      " 0.071609  +1.39954009e-16j 0.18229694-2.96355950e-16j]\n",
      "========================================\n",
      "overlap between expected and resulting state: 1.0\n"
     ]
    }
   ],
   "source": [
    "renormalized_be, overlap_be = compare_quantum_classical_states(\n",
    "    expected_state_be, state_result_be, be_scaling\n",
    ")\n",
    "print(\"expected state:\", expected_state_be)\n",
    "print(\"resulting state after rescaling:\", renormalized_be)\n",
    "assert np.linalg.norm(renormalized_be - expected_state_be) < EPS\n",
    "print(\"=\" * 40)\n",
    "print(\"overlap between expected and resulting state:\", overlap_be)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "40",
   "metadata": {},
   "source": [
    "***\n",
    "***\n",
    "## Hamiltonian Simulation with GQSP\n",
    "\n",
    "Comment: The current implementation assumes that the block encoding **unitary** is also a Hermitian matrix (see third item at the Technical Notes below).\n",
    "\n",
    "The GQSP method applies a polynomial transformation on a *unitary*, see <a href=\"https://github.com/Classiq/classiq-library/blob/main/algorithms/quantum_primitives/gqsp/gqsp.ipynb\"> GQSP notebook</a>. The idea is to work with the walk operator, applying for it the polynomial transformation\n",
    "$$\n",
    "P(z) = \\sum_{k=-d}^{d} i^k J_k(-st) z^{k},\n",
    "$$\n",
    "which approximates $e^{-ist\\cos(\\theta)}$ according to the Jacobi-Anger expansion, see Eq. (1) above.\n",
    "\n",
    "To see why it works, we use the second property of the walk operator in Eq. (7), to write\n",
    "$$\n",
    "W = \\sum_{\\lambda} e^{i \\arccos(\\lambda/s)} |\\varphi^{+}_{\\lambda}\\rangle \\langle \\varphi^{+}_{\\lambda}| + e^{-i \\arccos(\\lambda/s)} |\\varphi^{-}_{\\lambda}\\rangle \\langle \\varphi^{-}_{\\lambda}|,\n",
    "$$\n",
    "where $\\lambda$ are the eigenvalues of the Hamiltonian $H$. The polynomial transformation works on each eigenvalue and gives\n",
    "$$P(W) = \\sum_{\\lambda} P(e^{i \\arccos(\\lambda/s)}) |\\varphi^{+}_{\\lambda}\\rangle \\langle \\varphi^{+}_{\\lambda}| + P(e^{-i \\arccos(\\lambda/s)})|\\varphi^{-}_{\\lambda}\\rangle \\langle \\varphi^{-}_{\\lambda}| = \n",
    "\\sum_{\\lambda} e^{-i \\lambda t}\\left( |\\varphi^{+}_{\\lambda}\\rangle \\langle \\varphi^{+}_{\\lambda}| + |\\varphi^{-}_{\\lambda}\\rangle \\langle \\varphi^{-}_{\\lambda}|\\right).$$\n",
    "When this unitary is applied on some state $|\\psi\\rangle|0\\rangle_m$ we get, approximately,  \n",
    "$$\n",
    "P(W)|\\psi\\rangle|0\\rangle_m \\approx e^{-iHt}|\\psi\\rangle|0\\rangle_m.\n",
    "$$"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "41",
   "metadata": {},
   "source": [
    "Applying a GQSP requires calculating a series of GQSP angles, which rotate an extra qubit. For a stable convergence of this classical preprocess, we need to take some scaling factor $\\beta_{\\rm  gqsp} P(z)$ with $\\beta_{\\rm gqsp}<1$. Below we take $\\beta_{\\rm gqsp}=0.99$. \n",
    "We use the `gqsp` function from the open-library and the classical `gqsp_phases`. The resulting unitary of the GQSP routine is a $(\\beta_{\\rm gqsp}^{-1}, m+1,\\epsilon)$-encoding for the Hamiltonian simulation:\n",
    "$$\n",
    "U_{(\\beta_{\\rm gqsp}^{-1}, m+1,\\epsilon)-\\exp{(-iHt)}} = \\begin{pmatrix}\n",
    "\\beta_{\\rm  gqsp}\\exp{(-iHt)} & * \\\\\n",
    "* & *\n",
    "\\end{pmatrix}.\n",
    "$$\n",
    "where $m$ is the block size of the Hamiltonian block-encoding."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "42",
   "metadata": {},
   "source": [
    "### Implementation"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "43",
   "metadata": {},
   "source": [
    "We find the GQSP angles, and define a Quantum Struct for the Hamiltonian evolution function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "44",
   "metadata": {},
   "outputs": [],
   "source": [
    "from classiq.applications.qsp import gqsp_phases\n",
    "\n",
    "GQSP_SCALE = 0.99\n",
    "t0 = time.perf_counter()\n",
    "gqsp_degree = get_jacobi_anger_degree(EPS, EVOLUTION_TIME * be_scaling)\n",
    "jacobi_anger_poly_expcos = [\n",
    "    GQSP_SCALE * jv(k, -EVOLUTION_TIME * be_scaling) * (1j**k)\n",
    "    for k in range(-gqsp_degree, gqsp_degree + 1)\n",
    "]\n",
    "jacobi_anger_phases_expcos = gqsp_phases(jacobi_anger_poly_expcos)\n",
    "classical_preprocess_time_gqsp = time.perf_counter() - t0"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "45",
   "metadata": {},
   "source": [
    "We define a Quantum Struct for the GQSP block-encoding:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "46",
   "metadata": {},
   "outputs": [],
   "source": [
    "class GQSPBlock(QStruct):\n",
    "    block_ham: QNum[block_size]\n",
    "    block_gqsp: QBit\n",
    "\n",
    "\n",
    "class GQSPState(QStruct):\n",
    "    data: QNum[data_size]\n",
    "    block: GQSPBlock"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "47",
   "metadata": {},
   "source": [
    "Next, we construct a quantum function that gets the GQSP parameters, the `gqsp_phases` and `negative_power` defining the polynomial, and the block-encoding function as a parameter. A negative power is needed, since the approximation of $e^{-it\\cos(x)}$ in Eq. (1) is a Laurent polynomial that includes negative powers as well."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "48",
   "metadata": {},
   "outputs": [],
   "source": [
    "@qfunc\n",
    "def gqsp_hamiltonian_evolution(\n",
    "    gqsp_expcos_phases: CArray[CArray[CReal], 3],\n",
    "    negative_power: int,\n",
    "    be_qfunc: QCallable[BlockEncodedState],\n",
    "    state: GQSPState,\n",
    "):\n",
    "    gqsp(\n",
    "        u=lambda: walk_operator(be_qfunc, [state.data, state.block.block_ham]),\n",
    "        aux=state.block.block_gqsp,\n",
    "        phases=gqsp_expcos_phases,\n",
    "        negative_power=negative_power,\n",
    "    )"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "49",
   "metadata": {},
   "source": [
    "The code in the rest of this section builds a model that applies the `gqsp_hamiltonian_evolution` function on the randomly prepared vector $(\\vec{\\psi},\\vec{0})$, synthesizes it, executes the resulting quantum program, and verifies the results. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "50",
   "metadata": {},
   "outputs": [],
   "source": [
    "@qfunc\n",
    "def main(data: Output[QNum[data_size]], block: Output[QNum[block_size + 1]]):\n",
    "    state = GQSPState()\n",
    "    allocate(state)\n",
    "\n",
    "    inplace_prepare_amplitudes(state_to_evolve, 0.0, state.data)\n",
    "    gqsp_hamiltonian_evolution(\n",
    "        jacobi_anger_phases_expcos, gqsp_degree, be_hamiltonian, state\n",
    "    )\n",
    "\n",
    "    bind(state, [data, block])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "51",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Quantum program link: https://platform.classiq.io/circuit/38B05mpLfgbaxbebXWsyk83fUTK\n"
     ]
    }
   ],
   "source": [
    "# Synthesize\n",
    "qprog_gqsp = synthesize(main)\n",
    "show(qprog_gqsp)"
   ]
  },
  {
   "attachments": {
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"
    }
   },
   "cell_type": "markdown",
   "id": "52",
   "metadata": {},
   "source": [
    "![Screenshot 2025-10-16 at 15.18.21.png](attachment:1a3b06c6-f404-4e65-96a9-bf8eeed94213.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "53",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Execute and construct the projected statevector\n",
    "with ExecutionSession(qprog_gqsp, execution_preferences) as es:\n",
    "    result_gqsp = es.sample()\n",
    "\n",
    "state_result_gqsp = get_projected_state_vector(result_gqsp)\n",
    "exp_scaling_factor_gqsp = 1 / GQSP_SCALE"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "54",
   "metadata": {},
   "source": [
    "For the verification, we call the pre-defined function for calculating the overlap:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "55",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "expected state: [-0.72880038-0.06293144j  0.39779918-0.13338688j -0.07939772-0.47893439j\n",
      " -0.20398188+0.10752969j]\n",
      "resulting state after rescaling: [-0.72880038-0.06293144j  0.39779918-0.13338688j -0.07939772-0.47893439j\n",
      " -0.20398188+0.10752969j]\n",
      "========================================\n",
      "overlap between expected and resulting state: 1.0\n"
     ]
    }
   ],
   "source": [
    "renormalized_state_gqsp, overlap_gqsp = compare_quantum_classical_states(\n",
    "    expected_state, state_result_gqsp, exp_scaling_factor_gqsp\n",
    ")\n",
    "print(\"expected state:\", expected_state)\n",
    "print(\"resulting state after rescaling:\", renormalized_state_gqsp)\n",
    "assert np.linalg.norm(renormalized_state_gqsp - expected_state) < EPS\n",
    "print(\"=\" * 40)\n",
    "print(\"overlap between expected and resulting state:\", overlap_gqsp)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "56",
   "metadata": {},
   "source": [
    "***\n",
    "***\n",
    "\n",
    "## Hamiltonian Simulation with QSVT"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "57",
   "metadata": {},
   "source": [
    "The QSVT is a general technique for applying block-encoding of matrix polynomials via quantum signal processing. Refer to Ref. [<a href=\"#ref-grand\">1</a>] and the [QSVT notebook](https://github.com/Classiq/classiq-library/blob/main/algorithms/quantum_linear_solvers/qsvt_matrix_inversion/qsvt_matrix_inversion.ipynb) for more information about this generic and important subject. The QSVT supports polynomials with a well defined parity. Thus, we can apply two QSVT blocks- one for approximating the $\\cos(xt)$ function and one for the $\\sin(xt)$ function- and then, for the finale, we construct an LCU to get the block-encoding of the sum $e^{-ixt} = \\cos(xt)-i\\sin(xt)$. For a realization of such a model on real hardware, see Ref. [<a href=\"#ref-kikuchi\">7</a>].\n",
    "\n",
    "The QSVT routine requires pre-calculation of rotation angles on an extra qubit. For the stability of this classical preprocess, it is essential to work with $\\beta_{\\cos} \\cos(xt)$ and $\\beta_{\\sin} \\sin(xt)$, where $\\beta_{\\cos} , \\beta_{\\sin} <1 $. Below we take $\\beta_{\\cos}= \\beta_{\\sin}=0.99$.\n",
    "\n",
    "To apply the LCU of the even and odd QSVT routine, we call the `qsvt_lcu` function from the open-library, that performs a non-trivial, optimized `select` operation for the two QSVT calls, as described in the figure below.\n",
    "\n",
    "Overall, the LCU of the two unitaries: $U_{(\\beta_{\\cos}^{-1},m+1,\\epsilon)-\\cos(Ht)} - iU_{(\\beta_{\\sin}^{-1},m+1,\\epsilon)-\\sin(Ht)}$, gives a $(2\\beta_{\\cos}^{-1}, m+2,\\epsilon)$-encoding for the Hamiltonian simulation:\n",
    "$$\n",
    "U_{((2\\beta_{\\cos}^{-1}, m+2,\\epsilon))-\\exp{(iHt)}} = \\begin{pmatrix}\n",
    "\\frac{\\beta_{\\cos}}{2}\\exp{(iHt)} & * \\\\\n",
    "* & *\n",
    "\\end{pmatrix},\n",
    "$$\n",
    "where $m$ is the block size of the Hamiltonian block-encoding."
   ]
  },
  {
   "attachments": {
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"
    }
   },
   "cell_type": "markdown",
   "id": "58",
   "metadata": {},
   "source": [
    "![Screenshot 2025-10-13 at 12.07.04.png](attachment:4ce7aa7b-2df9-4419-8180-0be0dda27843.png)\n",
    "<center> On the left is the naive LCU design for the select of two QSVT calls (taken from Ref. [<a href=\"#ref-grand\">1</a>] ), where each call is applied using a repetition over the usual QSVT step on the upper right panel, where each polynomial is acheived by its own series of angles $\\phi_r$. However, one can design an optimized select operation, by selecting the rotations within the QSVT steps themselves, as shown in the lower right panel.</center>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "59",
   "metadata": {},
   "source": [
    "### Implementation"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "60",
   "metadata": {},
   "source": [
    "We start by calculating the rotation angles for the sine and cosine polynomial approximations, adding a factor of $0.99$."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "61",
   "metadata": {},
   "outputs": [],
   "source": [
    "from classiq.applications.qsp import qsvt_phases\n",
    "\n",
    "t0 = time.perf_counter()\n",
    "exp_coeffs = get_exp_cheb_coeffs(EPS, EVOLUTION_TIME * be_scaling)\n",
    "\n",
    "COS_SCALE = SIN_SCALE = 0.99\n",
    "poly_even = np.real(COS_SCALE * exp_coeffs)\n",
    "phases_cos = qsvt_phases(poly_even)\n",
    "\n",
    "poly_odd = -np.imag(SIN_SCALE * exp_coeffs)\n",
    "phases_sin = qsvt_phases(poly_odd)\n",
    "\n",
    "classical_preprocess_time_qsvt = time.perf_counter() - t0\n",
    "\n",
    "assert np.abs(len(phases_sin) - len(phases_cos)) <= 1"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "62",
   "metadata": {},
   "source": [
    "Next, we use Classiq's `prepare_select` function, which allows a flexible implementation of the select operation in LCU. In our case we take this operation to be the `qsvt_lcu` function. The coefficients $(1/2,-i/2)$ are chosen to get the desired $\\exp(-ix)$ polynomial."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "63",
   "metadata": {},
   "outputs": [],
   "source": [
    "class QSVTBlock(QStruct):\n",
    "    block_ham: QNum[block_size]\n",
    "    block_qsvt: QBit\n",
    "    block_lcu: QBit\n",
    "\n",
    "\n",
    "class QSVTState(QStruct):\n",
    "    data: QNum[data_size]\n",
    "    block: QSVTBlock\n",
    "\n",
    "\n",
    "@qfunc\n",
    "def qsvt_hamiltonian_evolution(\n",
    "    phases_cos: list[float],\n",
    "    phases_sin: list[float],\n",
    "    be_qfunc: QCallable[BlockEncodedState],\n",
    "    state: QSVTState,\n",
    "):\n",
    "    def projector_cnot(q: QBit):\n",
    "        q ^= state.block.block_ham == 0\n",
    "\n",
    "    prepare_select(\n",
    "        coefficients=[1 / 2, -1j / 2],\n",
    "        select=lambda block_lcu: qsvt_lcu(\n",
    "            phases_cos,\n",
    "            phases_sin,\n",
    "            projector_cnot,\n",
    "            projector_cnot,\n",
    "            lambda: be_qfunc([state.data, state.block.block_ham]),\n",
    "            state.block.block_qsvt,\n",
    "            block_lcu,\n",
    "        ),\n",
    "        block=state.block.block_lcu,\n",
    "    )"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "64",
   "metadata": {},
   "source": [
    "The code in the rest of this section builds a model that applies the `qsvt_hamiltonian_evolution` function on the randomly prepared vector $(\\vec{\\psi},\\vec{0})$, synthesizes it, executes the resulting quantum program, and verifies the results. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "65",
   "metadata": {},
   "outputs": [],
   "source": [
    "@qfunc\n",
    "def main(data: Output[QNum[data_size]], block: Output[QNum[block_size + 2]]):\n",
    "\n",
    "    state = QSVTState()\n",
    "    allocate(state)\n",
    "    inplace_prepare_amplitudes(state_to_evolve, 0.0, state.data)\n",
    "    qsvt_hamiltonian_evolution(phases_cos, phases_sin, be_hamiltonian, state)\n",
    "    bind(state, [data, block])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "66",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Quantum program link: https://platform.classiq.io/circuit/38B0Bo2qquc6uo7rrkYBEhcxe1x\n"
     ]
    }
   ],
   "source": [
    "qprog_qsvt = synthesize(main, constraints=Constraints(optimization_parameter=\"width\"))\n",
    "show(qprog_qsvt)"
   ]
  },
  {
   "attachments": {
    "51044976-d45a-4756-9335-8336c987e9a8.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "id": "67",
   "metadata": {},
   "source": [
    "![Screenshot 2025-10-16 at 15.26.55.png](attachment:51044976-d45a-4756-9335-8336c987e9a8.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "68",
   "metadata": {},
   "outputs": [],
   "source": [
    "with ExecutionSession(qprog_qsvt, execution_preferences) as es:\n",
    "    results_qsvt = es.sample()\n",
    "\n",
    "state_result_qsvt = get_projected_state_vector(results_qsvt)\n",
    "exp_scaling_factor_qsvt = 2 * (1 / COS_SCALE)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "69",
   "metadata": {},
   "source": [
    "For the verification, we call the pre-defined function for calculating the overlap:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "70",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "expected state: [-0.72880038-0.06293144j  0.39779918-0.13338688j -0.07939772-0.47893439j\n",
      " -0.20398188+0.10752969j]\n",
      "resulting state after rescaling: [-0.72880038-0.06293144j  0.39779918-0.13338688j -0.07939772-0.47893439j\n",
      " -0.20398188+0.10752969j]\n",
      "========================================\n",
      "overlap between expected and resulting state: 1.0\n"
     ]
    }
   ],
   "source": [
    "renormalized_state_qsvt, overlap_qsvt = compare_quantum_classical_states(\n",
    "    expected_state, state_result_qsvt, exp_scaling_factor_qsvt\n",
    ")\n",
    "print(\"expected state:\", expected_state)\n",
    "print(\"resulting state after rescaling:\", renormalized_state_qsvt)\n",
    "assert np.linalg.norm(renormalized_state_qsvt - expected_state) < EPS\n",
    "print(\"=\" * 40)\n",
    "print(\"overlap between expected and resulting state:\", overlap_qsvt)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "71",
   "metadata": {},
   "source": [
    "***\n",
    "***\n",
    "## Hamiltonian Simulation with Qubitization"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "72",
   "metadata": {},
   "source": [
    "Comment: The current implementation assumes that the block encoding **unitary** is also a Hermitian matrix (see third item in the Technical Notes below).\n",
    "\n",
    "From the first property of the walk operator, Eq. (6) , we can simply combine the Jacobi–Anger expansion with the LCU technique to get the block-encoding for the Hamiltonian simulation [<a href=\"#ref-childs\">8</a>]. Namely, we have an $\\epsilon$-approximation of $\\exp(-iHt)\\approx \\sum^d_{i=0} \\beta_{i} T_{i}(x)$, for which you can perform the following encoding:\n",
    "$$\n",
    "U_{(\\bar{\\beta},\\tilde{m},\\epsilon)-\\exp{(-iHt)}} =\n",
    "\\begin{pmatrix}\n",
    "\\exp{(-iHt)}/\\bar{\\beta} & * \\\\\n",
    "* & *\n",
    "\\end{pmatrix}=\n",
    "\\begin{pmatrix}\n",
    "\\frac{1}{\\bar{\\beta}}\\sum^d_{k=0} \\beta_{k} U_{(1,m,0)-T_k(H)} & * \\\\\n",
    "* & *\n",
    "\\end{pmatrix}=\n",
    "\\begin{pmatrix}\n",
    "\\frac{1}{\\bar{\\beta}}\\sum^d_{k=0} \\beta_{k} T_{k}(Ht) & * \\\\\n",
    "* & *\n",
    "\\end{pmatrix}\n",
    "=\\begin{pmatrix}\n",
    "\\frac{1}{\\bar{\\beta}}\\sum^d_{k=0} W^k & * \\\\\n",
    "* & *\n",
    "\\end{pmatrix},\n",
    "$$\n",
    "where $\\tilde{m}=m+\\lceil \\log_2(d+1) \\rceil$, where $m$ is the block size of the Hamiltonian block-encoding (recalling that $W^k$ are block-encodings themselves, with block size $m$).\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "73",
   "metadata": {},
   "source": [
    "### Implementation"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "74",
   "metadata": {},
   "source": [
    "First, we use the predefined function to calculate the coefficients $\\beta_i$ for approximating the sine and cosine functions. Recall that we must rescale the time by the block-encoding scaling factor for $H$."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "75",
   "metadata": {},
   "outputs": [],
   "source": [
    "t0 = time.perf_counter()\n",
    "exp_coeffs = get_exp_cheb_coeffs(EPS, EVOLUTION_TIME * be_scaling)\n",
    "classical_preprocess_time_chec_lcu = time.perf_counter() - t0"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "76",
   "metadata": {},
   "source": [
    "We need to build an LCU of the unitaries $\\{ W^k\\}$, with the following parameters:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "77",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The block size of the block-encoded Hamiltonian evolution is: 6\n",
      "The scaling factor for the block-encoded Hamiltonian evolution is: 5.626479051326803\n"
     ]
    }
   ],
   "source": [
    "exp_block_size = (len(exp_coeffs) - 1).bit_length() if len(exp_coeffs) != 1 else 1\n",
    "print(f\"The block size of the block-encoded Hamiltonian evolution is: {exp_block_size}\")\n",
    "exp_be_scaling = np.sum(np.abs(exp_coeffs))\n",
    "print(\n",
    "    f\"The scaling factor for the block-encoded Hamiltonian evolution is: {exp_be_scaling}\"\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "78",
   "metadata": {},
   "source": [
    "In principle, we could use the `lcu` function from the open-library, however, since the unitaries are just a series of powers of the same unitary, we can devise a better design to the `select` operator, as depicted in the figure below (this is similar to the construction of the QPE algorithm). Note that in this construction, the `select` operator runs over all powers $0,1,\\dots, 2^{l}-1$, with $l=2^{\\lceil \\log_2(d+1) \\rceil}.$"
   ]
  },
  {
   "attachments": {
    "28af0bcb-c6e7-402f-bbb2-7cfe6ef197d6.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "id": "79",
   "metadata": {},
   "source": [
    "![Screenshot 2025-10-12 at 14.44.44.png](attachment:28af0bcb-c6e7-402f-bbb2-7cfe6ef197d6.png)\n",
    "<center>\n",
    "    Design of a select operation over a series of unitary powers. Instead of applying a series of $2^l$ multi-controlled operations, we can apply $l$ single controlled operations.\n",
    "</center>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "80",
   "metadata": {},
   "source": [
    "We build this specified select in the following function:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "81",
   "metadata": {},
   "outputs": [],
   "source": [
    "@qfunc\n",
    "def select_powered_unitaries(u: QCallable, block: QArray):\n",
    "    repeat(block.len, lambda i: control(block[i], lambda: power(2**i, lambda: u())))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "82",
   "metadata": {},
   "source": [
    "Finally, we define a Quantum Struct for the Qubitization based block encoding, and build an `lcu_cheb` function that applies the LCU. We use  Classiq's `prepare_select` function, which allows a flexible implementation of the select operation in LCU. In our case we take this operation to be the `select_powered_unitaries` function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "83",
   "metadata": {},
   "outputs": [],
   "source": [
    "class QubitizationBlock(QStruct):\n",
    "    block_ham: QNum[block_size]\n",
    "    block_exp: QArray[exp_block_size]\n",
    "\n",
    "\n",
    "class QubitizationState(QStruct):\n",
    "    data: QNum[data_size]\n",
    "    block: QubitizationBlock\n",
    "\n",
    "\n",
    "@qfunc\n",
    "def lcu_cheb(\n",
    "    coefs: list[float], be_qfunc: QCallable[BlockEncodedState], state: QubitizationState\n",
    "):\n",
    "    prepare_select(\n",
    "        coefficients=coefs,\n",
    "        select=lambda lcu_block: select_powered_unitaries(\n",
    "            lambda: walk_operator(be_qfunc, [state.data, state.block.block_ham]),\n",
    "            lcu_block,\n",
    "        ),\n",
    "        block=state.block.block_exp,\n",
    "    )"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "84",
   "metadata": {},
   "source": [
    "The code in the rest of this section builds a model that applies the `lcu_cheb` function on the randomly prepared vector $(\\vec{\\psi},\\vec{0})$, synthesizes it, executes the resulting quantum program, and verifies the results. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "85",
   "metadata": {},
   "outputs": [],
   "source": [
    "@qfunc\n",
    "def main(\n",
    "    data: Output[QNum[data_size]], block: Output[QNum[block_size + exp_block_size]]\n",
    "):\n",
    "    state = QubitizationState()\n",
    "    allocate(state)\n",
    "    inplace_prepare_amplitudes(state_to_evolve, 0.0, state.data)\n",
    "\n",
    "    lcu_cheb(\n",
    "        exp_coeffs,\n",
    "        be_hamiltonian,\n",
    "        state,\n",
    "    )\n",
    "    bind(state, [data, block])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "86",
   "metadata": {},
   "source": [
    "Synthesize the quantum model and execute it:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "87",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Quantum program link: https://platform.classiq.io/circuit/38B0IcJUn3Ak40Y95DMrPnH6uSS\n"
     ]
    }
   ],
   "source": [
    "qprog_cheb_lcu = synthesize(main, preferences=Preferences(optimization_level=1))\n",
    "show(qprog_cheb_lcu)"
   ]
  },
  {
   "attachments": {
    "6aed4b0e-39ad-4a95-ae1a-f3bb05f14b1d.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "id": "88",
   "metadata": {},
   "source": [
    "![Screenshot 2025-10-16 at 15.33.22.png](attachment:6aed4b0e-39ad-4a95-ae1a-f3bb05f14b1d.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "89",
   "metadata": {},
   "outputs": [],
   "source": [
    "with ExecutionSession(qprog_cheb_lcu, execution_preferences) as es:\n",
    "    results_cheb_lcu = es.sample()\n",
    "\n",
    "state_result_cheb_lcu = get_projected_state_vector(results_cheb_lcu)\n",
    "exp_scaling_factor_cheb_lcu = exp_be_scaling"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "90",
   "metadata": {},
   "source": [
    "For the verification, we call the pre-defined function for calculating the overlap:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "91",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "expected state: [-0.72880038-0.06293144j  0.39779918-0.13338688j -0.07939772-0.47893439j\n",
      " -0.20398188+0.10752969j]\n",
      "resulting state after rescaling: [-0.72880038-0.06293144j  0.39779918-0.13338688j -0.07939772-0.47893439j\n",
      " -0.20398188+0.10752969j]\n",
      "========================================\n",
      "overlap between expected and resulting state: 1.0\n"
     ]
    }
   ],
   "source": [
    "renormalized_state_cheb_lcu, overlap_cheb_lcu = compare_quantum_classical_states(\n",
    "    expected_state, state_result_cheb_lcu, exp_scaling_factor_cheb_lcu\n",
    ")\n",
    "print(\"expected state:\", expected_state)\n",
    "print(\"resulting state after rescaling:\", renormalized_state_cheb_lcu)\n",
    "assert np.linalg.norm(renormalized_state_cheb_lcu - expected_state) < EPS\n",
    "print(\"=\" * 40)\n",
    "print(\"overlap between expected and resulting state:\", overlap_cheb_lcu)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "92",
   "metadata": {},
   "source": [
    "***\n",
    "***\n",
    "## Summary\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "93",
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_qprog_data(qprog):\n",
    "    return qprog.data.width, qprog.transpiled_circuit.count_ops[\"cx\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "94",
   "metadata": {},
   "outputs": [],
   "source": [
    "widths = []\n",
    "cx_counts = []\n",
    "\n",
    "for qprog in [qprog_gqsp, qprog_qsvt, qprog_cheb_lcu]:\n",
    "    widths.append(get_qprog_data(qprog)[0])\n",
    "    cx_counts.append(get_qprog_data(qprog)[1])\n",
    "\n",
    "exp_scaling = [\n",
    "    exp_scaling_factor_gqsp,\n",
    "    exp_scaling_factor_qsvt,\n",
    "    exp_scaling_factor_cheb_lcu,\n",
    "]\n",
    "\n",
    "eff_cx_counts = [\n",
    "    int(np.ceil(counts * scale)) for counts, scale in zip(cx_counts, exp_scaling)\n",
    "]\n",
    "\n",
    "exp_block_sizes = [block_size + 1, block_size + 2, block_size + exp_block_size]\n",
    "\n",
    "methods = [\"GQSP\", \"QSVT\", \"Cheb. LCU\"]\n",
    "\n",
    "preprocess_times = [\n",
    "    classical_preprocess_time_gqsp,\n",
    "    classical_preprocess_time_qsvt,\n",
    "    classical_preprocess_time_chec_lcu,\n",
    "]\n",
    "\n",
    "state_distance = [\n",
    "    np.linalg.norm(expected_state - state)\n",
    "    for state in [\n",
    "        renormalized_state_gqsp,\n",
    "        renormalized_state_qsvt,\n",
    "        renormalized_state_cheb_lcu,\n",
    "    ]\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "95",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "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>methods</th>\n",
       "      <th>block size</th>\n",
       "      <th>scaling factor</th>\n",
       "      <th>width</th>\n",
       "      <th>cx counts</th>\n",
       "      <th>effective cx counts</th>\n",
       "      <th>preprocess time [sec]</th>\n",
       "      <th>accuracy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>GQSP</td>\n",
       "      <td>3</td>\n",
       "      <td>1.010101</td>\n",
       "      <td>7</td>\n",
       "      <td>8210</td>\n",
       "      <td>8293</td>\n",
       "      <td>0.036697</td>\n",
       "      <td>4.890210e-11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>QSVT</td>\n",
       "      <td>4</td>\n",
       "      <td>2.020202</td>\n",
       "      <td>7</td>\n",
       "      <td>2003</td>\n",
       "      <td>4047</td>\n",
       "      <td>0.180050</td>\n",
       "      <td>9.431843e-12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Cheb. LCU</td>\n",
       "      <td>8</td>\n",
       "      <td>5.626479</td>\n",
       "      <td>12</td>\n",
       "      <td>7055</td>\n",
       "      <td>39695</td>\n",
       "      <td>0.000603</td>\n",
       "      <td>9.395222e-12</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     methods  block size  scaling factor   width  cx counts  \\\n",
       "0       GQSP           3         1.010101      7       8210   \n",
       "1       QSVT           4         2.020202      7       2003   \n",
       "2  Cheb. LCU           8         5.626479     12       7055   \n",
       "\n",
       "   effective cx counts  preprocess time [sec]      accuracy  \n",
       "0                 8293               0.036697  4.890210e-11  \n",
       "1                 4047               0.180050  9.431843e-12  \n",
       "2                39695               0.000603  9.395222e-12  "
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "df = pd.DataFrame(\n",
    "    {\n",
    "        \"methods\": methods,\n",
    "        \"block size\": exp_block_sizes,\n",
    "        \"scaling factor \": exp_scaling,\n",
    "        \"width\": widths,\n",
    "        \"cx counts\": cx_counts,\n",
    "        \"effective cx counts\": eff_cx_counts,\n",
    "        \"preprocess time [sec]\": preprocess_times,\n",
    "        \"accuracy\": state_distance,\n",
    "    }\n",
    ")\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "96",
   "metadata": {},
   "source": [
    "As we can see, the QSVT gives better results in terms of CX counts, however, its scaling factor means that in practice, its depth/CX-counts must be doubled in comparison to the GQSP result. For a scaling factor of 2 we need to apply 1 iteration of Oblivious Amplitude amplification, whereas for a factor of 4 we need to call it twice. In other words, measuring the block being at state 0 is 2 times less likely in the QSVT method, compared to the GQSP on. This issue is taken into account in the \"effective CX counts\" measure presented in the table. Even with this fact, the QSVT gives better results in terms of gate count, using one extra qubit. The Chebyshev LCU approach (Qubitization) is less effective in all parameters, except it requires no heavy preprocessing for calculating angles. We note that all three methods have the same asymptotic scaling, and the differences appeared in the tables are due to the detailed implementations of the current example.\n",
    "***\n",
    "***"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "97",
   "metadata": {},
   "source": [
    "<h3 id=\"references\">References</h3>\n",
    "<ol>\n",
    "      <li id=\"ref-grand\">\n",
    "    Martyn, J. M., Rossi, Z. M., Tan, A. K., & Chuang, I. L. \n",
    "    <em>Grand unification of quantum algorithms.</em> \n",
    "    PRX Quantum <strong>2</strong>, 040203 (2021). \n",
    "    <!-- External links -->\n",
    "    &nbsp;- &nbsp;\n",
    "    <a href=\"https://journals.aps.org/prxquantum/abstract/10.1103/PRXQuantum.2.040203\" target=\"_blank\" rel=\"noopener noreferrer\">DOI &#8599;</a>\n",
    "  </li>\n",
    "  <li id=\"ref-gqsp\">\n",
    "    Motlagh, D, and Nathan W. Generalized quantum signal processing. PRX Quantum 5 020368 (2024). \n",
    "    <!-- External links -->\n",
    "    &nbsp;- &nbsp;\n",
    "    <a href=\"https://journals.aps.org/prxquantum/abstract/10.1103/PRXQuantum.5.020368\" target=\"_blank\" rel=\"noopener noreferrer\">DOI&#8599;</a>\n",
    "  <li id=\"ref-bck\">\n",
    "  Berry, D. W., Childs, A. M., & Kothari, R.\n",
    "  <em>Hamiltonian simulation with nearly optimal dependence on all parameters.</em>\n",
    "  In <em>Proceedings of the 56th IEEE Symposium on Foundations of Computer Science (FOCS)</em>, pp. 792–809 (2015).\n",
    "  <!-- External links -->\n",
    "  &nbsp;- &nbsp;\n",
    "  <a href=\"https://arxiv.org/abs/1501.01715\" target=\"_blank\" rel=\"noopener noreferrer\">arXiv &#8599;</a>\n",
    "  &nbsp;|&nbsp;\n",
    "  <a href=\"https://doi.org/10.1109/FOCS.2015.54\" target=\"_blank\" rel=\"noopener noreferrer\">DOI &#8599;</a>\n",
    "</li>\n",
    "  <li id=\"ref-jawiki\">\n",
    "    Jacobi–Anger Expansion. \n",
    "    <!-- External links -->\n",
    "    &nbsp;- &nbsp;\n",
    "    <a href=\"https://en.wikipedia.org/wiki/Jacobi%E2%80%93Anger_expansion\" target=\"_blank\" rel=\"noopener noreferrer\">Wikipedia &#8599;</a>\n",
    "  </li>\n",
    "  <li id=\"ref-szegedy\">\n",
    "    Szegedy, M. \n",
    "    <em>Quantum speed-up of Markov chain based algorithms.</em> \n",
    "    In <em>45th Annual IEEE Symposium on Foundations of Computer Science</em>, pp. 32–41 (2004). \n",
    "    <!-- External links -->\n",
    "    &nbsp;- &nbsp;\n",
    "    <a href=\"https://ieeexplore.ieee.org/abstract/document/1366222\" target=\"_blank\" rel=\"noopener noreferrer\">IEEE &#8599;</a>\n",
    "  </li>\n",
    "  <li id=\"ref-lin\">\n",
    "    Lin, L. <em>Lecture notes on quantum algorithms for scientific computation.</em> arXiv:2201.08309 [quant-ph] (2022). \n",
    "    <!-- External links -->\n",
    "    &nbsp;- &nbsp;\n",
    "    <a href=\"https://arxiv.org/abs/2201.08309\" target=\"_blank\" rel=\"noopener noreferrer\">arXiv &#8599;</a>\n",
    "  </li>\n",
    "\n",
    "  <li id=\"ref-kikuchi\">\n",
    "    Kikuchi, Y., McKeever, C., Coopmans, L., <em>et al.</em> \n",
    "    <em>Realization of quantum signal processing on a noisy quantum computer.</em> \n",
    "    <em>npj Quantum Information</em> <strong>9</strong>, 93 (2023). \n",
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    "  </li>\n",
    "\n",
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    "    Childs, A. M., Kothari, R., & Somma, R. D. \n",
    "    <em>Quantum algorithm for systems of linear equations with exponentially improved dependence on precision.</em> \n",
    "    <em>SIAM Journal on Computing</em> <strong>46</strong>, 1920–1950 (2017). \n",
    "    <!-- External links -->\n",
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    "  </li>\n",
    "       \n",
    "</ol>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "98",
   "metadata": {},
   "source": [
    "***\n",
    "***"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "99",
   "metadata": {},
   "source": [
    "## Technical Notes"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "100",
   "metadata": {},
   "source": [
    "### Recap on Block-Encoding matrices "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "101",
   "metadata": {},
   "source": [
    "The basic use of the block-encoding unitary is the following. Let us say we have a $(s, m, 0)$-encoding of a matrix $A$\n",
    "$$\n",
    "U_{(s, m, 0)-A} = \\begin{pmatrix}\n",
    "\t A/s & *  \\\\\n",
    "\t * & * \n",
    "\\end{pmatrix},\n",
    "$$\n",
    "where the dimension of $A$ is $2^n\\times 2^n$.\n",
    "Applying this unitary on the state \n",
    "$$\n",
    "|\\psi\\rangle_n|0\\rangle_m =|\\psi\\rangle_n \\otimes \n",
    "\\begin{pmatrix}\n",
    "\t 1  \\\\\n",
    "      0\\\\\n",
    "\t \\vdots \\\\\n",
    "  0\n",
    "\\end{pmatrix} =  \n",
    "\\begin{pmatrix}\n",
    "\t |\\psi\\rangle_n  \\\\\n",
    "      0\\\\\n",
    "\t \\vdots \\\\\n",
    "  0\n",
    "\\end{pmatrix},\n",
    "$$\n",
    "yields\n",
    "$$\n",
    "U_{(s, m, 0)-A} \\left(|\\psi\\rangle_n|0\\rangle_m \\right)  = \n",
    "\\begin{pmatrix}\n",
    "\t \\frac{1}{s}A|\\psi\\rangle_n  \\\\\n",
    "      0\\\\\n",
    "\t \\vdots \\\\\n",
    "  0\n",
    "\\end{pmatrix}\n",
    "+\n",
    "\\begin{pmatrix}\n",
    "\t 0  \\\\\n",
    "      *\\\\\n",
    "\t \\vdots \\\\\n",
    "  *\n",
    "\\end{pmatrix} = \\frac{1}{s}A|\\psi\\rangle_n|0\\rangle_m +\\sum_{l\\neq 0} c_l |\\phi\\rangle_n|l\\rangle_m .\n",
    "$$\n",
    "Thus, if we measure $|0\\rangle_m$ on the second variable, we know the first variable is the state $\\frac{1}{s}A|\\psi\\rangle_n$. In terms of measurement, we can post-select on $|0\\rangle_m$ to measure the desired state. We can amplify the success of this operation, given by $|\\frac{1}{s}A|\\psi\\rangle_n|$, with Quantum Amplitude Amplification."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "102",
   "metadata": {},
   "source": [
    "### Recap on LCU\n",
    "\n",
    "The task of LCU is to block-encode a sum of unitary functions:\n",
    "$$\n",
    "U_{(\\bar{\\alpha},m,0)-A} =\\begin{pmatrix}\n",
    "A/\\bar{\\alpha} & * \\\\\n",
    "* & *\n",
    "\\end{pmatrix}, \\text{   for     } A = \\sum^{L-1}_{i=0} \\alpha_i U_i, \\,\\,\\,\\,\\, \\alpha_i\\geq 0.\n",
    "$$\n",
    "This operation is implemented by the so-called `prepare` and `select` operations. The `prepare` function is simply a state preparation function on the block variable:\n",
    "$$\n",
    "\\mathrm{PREPARE} \\equiv \\mathcal{I} \\otimes \\left(\\sum^{L-1}_{i=0}\\sqrt{\\alpha_i/\\bar{\\alpha}} |i\\rangle \\langle 0| +\\sum^{L-1}_{i=0}\\sum^{L-1}_{j=1}g_{ij}|i\\rangle \\langle j|\\right),\n",
    "$$\n",
    "where $g_{ij}$ are some uninteresting coefficients, and $\\mathcal{I}$ is the identity operation working on the data space. The `select` operation acts as a quantum switch, applying the operation $U_i$ on the data variable if the block variable is at state $|i\\rangle$:\n",
    "$$\n",
    "\\mathrm{SELECT} \\equiv \\sum_{i}U_i \\otimes \\left(|i\\rangle \\langle i|\\right).\n",
    "$$\n",
    "The LCU operation is acheived by $\\mathrm{PREPARE}\\cdot \\mathrm{SELECT}\\cdot \\mathrm{PREPARE}^{\\dagger}$. One can check that:\n",
    "\\begin{align}\n",
    "\\left(\\mathcal{I} \\otimes \\left(\\sum^{L-1}_{i=0}\\sqrt{\\alpha_i/\\bar{\\alpha}} |i\\rangle \\langle 0| +\\sum^{L-1}_{i=0}\\sum^{L-1}_{j=1}g_{ij}|i\\rangle \\langle j|\\right)\\right)\n",
    "\\left( \\sum_{i'}U_{i'} \\otimes \\left(|i'\\rangle \\langle i'|\\right) \\right)\n",
    "\\left(\\mathcal{I} \\otimes \\left(\\sum^{L-1}_{i''=0}\\sqrt{\\alpha_{i''}/\\bar{\\alpha}} |0\\rangle \\langle i''| +\\sum^{L-1}_{i''=0}\\sum^{L-1}_{j''=1}g^{*}_{i''j''}|j''\\rangle \\langle i''|\\right)\\right) =\\\\\n",
    "= \\left(\\sum^{L-1}_{i=0}(\\alpha_i/\\bar{\\alpha}) U_{i} \\right) \\otimes |0\\rangle \\langle 0| + \\left(\\sum^{L-1}_{i=1}\\sum^{L-1}_{j=1}G_{ij}|i\\rangle \\langle j|\\right),\n",
    "\\end{align}\n",
    "with $G_{ij}$ some non-interesting operators."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "103",
   "metadata": {},
   "source": [
    "### Generalizing to non-Hermitian $U_H$\n",
    "\n",
    "The GQSP and Qubitization methods assumes that the block-encoding unitary is also Hermitian. This assumption sets the two propetries of the walk operator, Eqs. (6) and (7), on which those two methods are based upon.  In the case of non-Hermitian unitary block-encoding, the cited property of the walk operator $W$ does not hold. However, a similar property holds for $\\tilde{W}\\equiv U_{(s,m,0)-H}^T\\Pi_{|0\\rangle_m} U_{(s,m,0)-H}\\Pi_{|0\\rangle_m}$ (See Sec. 7.4 in Ref.  [<a href=\"#ref-lin\">6</a>].) We can work with this operator, and with its equivalent properties that generalize Eqs. (6) and (7)."
   ]
  }
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