Introduction_of QC_with_continuas_variablesl.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "# Quantum Computing with Continuous Variables (CVC)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "In this tutorial, we will learn about quantum computing with continuous variables.\n",
        "\n",
        "My proposal to present about:\n",
        "* Quantum computing with continuous variables (CQVC)\n",
        "* Some basic gates with CQVC and how to implement them"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Introduction"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Quantum computing is a field of computer science that uses principles of quantum mechanics to perform calculations. Unlike classical computers, which use bits to represent data as 0s and 1s, quantum computers use qubits, which can be in a state of 0, 1, or any quantum superposition of these states. This allows quantum computers to process massive amounts of information simultaneously, potentially solving certain problems much faster than classical computers.\n",
        "\n",
        "The term Quantum Computing was coined in 1982, when R. Feynman (Nobel Prize in Physics in 1965) proposed the use of quantum systems to simulate physical phenomena in general \\cite{Feynman}, an area now known as ``Quantum Simulation'' \\cite{simulators}.\n",
        "\n",
        "Thus, the development of quantum computing protocols became a subject of study. In particular, one of the most famous algorithms is known as the decomposition algorithm proposed by P. Shor \\cite{SHOR}, which is capable of factoring extremely large numbers in a number of steps that grows polynomially with the number of digits of the number to be factored, in contrast to the exponential growth of the best known classical algorithms."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "# What is continuous variable quantum computing?\n",
        "\n",
        "Continuous Variable Quantum Computing (CVQ) is a model of quantum computing that uses quantum systems whose properties, such as the position and momentum of a particle, can vary continuously. Instead of using qubits, this model uses continuous modes of light, such as quantum states of the electromagnetic field."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "# Why use continuous variables?\n",
        "\n",
        "There are several reasons to use continuous variables in quantum computing:\n",
        "\n",
        "* Simpler Hardware: Continuous variable systems often use more mature and accessible technologies, such as lasers and light detectors, rather than complex qubit control devices.\n",
        "\n",
        "* Ease of Manipulation: Continuous states, such as the coherent states of light, are relatively easier to generate, manipulate, and detect with high precision using standard optical techniques.\n",
        "\n",
        "* Scalability: In some respects, continuous variable systems can be easier to scale, allowing for the creation of more efficient quantum communication networks and quantum cryptography protocols.\n",
        "\n",
        "* Information Processing: Continuous variables can be used to implement certain quantum operations efficiently, facilitating tasks such as the quantum Fourier transform and other essential mathematical operations.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "# The difference between qubit and continuous variable systems is most evident in the basic expansions of quantum states:\n",
        "\n",
        "\\begin{equation*}\n",
        "\\ket{\\Psi} = a\\ket{0}+ b\\ket{1} , \\textbf{Qubit},\n",
        "\\end{equation*}\n",
        "\n",
        "\\begin{equation*}\n",
        "\\ket{\\Psi} = \\int \\psi(x) \\ket{x} dx , \\textbf{Qumode}.\n",
        "\\end{equation*}\n",
        "\n",
        "For the case of a single quantum mode, we can perform the encoding of two real-valued variables (x,p) $\\in \\mathbb{R}^{2}$. For the case with $N$ modes, the phase space has $2N$ real variables $(x,p)$ that $\\in R^N$. The states of the modes also have a representation as density vectors or matrices in Hilbert space in the Fock state basis $\\{\\ket{n}\\}$, $n=0..\\infty$, which are the eigenstates of the photon number operator $\\hat{n} = ( \\hat{x}^2 + \\hat{p}^2 -1 )/2$.\n",
        "\n",
        "The union of the areas of Quantum Computing and Machine Learning gave rise to the field of study called Quantum Machine Learning, with the aim of taking advantage of the advantages of both. In the development of the area, we observe two strands: the first consists of trying to implement classical ML algorithms in the language of quantum computers, while the second consists of developing new ML techniques exclusive to quantum models, with possible gains in relation to classical ML."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Portas Lógicas em Computação Quântica com Variáveis Contínuas"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "# First let's import some useful libraries\n",
        "import strawberryfields as sf\n",
        "from strawberryfields.ops import *\n",
        "\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "from mpl_toolkits.mplot3d import Axes3D"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "# if we need , install the Strawberry Fields.\n",
        "#!pip install strawberryfields --upgrade"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "A quantum circuit is always initialized in the state of vacuum (The vacuum state is the lowest energy Gaussian state). \n",
        "\n",
        "Here we learn how to create the vacuum state on a quantum circuit with one qumode.\n",
        "\n",
        "We initialize the Strawberry Fields program. q represents a qumode and below we start with one qumode for our circuit."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "prog = sf.Program(1)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "There are four main types of Operations:\n",
        "\n",
        "* State preparation\n",
        "\n",
        "* Gate application\n",
        "\n",
        "* Measurements\n",
        "\n",
        "These all use the following general syntax:\n",
        "\n",
        "        Operation(args) | q\n",
        "\n",
        "where args represents a list of parameters for the operation, and q is the qumode (or sequence of qumodes) the quantum operation acts upon. The symbol | always separates the operation to be applied (on the left) and the subsystem(s) it acts on (on the right)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "with prog.context as q:\n",
        "    Vac | q[0]\n",
        "\n",
        "eng = sf.Engine('gaussian')\n",
        "result = eng.run(prog)\n",
        "result.state"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "In the code above we have an example of how to prepare the state in the vacuum. In most algorithms we always consider the initial state to be 0, which means it is in the vacuum (the most fundamental state), but it is important to know that there is another way to prepare the initial state of the algorithm, as a terminal state or one of the fock bases."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Using matplotlib, we then plot the Wigner function of the vacuum state, which is a Gaussian distribution. The Wigner function quasi-probability illustrates negative and positive regions in phase space. We’ll notice below that Gaussian states such as the vacuum and squeezed state are in positive regions of phase space while non-Gaussian states can reach negative regions of the phase space. The y-axis on this plot can describe the $p$ quadrature or momentum operator and the x-axis describes the $x$ quadrature or position operator of the state."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "fig = plt.figure()\n",
        "X = np.linspace(-5, 5, 100)\n",
        "P = np.linspace(-5, 5, 100)\n",
        "Z = result.state.wigner(0, X, P)\n",
        "X, P = np.meshgrid(X, P)\n",
        "ax = fig.add_subplot(111, projection=\"3d\")\n",
        "ax.plot_surface(X, P, Z, cmap=\"RdYlGn\", lw=0.5, rstride=1, cstride=1)\n",
        "fig.set_size_inches(4.8, 5)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "We can make a couple of observations from the Gaussian distribution above. We notice that it has positive quasi-probability everywhere. Later on, we’ll have a look at non-Gaussian gates that have regions of negative quasi-probability. The vacuum state is also centered at 0 on both the position axis $x$ and momentum axis $p$."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "#### Squeeze\n",
        "\n",
        "The Squeeze operation is given by:\n",
        "\n",
        "\\begin{equation*}\n",
        "\\hat{S}(z) = \\exp{\\Bigl[ \\frac{1}{2}(z^*\\hat{a}^{2} - z \\hat{a}^{\\dagger^2} )\\Bigl]}\n",
        "= \\exp{\\Bigl[ \\frac{r}{2}(e^{-i\\phi}\\hat{a}^2 -e^{i\\phi}\\hat{a}^{\\dagger^2} ) \\Bigl] },\n",
        "\\end{equation*}\n",
        "\n",
        "where $z = re^{i\\phi}$, with magnitude $r \\ge 0$ and phase $\\phi \\in [0,2\\pi)$.\n",
        "\n",
        "In CQVC, when applied to the vacuum state, compression produces different results compared to coherent displacement (i.e., $\\hat{S}(z)\\ket{0} = \\ket{z}$). This operation can be used both in classical information coding and in algorithmic steps. Due to the uncertainty principle, compression of one of the quadratures leads to stretching of the other."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "prog = sf.Program(1)\n",
        "with prog.context as q:\n",
        "    S = Sgate(1)\n",
        "    Vac | q[0]\n",
        "    S   | q[0]\n",
        "\n",
        "\n",
        "state = eng.run(prog).state\n",
        "fig = plt.figure()\n",
        "X = np.linspace(-5, 5, 100)\n",
        "P = np.linspace(-5, 5, 100)\n",
        "Z = state.wigner(0, X, P)\n",
        "X, P = np.meshgrid(X, P)\n",
        "ax = fig.add_subplot(111, projection=\"3d\")\n",
        "fig.set_size_inches(4.8, 5)\n",
        "ax.plot_surface(X, P, Z, cmap=\"RdYlGn\", lw=0.5, rstride=1, cstride=1)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "In the previous case using $r=1$ and $\\phi=0$, we can change it"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<mpl_toolkits.mplot3d.art3d.Poly3DCollection at 0x2389272a1d0>"
            ]
          },
          "execution_count": 7,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
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",
            "text/plain": [
              "<Figure size 480x500 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "prog = sf.Program(1)\n",
        "with prog.context as q:\n",
        "    S = Sgate(0.5,np.pi)\n",
        "    Vac | q[0]\n",
        "    S   | q[0]\n",
        "\n",
        "\n",
        "state = eng.run(prog).state\n",
        "fig = plt.figure()\n",
        "X = np.linspace(-5, 5, 100)\n",
        "P = np.linspace(-5, 5, 100)\n",
        "Z = state.wigner(0, X, P)\n",
        "X, P = np.meshgrid(X, P)\n",
        "ax = fig.add_subplot(111, projection=\"3d\")\n",
        "fig.set_size_inches(4.8, 5)\n",
        "ax.plot_surface(X, P, Z, cmap=\"RdYlGn\", lw=0.5, rstride=1, cstride=1)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "#### Rotation\n",
        "\n",
        "The rotation operation is represented by:\n",
        "\n",
        "\\begin{equation*}\n",
        "\\hat{R}(\\theta) = \\exp{(i\\theta \\hat{a}^{\\dagger}\\hat{a} )}\n",
        "= \\exp{ \\Bigl[ i\\frac{\\theta}{2}( \\hat{x}^2+\\hat{p}^2 - \\hat{I} ) \\Bigl]}.\n",
        "\\end{equation*}\n",
        "\n",
        "When applied to a vacuum state, this operation has no effect. However, its application to a coherent state results in a rotation of $\\theta$ of that state around the center of phase space."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "In this case, applying the rotation in a vacuum does not change anything in the wigner function, so we will apply a squeeze and then the rotation."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Error in callback <function _draw_all_if_interactive at 0x00000238927ED760> (for post_execute), with arguments args (),kwargs {}:\n"
          ]
        },
        {
          "ename": "KeyboardInterrupt",
          "evalue": "",
          "output_type": "error",
          "traceback": [
            "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[1;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
            "File \u001b[1;32mc:\\Users\\gubio\\anaconda3\\envs\\Strawberry-fields\\Lib\\site-packages\\matplotlib\\pyplot.py:197\u001b[0m, in \u001b[0;36m_draw_all_if_interactive\u001b[1;34m()\u001b[0m\n\u001b[0;32m    195\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_draw_all_if_interactive\u001b[39m() \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m    196\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m matplotlib\u001b[38;5;241m.\u001b[39mis_interactive():\n\u001b[1;32m--> 197\u001b[0m         \u001b[43mdraw_all\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n",
            "File \u001b[1;32mc:\\Users\\gubio\\anaconda3\\envs\\Strawberry-fields\\Lib\\site-packages\\matplotlib\\_pylab_helpers.py:132\u001b[0m, in \u001b[0;36mGcf.draw_all\u001b[1;34m(cls, force)\u001b[0m\n\u001b[0;32m    130\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m manager \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mcls\u001b[39m\u001b[38;5;241m.\u001b[39mget_all_fig_managers():\n\u001b[0;32m    131\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m force \u001b[38;5;129;01mor\u001b[39;00m manager\u001b[38;5;241m.\u001b[39mcanvas\u001b[38;5;241m.\u001b[39mfigure\u001b[38;5;241m.\u001b[39mstale:\n\u001b[1;32m--> 132\u001b[0m         \u001b[43mmanager\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcanvas\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdraw_idle\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n",
            "File \u001b[1;32mc:\\Users\\gubio\\anaconda3\\envs\\Strawberry-fields\\Lib\\site-packages\\matplotlib\\backend_bases.py:1893\u001b[0m, in \u001b[0;36mFigureCanvasBase.draw_idle\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m   1891\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_is_idle_drawing:\n\u001b[0;32m   1892\u001b[0m     \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_idle_draw_cntx():\n\u001b[1;32m-> 1893\u001b[0m         \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdraw\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
            "File \u001b[1;32mc:\\Users\\gubio\\anaconda3\\envs\\Strawberry-fields\\Lib\\site-packages\\matplotlib\\backends\\backend_agg.py:388\u001b[0m, in \u001b[0;36mFigureCanvasAgg.draw\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    385\u001b[0m \u001b[38;5;66;03m# Acquire a lock on the shared font cache.\u001b[39;00m\n\u001b[0;32m    386\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtoolbar\u001b[38;5;241m.\u001b[39m_wait_cursor_for_draw_cm() \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtoolbar\n\u001b[0;32m    387\u001b[0m       \u001b[38;5;28;01melse\u001b[39;00m nullcontext()):\n\u001b[1;32m--> 388\u001b[0m     \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfigure\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdraw\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrenderer\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    389\u001b[0m     \u001b[38;5;66;03m# A GUI class may be need to update a window using this draw, so\u001b[39;00m\n\u001b[0;32m    390\u001b[0m     \u001b[38;5;66;03m# don't forget to call the superclass.\u001b[39;00m\n\u001b[0;32m    391\u001b[0m     \u001b[38;5;28msuper\u001b[39m()\u001b[38;5;241m.\u001b[39mdraw()\n",
            "File \u001b[1;32mc:\\Users\\gubio\\anaconda3\\envs\\Strawberry-fields\\Lib\\site-packages\\matplotlib\\artist.py:95\u001b[0m, in \u001b[0;36m_finalize_rasterization.<locals>.draw_wrapper\u001b[1;34m(artist, renderer, *args, **kwargs)\u001b[0m\n\u001b[0;32m     93\u001b[0m \u001b[38;5;129m@wraps\u001b[39m(draw)\n\u001b[0;32m     94\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mdraw_wrapper\u001b[39m(artist, renderer, \u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[1;32m---> 95\u001b[0m     result \u001b[38;5;241m=\u001b[39m \u001b[43mdraw\u001b[49m\u001b[43m(\u001b[49m\u001b[43martist\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mrenderer\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m     96\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m renderer\u001b[38;5;241m.\u001b[39m_rasterizing:\n\u001b[0;32m     97\u001b[0m         renderer\u001b[38;5;241m.\u001b[39mstop_rasterizing()\n",
            "File \u001b[1;32mc:\\Users\\gubio\\anaconda3\\envs\\Strawberry-fields\\Lib\\site-packages\\matplotlib\\artist.py:72\u001b[0m, in \u001b[0;36mallow_rasterization.<locals>.draw_wrapper\u001b[1;34m(artist, renderer)\u001b[0m\n\u001b[0;32m     69\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m artist\u001b[38;5;241m.\u001b[39mget_agg_filter() \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m     70\u001b[0m         renderer\u001b[38;5;241m.\u001b[39mstart_filter()\n\u001b[1;32m---> 72\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mdraw\u001b[49m\u001b[43m(\u001b[49m\u001b[43martist\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mrenderer\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m     73\u001b[0m \u001b[38;5;28;01mfinally\u001b[39;00m:\n\u001b[0;32m     74\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m artist\u001b[38;5;241m.\u001b[39mget_agg_filter() \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n",
            "File \u001b[1;32mc:\\Users\\gubio\\anaconda3\\envs\\Strawberry-fields\\Lib\\site-packages\\matplotlib\\figure.py:3154\u001b[0m, in \u001b[0;36mFigure.draw\u001b[1;34m(self, renderer)\u001b[0m\n\u001b[0;32m   3151\u001b[0m         \u001b[38;5;66;03m# ValueError can occur when resizing a window.\u001b[39;00m\n\u001b[0;32m   3153\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mpatch\u001b[38;5;241m.\u001b[39mdraw(renderer)\n\u001b[1;32m-> 3154\u001b[0m \u001b[43mmimage\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_draw_list_compositing_images\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m   3155\u001b[0m \u001b[43m    \u001b[49m\u001b[43mrenderer\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43martists\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msuppressComposite\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m   3157\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m sfig \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msubfigs:\n\u001b[0;32m   3158\u001b[0m     sfig\u001b[38;5;241m.\u001b[39mdraw(renderer)\n",
            "File \u001b[1;32mc:\\Users\\gubio\\anaconda3\\envs\\Strawberry-fields\\Lib\\site-packages\\matplotlib\\image.py:132\u001b[0m, in \u001b[0;36m_draw_list_compositing_images\u001b[1;34m(renderer, parent, artists, suppress_composite)\u001b[0m\n\u001b[0;32m    130\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m not_composite \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m has_images:\n\u001b[0;32m    131\u001b[0m     \u001b[38;5;28;01mfor\u001b[39;00m a \u001b[38;5;129;01min\u001b[39;00m artists:\n\u001b[1;32m--> 132\u001b[0m         \u001b[43ma\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdraw\u001b[49m\u001b[43m(\u001b[49m\u001b[43mrenderer\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    133\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m    134\u001b[0m     \u001b[38;5;66;03m# Composite any adjacent images together\u001b[39;00m\n\u001b[0;32m    135\u001b[0m     image_group \u001b[38;5;241m=\u001b[39m []\n",
            "File \u001b[1;32mc:\\Users\\gubio\\anaconda3\\envs\\Strawberry-fields\\Lib\\site-packages\\matplotlib\\artist.py:72\u001b[0m, in \u001b[0;36mallow_rasterization.<locals>.draw_wrapper\u001b[1;34m(artist, renderer)\u001b[0m\n\u001b[0;32m     69\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m artist\u001b[38;5;241m.\u001b[39mget_agg_filter() \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m     70\u001b[0m         renderer\u001b[38;5;241m.\u001b[39mstart_filter()\n\u001b[1;32m---> 72\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mdraw\u001b[49m\u001b[43m(\u001b[49m\u001b[43martist\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mrenderer\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m     73\u001b[0m \u001b[38;5;28;01mfinally\u001b[39;00m:\n\u001b[0;32m     74\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m artist\u001b[38;5;241m.\u001b[39mget_agg_filter() \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n",
            "File \u001b[1;32mc:\\Users\\gubio\\anaconda3\\envs\\Strawberry-fields\\Lib\\site-packages\\mpl_toolkits\\mplot3d\\axes3d.py:491\u001b[0m, in \u001b[0;36mAxes3D.draw\u001b[1;34m(self, renderer)\u001b[0m\n\u001b[0;32m    489\u001b[0m \u001b[38;5;66;03m# Then gridlines\u001b[39;00m\n\u001b[0;32m    490\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m axis \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_axis_map\u001b[38;5;241m.\u001b[39mvalues():\n\u001b[1;32m--> 491\u001b[0m     \u001b[43maxis\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdraw_grid\u001b[49m\u001b[43m(\u001b[49m\u001b[43mrenderer\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    492\u001b[0m \u001b[38;5;66;03m# Then axes, labels, text, and ticks\u001b[39;00m\n\u001b[0;32m    493\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m axis \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_axis_map\u001b[38;5;241m.\u001b[39mvalues():\n",
            "File \u001b[1;32mc:\\Users\\gubio\\anaconda3\\envs\\Strawberry-fields\\Lib\\site-packages\\matplotlib\\artist.py:72\u001b[0m, in \u001b[0;36mallow_rasterization.<locals>.draw_wrapper\u001b[1;34m(artist, renderer)\u001b[0m\n\u001b[0;32m     69\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m artist\u001b[38;5;241m.\u001b[39mget_agg_filter() \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m     70\u001b[0m         renderer\u001b[38;5;241m.\u001b[39mstart_filter()\n\u001b[1;32m---> 72\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mdraw\u001b[49m\u001b[43m(\u001b[49m\u001b[43martist\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mrenderer\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m     73\u001b[0m \u001b[38;5;28;01mfinally\u001b[39;00m:\n\u001b[0;32m     74\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m artist\u001b[38;5;241m.\u001b[39mget_agg_filter() \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n",
            "File \u001b[1;32mc:\\Users\\gubio\\anaconda3\\envs\\Strawberry-fields\\Lib\\site-packages\\mpl_toolkits\\mplot3d\\axis3d.py:642\u001b[0m, in \u001b[0;36mAxis.draw_grid\u001b[1;34m(self, renderer)\u001b[0m\n\u001b[0;32m    638\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m\n\u001b[0;32m    640\u001b[0m renderer\u001b[38;5;241m.\u001b[39mopen_group(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mgrid3d\u001b[39m\u001b[38;5;124m\"\u001b[39m, gid\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mget_gid())\n\u001b[1;32m--> 642\u001b[0m ticks \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_update_ticks\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    643\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(ticks):\n\u001b[0;32m    644\u001b[0m     \u001b[38;5;66;03m# Get general axis information:\u001b[39;00m\n\u001b[0;32m    645\u001b[0m     info \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_axinfo\n",
            "File \u001b[1;32mc:\\Users\\gubio\\anaconda3\\envs\\Strawberry-fields\\Lib\\site-packages\\matplotlib\\axis.py:1277\u001b[0m, in \u001b[0;36mAxis._update_ticks\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m   1275\u001b[0m major_locs \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mget_majorticklocs()\n\u001b[0;32m   1276\u001b[0m major_labels \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mmajor\u001b[38;5;241m.\u001b[39mformatter\u001b[38;5;241m.\u001b[39mformat_ticks(major_locs)\n\u001b[1;32m-> 1277\u001b[0m major_ticks \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_major_ticks\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mlen\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mmajor_locs\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m   1278\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m tick, loc, label \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mzip\u001b[39m(major_ticks, major_locs, major_labels):\n\u001b[0;32m   1279\u001b[0m     tick\u001b[38;5;241m.\u001b[39mupdate_position(loc)\n",
            "File \u001b[1;32mc:\\Users\\gubio\\anaconda3\\envs\\Strawberry-fields\\Lib\\site-packages\\mpl_toolkits\\mplot3d\\axis3d.py:174\u001b[0m, in \u001b[0;36mAxis.get_major_ticks\u001b[1;34m(self, numticks)\u001b[0m\n\u001b[0;32m    173\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mget_major_ticks\u001b[39m(\u001b[38;5;28mself\u001b[39m, numticks\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m):\n\u001b[1;32m--> 174\u001b[0m     ticks \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43msuper\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_major_ticks\u001b[49m\u001b[43m(\u001b[49m\u001b[43mnumticks\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    175\u001b[0m     \u001b[38;5;28;01mfor\u001b[39;00m t \u001b[38;5;129;01min\u001b[39;00m ticks:\n\u001b[0;32m    176\u001b[0m         \u001b[38;5;28;01mfor\u001b[39;00m obj \u001b[38;5;129;01min\u001b[39;00m [\n\u001b[0;32m    177\u001b[0m                 t\u001b[38;5;241m.\u001b[39mtick1line, t\u001b[38;5;241m.\u001b[39mtick2line, t\u001b[38;5;241m.\u001b[39mgridline, t\u001b[38;5;241m.\u001b[39mlabel1, t\u001b[38;5;241m.\u001b[39mlabel2]:\n",
            "File \u001b[1;32mc:\\Users\\gubio\\anaconda3\\envs\\Strawberry-fields\\Lib\\site-packages\\matplotlib\\axis.py:1626\u001b[0m, in \u001b[0;36mAxis.get_major_ticks\u001b[1;34m(self, numticks)\u001b[0m\n\u001b[0;32m   1622\u001b[0m     numticks \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mlen\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mget_majorticklocs())\n\u001b[0;32m   1624\u001b[0m \u001b[38;5;28;01mwhile\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mmajorTicks) \u001b[38;5;241m<\u001b[39m numticks:\n\u001b[0;32m   1625\u001b[0m     \u001b[38;5;66;03m# Update the new tick label properties from the old.\u001b[39;00m\n\u001b[1;32m-> 1626\u001b[0m     tick \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_get_tick\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmajor\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m)\u001b[49m\n\u001b[0;32m   1627\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mmajorTicks\u001b[38;5;241m.\u001b[39mappend(tick)\n\u001b[0;32m   1628\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_copy_tick_props(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mmajorTicks[\u001b[38;5;241m0\u001b[39m], tick)\n",
            "File \u001b[1;32mc:\\Users\\gubio\\anaconda3\\envs\\Strawberry-fields\\Lib\\site-packages\\matplotlib\\axis.py:1562\u001b[0m, in \u001b[0;36mAxis._get_tick\u001b[1;34m(self, major)\u001b[0m\n\u001b[0;32m   1558\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mNotImplementedError\u001b[39;00m(\n\u001b[0;32m   1559\u001b[0m         \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mThe Axis subclass \u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__class__\u001b[39m\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__name__\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m must define \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m   1560\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m_tick_class or reimplement _get_tick()\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m   1561\u001b[0m tick_kw \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_major_tick_kw \u001b[38;5;28;01mif\u001b[39;00m major \u001b[38;5;28;01melse\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_minor_tick_kw\n\u001b[1;32m-> 1562\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_tick_class\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43maxes\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmajor\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmajor\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mtick_kw\u001b[49m\u001b[43m)\u001b[49m\n",
            "File \u001b[1;32mc:\\Users\\gubio\\anaconda3\\envs\\Strawberry-fields\\Lib\\site-packages\\matplotlib\\axis.py:413\u001b[0m, in \u001b[0;36mXTick.__init__\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m    412\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m__init__\u001b[39m(\u001b[38;5;28mself\u001b[39m, \u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[1;32m--> 413\u001b[0m     \u001b[38;5;28;43msuper\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[38;5;21;43m__init__\u001b[39;49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    414\u001b[0m     \u001b[38;5;66;03m# x in data coords, y in axes coords\u001b[39;00m\n\u001b[0;32m    415\u001b[0m     ax \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39maxes\n",
            "File \u001b[1;32mc:\\Users\\gubio\\anaconda3\\envs\\Strawberry-fields\\Lib\\site-packages\\matplotlib\\axis.py:176\u001b[0m, in \u001b[0;36mTick.__init__\u001b[1;34m(self, axes, loc, size, width, color, tickdir, pad, labelsize, labelcolor, labelfontfamily, zorder, gridOn, tick1On, tick2On, label1On, label2On, major, labelrotation, grid_color, grid_linestyle, grid_linewidth, grid_alpha, **kwargs)\u001b[0m\n\u001b[0;32m    165\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtick2line \u001b[38;5;241m=\u001b[39m mlines\u001b[38;5;241m.\u001b[39mLine2D(\n\u001b[0;32m    166\u001b[0m     [], [],\n\u001b[0;32m    167\u001b[0m     color\u001b[38;5;241m=\u001b[39mcolor, linestyle\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mnone\u001b[39m\u001b[38;5;124m\"\u001b[39m, zorder\u001b[38;5;241m=\u001b[39mzorder, visible\u001b[38;5;241m=\u001b[39mtick2On,\n\u001b[0;32m    168\u001b[0m     markeredgecolor\u001b[38;5;241m=\u001b[39mcolor, markersize\u001b[38;5;241m=\u001b[39msize, markeredgewidth\u001b[38;5;241m=\u001b[39mwidth,\n\u001b[0;32m    169\u001b[0m )\n\u001b[0;32m    170\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mgridline \u001b[38;5;241m=\u001b[39m mlines\u001b[38;5;241m.\u001b[39mLine2D(\n\u001b[0;32m    171\u001b[0m     [], [],\n\u001b[0;32m    172\u001b[0m     color\u001b[38;5;241m=\u001b[39mgrid_color, alpha\u001b[38;5;241m=\u001b[39mgrid_alpha, visible\u001b[38;5;241m=\u001b[39mgridOn,\n\u001b[0;32m    173\u001b[0m     linestyle\u001b[38;5;241m=\u001b[39mgrid_linestyle, linewidth\u001b[38;5;241m=\u001b[39mgrid_linewidth, marker\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[0;32m    174\u001b[0m     \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mgrid_kw,\n\u001b[0;32m    175\u001b[0m )\n\u001b[1;32m--> 176\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mgridline\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_path\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241m.\u001b[39m_interpolation_steps \u001b[38;5;241m=\u001b[39m \\\n\u001b[0;32m    177\u001b[0m     GRIDLINE_INTERPOLATION_STEPS\n\u001b[0;32m    178\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mlabel1 \u001b[38;5;241m=\u001b[39m mtext\u001b[38;5;241m.\u001b[39mText(\n\u001b[0;32m    179\u001b[0m     np\u001b[38;5;241m.\u001b[39mnan, np\u001b[38;5;241m.\u001b[39mnan,\n\u001b[0;32m    180\u001b[0m     fontsize\u001b[38;5;241m=\u001b[39mlabelsize, color\u001b[38;5;241m=\u001b[39mlabelcolor, visible\u001b[38;5;241m=\u001b[39mlabel1On,\n\u001b[0;32m    181\u001b[0m     fontfamily\u001b[38;5;241m=\u001b[39mlabelfontfamily, rotation\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_labelrotation[\u001b[38;5;241m1\u001b[39m])\n\u001b[0;32m    182\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mlabel2 \u001b[38;5;241m=\u001b[39m mtext\u001b[38;5;241m.\u001b[39mText(\n\u001b[0;32m    183\u001b[0m     np\u001b[38;5;241m.\u001b[39mnan, np\u001b[38;5;241m.\u001b[39mnan,\n\u001b[0;32m    184\u001b[0m     fontsize\u001b[38;5;241m=\u001b[39mlabelsize, color\u001b[38;5;241m=\u001b[39mlabelcolor, visible\u001b[38;5;241m=\u001b[39mlabel2On,\n\u001b[0;32m    185\u001b[0m     fontfamily\u001b[38;5;241m=\u001b[39mlabelfontfamily, rotation\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_labelrotation[\u001b[38;5;241m1\u001b[39m])\n",
            "File \u001b[1;32mc:\\Users\\gubio\\anaconda3\\envs\\Strawberry-fields\\Lib\\site-packages\\matplotlib\\lines.py:1032\u001b[0m, in \u001b[0;36mLine2D.get_path\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m   1030\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"Return the `~matplotlib.path.Path` associated with this line.\"\"\"\u001b[39;00m\n\u001b[0;32m   1031\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_invalidy \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_invalidx:\n\u001b[1;32m-> 1032\u001b[0m     \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrecache\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m   1033\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_path\n",
            "File \u001b[1;32mc:\\Users\\gubio\\anaconda3\\envs\\Strawberry-fields\\Lib\\site-packages\\matplotlib\\lines.py:705\u001b[0m, in \u001b[0;36mLine2D.recache\u001b[1;34m(self, always)\u001b[0m\n\u001b[0;32m    703\u001b[0m     interpolation_steps \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m1\u001b[39m\n\u001b[0;32m    704\u001b[0m xy \u001b[38;5;241m=\u001b[39m STEP_LOOKUP_MAP[\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_drawstyle](\u001b[38;5;241m*\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_xy\u001b[38;5;241m.\u001b[39mT)\n\u001b[1;32m--> 705\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_path \u001b[38;5;241m=\u001b[39m Path(np\u001b[38;5;241m.\u001b[39masarray(xy)\u001b[38;5;241m.\u001b[39mT,\n\u001b[0;32m    706\u001b[0m                   _interpolation_steps\u001b[38;5;241m=\u001b[39minterpolation_steps)\n\u001b[0;32m    707\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_transformed_path \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[0;32m    708\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_invalidx \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mFalse\u001b[39;00m\n",
            "\u001b[1;31mKeyboardInterrupt\u001b[0m: "
          ]
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 480x500 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "prog = sf.Program(1)\n",
        "with prog.context as q:\n",
        "    S = Sgate(0.5)\n",
        "    Vac | q[0]\n",
        "    S   | q[0]\n",
        "    Rgate(np.pi/3) | q[0]\n",
        "\n",
        "eng.reset()\n",
        "state = eng.run(prog).state\n",
        "fig = plt.figure()\n",
        "X = np.linspace(-5, 5, 100)\n",
        "P = np.linspace(-5, 5, 100)\n",
        "Z = state.wigner(0, X, P)\n",
        "X, P = np.meshgrid(X, P)\n",
        "ax = fig.add_subplot(111, projection=\"3d\")\n",
        "ax.plot_surface(X, P, Z, cmap=\"RdYlGn\", lw=0.5, rstride=1, cstride=1)\n",
        "fig.set_size_inches(4.8, 5)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Note that the result was identical to the squeeze case with the changes in the parameter values."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Displaciment:\n",
        "\n",
        "\\begin{equation}\n",
        "\\hat{D}(\\alpha) = \\exp{( \\hat{a}\\hat{a}^{\\dagger} -\\hat{a}^*\\hat{a})}\n",
        "= \\exp{( -i\\sqrt{2}(Re(\\alpha)\\hat{p} -Im(\\alpha)\\hat{x} ) )},\n",
        "\\end{equation}\n",
        "\n",
        "where $\\alpha = re^{i\\theta}$ with magnitude $r \\ge 0$ and phase $\\theta \\in [0,2\\pi )$.\n",
        "\n",
        "In CQCV, this operation is used both for encoding and as part of the subroutines of quantum algorithms. When applied to the vacuum state, the operation causes a shift in the phase space of the quantum state, transforming the state $ \\ket{0}$ into $ \\hat{D}(\\alpha)\\ket{0} =\\ket{\\alpha} $."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "prog = sf.Program(1)\n",
        "with prog.context as q:\n",
        "    Vac | q[0]\n",
        "    Dgate(-1.4) | q[0]\n",
        "\n",
        "eng.reset()\n",
        "state = eng.run(prog).state\n",
        "fig = plt.figure()\n",
        "X = np.linspace(-5, 5, 100)\n",
        "P = np.linspace(-5, 5, 100)\n",
        "Z = state.wigner(0, X, P)\n",
        "X, P = np.meshgrid(X, P)\n",
        "ax = fig.add_subplot(111, projection=\"3d\")\n",
        "ax.plot_surface(X, P, Z, cmap=\"RdYlGn\", lw=0.5, rstride=1, cstride=1)\n",
        "fig.set_size_inches(4.8, 5)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "#### Non-Gaussian Gates\n",
        "\n",
        "Non-Gaussian gates, also called nonlinear gates, include cubic phase and $\\textit{Kerr}$ gates, characterized by the parameters $\\gamma$ and $\\kappa$, respectively. They are described by:\n",
        "\n",
        "\\begin{equation*}\n",
        "\\hat{V}(\\gamma) = \\exp{(i\\gamma \\hat{x}^3 /3 ) },\n",
        "\\end{equation*}\n",
        "\n",
        "and\n",
        "\n",
        "\\begin{equation*}\n",
        "\\hat{K}(\\kappa) = \\exp{(i\\kappa (\\hat{a}^{\\dagger}\\hat{a})^2 ) }.\n",
        "\\end{equation*}"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "prog = sf.Program(1)\n",
        "with prog.context as q:\n",
        "    gamma = 0.5\n",
        "    Vgate(gamma) | q[0]\n",
        "\n",
        "#We use the fock backend and define the argument cutoff_dim\n",
        "eng = sf.Engine('fock', backend_options={\"cutoff_dim\": 10})\n",
        "state = eng.run(prog).state\n",
        "\n",
        "fig = plt.figure()\n",
        "X = np.linspace(-5, 5, 100)\n",
        "P = np.linspace(-5, 5, 100)\n",
        "Z = state.wigner(0, X, P)\n",
        "X, P = np.meshgrid(X, P)\n",
        "ax = fig.add_subplot(111, projection=\"3d\")\n",
        "ax.plot_surface(X, P, Z, cmap=\"RdYlGn\", lw=0.5, rstride=1, cstride=1)\n",
        "fig.set_size_inches(4.8, 5)\n",
        "ax.set_axis_off()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "An important detail is the cutoff value, it defines the size of the Hilbert space (or better Fock space) in which we will simulate our system, the larger it is, the heavier the simulation will be."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "The last part of the circuit is the measurement in strawberryfield we have\n",
        "\n",
        "* homodyne detection (MeasureHomodyne, as well as the shortcuts MeasureX and MeasureP),\n",
        "* heterodyne detection (MeasureHD), and photon detection\n",
        "\n",
        "Let's create a circuit to learn how to implement the measurements"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "prog = sf.Program(1)\n",
        "with prog.context as q:\n",
        "    # inicial state \n",
        "    Vac | q[0]\n",
        "\n",
        "    # Apply the Displacement gate to qumode 0\n",
        "    alpha = 2.0 + 1j\n",
        "    Dgate(np.abs(alpha), np.angle(alpha)) | q[0]\n",
        "\n",
        "    # Apply the Rotation gate\n",
        "    Rgate(3.14 / 2) | q[0]\n",
        "\n",
        "    # Apply the Squeezing gate\n",
        "    Sgate(1.0, 0.1) | q[0]\n",
        "\n",
        "\n",
        "    # Homodyne measurement at angle phi\n",
        "    phi = np.pi/4\n",
        "    MeasureFock() | q[0]\n",
        "\n",
        "#We use the fock backend and define the argument cutoff_dim\n",
        "eng = sf.Engine('gaussian', backend_options={\"cutoff_dim\": 3})\n",
        "state = eng.run(prog,shots=10)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Note that I changed the back-engine in the library at strawbarryfields we have several options see the link[https://strawberryfields.readthedocs.io/en/stable/code/sf_backends.html?highlight=backend] .\n",
        "\n",
        "We changed the back-engine to increase the number of shots to 10."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "state.samples"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Each sample represents a complete execution of the circuit. The numbers indicate the number of photons found in the circuit. Using these values, we can post-process the measurements to use as a result for some application.\n",
        "\n",
        "For each application we will modify the circuit, base size, number of modes (here we use only 1 for illustration), and the way of measuring.\n",
        "\n",
        "This tutorial is just your first step in your learning process.\n",
        "\n",
        "As a future recommendation I recommend accessing the xanadu/pennylane and Strawbarryfields websites which contain several tutorials.\n",
        "\n",
        "In particular I would like to make it clear that this tutorial was inspired by the public educational material from pennylane and Strawbarryfields\n",
        "\n",
        "https://strawberryfields.ai/photonics/demos/run_blackbird.html\n",
        "\n",
        "https://strawberryfields.ai/photonics/demos/run_teleportation.html\n",
        "\n",
        "https://strawberryfields.ai/photonics/demos/run_gate_visualization.html"
      ]
    }
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