LLMs-Pharmaceutical / Git / [404218] /Code/PennyLane/Benchmarking Devices II/lightning.kokkos 25 qubit CPU 65.07s (5) kkawchak.ipynb

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[404218]: / Code / PennyLane / Benchmarking Devices II / lightning.kokkos 25 qubit CPU 65.07s (5) kkawchak.ipynb
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{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 57,
      "metadata": {
        "id": "rqYMIG5_jAmf",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "outputId": "861d9abb-6d8f-440c-d242-f30f70921274"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Time in seconds since beginning of run: 1701143094.0957246\n",
            "Tue Nov 28 03:44:54 2023\n"
          ]
        }
      ],
      "source": [
        "# This cell is added by sphinx-gallery\n",
        "# It can be customized to whatever you like\n",
        "%matplotlib inline\n",
        "# !pip install pennylane custatevec-cu11 pennylane-lightning-kokkos\n",
        "\n",
        "import time\n",
        "seconds = time.time()\n",
        "print(\"Time in seconds since beginning of run:\", seconds)\n",
        "local_time = time.ctime(seconds)\n",
        "print(local_time)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 58,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "30MBcn8AjAmh",
        "outputId": "588f66ce-fded-4b40-89d5-b716c302aae1"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[0.12 0.53 0.67]\n",
            " [0.53 0.4  0.74]\n",
            " [0.67 0.74 0.88]]\n"
          ]
        }
      ],
      "source": [
        "import numpy as np\n",
        "import networkx as nx\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "\n",
        "def create_data_point(n):\n",
        "    \"\"\"\n",
        "    Returns a random undirected adjacency matrix of dimension (n,n).\n",
        "    The diagonal elements are interpreted as node attributes.\n",
        "    \"\"\"\n",
        "    mat = np.random.rand(n, n)\n",
        "    A = (mat + np.transpose(mat))/2\n",
        "    return np.round(A, decimals=2)\n",
        "\n",
        "A = create_data_point(3)\n",
        "print(A)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fZNWu6j_jAmh"
      },
      "source": [
        "Let\\'s also write a function to generate permuted versions of this\n",
        "adjacency matrix.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 59,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "9AlMlrsVjAmi",
        "outputId": "befa3f21-36f8-414a-f76b-e1106f2b4307"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[0.4  0.74 0.53]\n",
            " [0.74 0.88 0.67]\n",
            " [0.53 0.67 0.12]]\n"
          ]
        }
      ],
      "source": [
        "def permute(A, permutation):\n",
        "    \"\"\"\n",
        "    Returns a copy of A with rows and columns swapped according to permutation.\n",
        "    For example, the permutation [1, 2, 0] swaps 0->1, 1->2, 2->0.\n",
        "    \"\"\"\n",
        "\n",
        "    P = np.zeros((len(A), len(A)))\n",
        "    for i,j in enumerate(permutation):\n",
        "        P[i,j] = 1\n",
        "\n",
        "    return P @ A @ np.transpose(P)\n",
        "\n",
        "A_perm = permute(A, [1, 2, 0])\n",
        "print(A_perm)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Js9CqRTZjAmi"
      },
      "source": [
        "If we create [networkx]{.title-ref} graphs from both adjacency matrices\n",
        "and plot them, we see that they are identical as claimed.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 60,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 487
        },
        "id": "Q7N1C_MKjAmi",
        "outputId": "84dddd92-a961-4ee1-833a-8d78cf4d38df"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "fig, (ax1, ax2) = plt.subplots(1, 2)\n",
        "\n",
        "# interpret diagonal of matrix as node attributes\n",
        "node_labels = {n: A[n,n] for n in range(len(A))}\n",
        "np.fill_diagonal(A, np.zeros(len(A)))\n",
        "\n",
        "G1 = nx.Graph(A)\n",
        "pos1=nx.spring_layout(G1)\n",
        "nx.draw(G1, pos1, labels=node_labels, ax=ax1, node_size = 800, node_color = \"#ACE3FF\")\n",
        "edge_labels = nx.get_edge_attributes(G1,'weight')\n",
        "nx.draw_networkx_edge_labels(G1,pos1,edge_labels=edge_labels, ax=ax1)\n",
        "\n",
        "# interpret diagonal of permuted matrix as node attributes\n",
        "node_labels = {n: A_perm[n,n] for n in range(len(A_perm))}\n",
        "np.fill_diagonal(A_perm, np.zeros(len(A)))\n",
        "\n",
        "G2 = nx.Graph(A_perm)\n",
        "pos2=nx.spring_layout(G2)\n",
        "nx.draw(G2, pos2, labels=node_labels, ax=ax2, node_size = 800, node_color = \"#ACE3FF\")\n",
        "edge_labels = nx.get_edge_attributes(G2,'weight')\n",
        "nx.draw_networkx_edge_labels(G2,pos2,edge_labels=edge_labels, ax=ax2)\n",
        "\n",
        "ax1.set_xlim([1.2*x for x in ax1.get_xlim()])\n",
        "ax2.set_xlim([1.2*x for x in ax2.get_xlim()])\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 61,
      "metadata": {
        "id": "eUhWZ9_3jAmi"
      },
      "outputs": [],
      "source": [
        "import pennylane as qml\n",
        "\n",
        "def perm_equivariant_embedding(A, betas, gammas):\n",
        "    \"\"\"\n",
        "    Ansatz to embedd a graph with node and edge weights into a quantum state.\n",
        "\n",
        "    The adjacency matrix A contains the edge weights on the off-diagonal,\n",
        "    as well as the node attributes on the diagonal.\n",
        "\n",
        "    The embedding contains trainable weights 'betas' and 'gammas'.\n",
        "    \"\"\"\n",
        "    n_nodes = len(A)\n",
        "    n_layers = len(betas) # infer the number of layers from the parameters\n",
        "\n",
        "    # initialise in the plus state\n",
        "    for i in range(n_nodes):\n",
        "        qml.Hadamard(i)\n",
        "\n",
        "    for l in range(n_layers):\n",
        "\n",
        "        for i in range(n_nodes):\n",
        "            for j in range(i):\n",
        "            \t# factor of 2 due to definition of gate\n",
        "                qml.IsingZZ(2*gammas[l]*A[i,j], wires=[i,j])\n",
        "\n",
        "        for i in range(n_nodes):\n",
        "            qml.RX(A[i,i]*betas[l], wires=i)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "glOVsqRvjAmi"
      },
      "source": [
        "We can use this ansatz in a circuit.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 62,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 69
        },
        "id": "0oMs3Tu0jAmi",
        "outputId": "98cbd434-38d5-4d90-829c-4becc57680b7"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 58400x2600 with 1 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ],
      "source": [
        "n_qubits = 25\n",
        "n_layers = 2\n",
        "\n",
        "dev = qml.device(\"lightning.kokkos\", wires=n_qubits)\n",
        "\n",
        "@qml.qnode(dev)\n",
        "def eqc(adjacency_matrix, observable, trainable_betas, trainable_gammas):\n",
        "    \"\"\"Circuit that uses the permutation equivariant embedding\"\"\"\n",
        "\n",
        "    perm_equivariant_embedding(adjacency_matrix, trainable_betas, trainable_gammas)\n",
        "    return qml.expval(observable)\n",
        "\n",
        "\n",
        "A = create_data_point(n_qubits)\n",
        "betas = np.random.rand(n_layers)\n",
        "gammas = np.random.rand(n_layers)\n",
        "observable = qml.PauliX(0) @ qml.PauliX(1) @ qml.PauliX(3)\n",
        "\n",
        "qml.draw_mpl(eqc, decimals=2)(A, observable, betas, gammas)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TIHRQVnDjAmi"
      },
      "source": [
        "Validating the equivariance\n",
        "===========================\n",
        "\n",
        "Let\\'s now check if the circuit is really equivariant!\n",
        "\n",
        "This is the expectation value we get using the original adjacency matrix\n",
        "as an input:\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 63,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "ZCFN2RkSjAmj",
        "outputId": "bd740ba1-2f2c-48a2-fc5b-ddeaccc9d5d0"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model output for A: -0.0018115966556443183\n"
          ]
        }
      ],
      "source": [
        "result_A = eqc(A, observable, betas, gammas)\n",
        "print(\"Model output for A:\", result_A)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ncmkAwH-jAmj"
      },
      "source": [
        "If we permute the adjacency matrix, this is what we get:\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 64,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "KjwEdmGXjAmj",
        "outputId": "6567dcbb-b33e-458e-9b8c-e40943888a33"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model output for permutation of A:  0.16152411267326436\n"
          ]
        }
      ],
      "source": [
        "perm = [2, 3, 0, 1, 4]\n",
        "A_perm = permute(A, perm)\n",
        "result_Aperm = eqc(A_perm, observable, betas, gammas)\n",
        "print(\"Model output for permutation of A: \", result_Aperm)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZOIqQmysjAmj"
      },
      "source": [
        "Why are the two values different? Well, we constructed an *equivariant*\n",
        "ansatz, not an *invariant* one! Remember, an *invariant* ansatz means\n",
        "that embedding a permutation of the adjacency matrix leads to the same\n",
        "state as an embedding of the original matrix. An *equivariant* ansatz\n",
        "embeds the permuted adjacency matrix into a state where the qubits are\n",
        "permuted as well.\n",
        "\n",
        "As a result, the final state before measurement is only the same if we\n",
        "permute the qubits in the same manner that we permute the input\n",
        "adjacency matrix. We could insert a permutation operator\n",
        "`qml.Permute(perm)` to achieve this, or we simply permute the wires of\n",
        "the observables!\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 65,
      "metadata": {
        "id": "MgYVIMP6jAmj"
      },
      "outputs": [],
      "source": [
        "observable_perm = qml.PauliX(perm[0]) @ qml.PauliX(perm[1]) @ qml.PauliX(perm[3])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pb21hBrLjAmj"
      },
      "source": [
        "Now everything should work out!\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 66,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "tpzF6L3TjAmj",
        "outputId": "b8016b29-2a35-45bc-a85b-e2ea41e4c52c"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model output for permutation of A, and with permuted observable:  0.22855804765603382\n"
          ]
        }
      ],
      "source": [
        "result_Aperm = eqc(A_perm, observable_perm, betas, gammas)\n",
        "print(\"Model output for permutation of A, and with permuted observable: \", result_Aperm)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0wblJk9mjAmj"
      },
      "source": [
        "Et voilà!\n",
        "\n",
        "Conclusion\n",
        "==========\n",
        "\n",
        "Equivariant graph embeddings can be combined with other equivariant\n",
        "parts of a quantum machine learning pipeline (like measurements and the\n",
        "cost function). [Skolik et al.\n",
        "(2022)](https://arxiv.org/pdf/2205.06109.pdf), for example, use such a\n",
        "pipeline as part of a reinforcement learning scheme that finds heuristic\n",
        "solutions for the traveling salesman problem. Their simulations compare\n",
        "a fully equivariant model to circuits that break permutation\n",
        "equivariance and show that it performs better, confirming that if we\n",
        "know about structure in our data, we should try to use this knowledge in\n",
        "machine learning.\n",
        "\n",
        "References\n",
        "==========\n",
        "\n",
        "1.  Andrea Skolik, Michele Cattelan, Sheir Yarkoni,Thomas Baeck and\n",
        "    Vedran Dunjko (2022). Equivariant quantum circuits for learning on\n",
        "    weighted graphs.\n",
        "    [arXiv:2205.06109](https://arxiv.org/abs/2205.06109)\n",
        "2.  Quynh T. Nguyen, Louis Schatzki, Paolo Braccia, Michael Ragone,\n",
        "    Patrick J. Coles, Frédéric Sauvage, Martín Larocca and Marco Cerezo\n",
        "    (2022). Theory for Equivariant Quantum Neural Networks.\n",
        "    [arXiv:2210.08566](https://arxiv.org/abs/2210.08566)\n",
        "\n",
        "About the author\n",
        "================\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "seconds = time.time()\n",
        "print(\"Time in seconds since end of run:\", seconds)\n",
        "local_time = time.ctime(seconds)\n",
        "print(local_time)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "HFTIJBPqph2f",
        "outputId": "aa962e0c-97bf-4fbc-c3e0-9530b16aa716"
      },
      "execution_count": 67,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Time in seconds since end of run: 1701143159.1691494\n",
            "Tue Nov 28 03:45:59 2023\n"
          ]
        }
      ]
    }
  ],
  "metadata": {
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
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    "language_info": {
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        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.9.17"
    },
    "colab": {
      "provenance": [],
      "machine_shape": "hm"
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}