LLMs-Pharmaceutical / Git / [404218] /Code/PennyLane/Benchmarking Devices II/lightning.kokkos 25 qubit TPU 44.28s (1) kkawchak.ipynb

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[404218]: / Code / PennyLane / Benchmarking Devices II / lightning.kokkos 25 qubit TPU 44.28s (1) kkawchak.ipynb
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{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "id": "rqYMIG5_jAmf",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "outputId": "faadd8d6-b814-4899-f814-97f0a16af156"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Time in seconds since beginning of run: 1701146305.5387535\n",
            "Tue Nov 28 04:38:25 2023\n"
          ]
        }
      ],
      "source": [
        "# This cell is added by sphinx-gallery\n",
        "# It can be customized to whatever you like\n",
        "%matplotlib inline\n",
        "\n",
        "# !pip install pennylane 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": 14,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "30MBcn8AjAmh",
        "outputId": "66c80e05-39aa-452d-febb-2920f3c1cb4e"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[0.14 0.2  0.39]\n",
            " [0.2  0.25 0.46]\n",
            " [0.39 0.46 0.93]]\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": 15,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "9AlMlrsVjAmi",
        "outputId": "fb8c018d-bc90-41c4-8ab5-7878605c2cbb"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[0.25 0.46 0.2 ]\n",
            " [0.46 0.93 0.39]\n",
            " [0.2  0.39 0.14]]\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": 16,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 487
        },
        "id": "Q7N1C_MKjAmi",
        "outputId": "95acc275-b6b0-4b8b-e308-4636429190b4"
      },
      "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": 17,
      "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": 18,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 53
        },
        "id": "0oMs3Tu0jAmi",
        "outputId": "d6862b4f-5aa6-43f3-8b46-765737799cc2"
      },
      "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": 19,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "ZCFN2RkSjAmj",
        "outputId": "d721f6a6-b43e-4369-9a55-62c1dd6d9122"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model output for A: 0.00017590236649367622\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": 20,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "KjwEdmGXjAmj",
        "outputId": "adabe523-864f-4620-838e-baf3362cbc7e"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model output for permutation of A:  0.6871668635277014\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": 21,
      "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": 22,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "tpzF6L3TjAmj",
        "outputId": "66e371e3-838b-44f3-88ff-40abd75f662c"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model output for permutation of A, and with permuted observable:  0.06503572592627782\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": "22d77e42-e3d1-4149-8164-bf1fbc5b009a"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Time in seconds since end of run: 1701146349.8203373\n",
            "Tue Nov 28 04:39:09 2023\n"
          ]
        }
      ]
    }
  ],
  "metadata": {
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      "display_name": "Python 3",
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      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
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    },
    "colab": {
      "provenance": [],
      "machine_shape": "hm"
    },
    "accelerator": "TPU"
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}