LLMs-Pharmaceutical / Git / [404218] /Code/PennyLane/Benchmarking Devices II/cirq.qsim 25 qubit V100 154.45s (1) kkawchak.ipynb

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[404218]: / Code / PennyLane / Benchmarking Devices II / cirq.qsim 25 qubit V100 154.45s (1) kkawchak.ipynb
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{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 179,
      "metadata": {
        "id": "rqYMIG5_jAmf",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "outputId": "7d8b05a9-950d-4f54-dc2f-356ecfc215bc"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Time in seconds since beginning of run: 1695110991.3527076\n",
            "Tue Sep 19 08:09:51 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-gpu\n",
        "# !pip install Pennylane-Cirq\n",
        "# !pip install qsimcirq\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": 180,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "30MBcn8AjAmh",
        "outputId": "c94f7f56-185b-43a0-8958-3d6b3d22323d"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[0.82 0.49 0.68]\n",
            " [0.49 0.78 0.39]\n",
            " [0.68 0.39 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": 181,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "9AlMlrsVjAmi",
        "outputId": "7d9a3e24-f6c0-45af-9d12-5310418bc93a"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[0.78 0.39 0.49]\n",
            " [0.39 0.88 0.68]\n",
            " [0.49 0.68 0.82]]\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": 182,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 487
        },
        "id": "Q7N1C_MKjAmi",
        "outputId": "68b98593-166a-4b23-b08e-2e40b9da498a"
      },
      "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": 183,
      "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": 184,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 57
        },
        "id": "0oMs3Tu0jAmi",
        "outputId": "df990ff9-9522-41be-e6ab-cb69eb01df63"
      },
      "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(\"cirq.qsim\", 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": 185,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "ZCFN2RkSjAmj",
        "outputId": "bed96e09-3cb3-4316-bf09-19cf974afe67"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model output for A: -0.0009327541222280411\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": 186,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "KjwEdmGXjAmj",
        "outputId": "13e3f607-59c3-49ca-a9b5-96f4807edca3"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model output for permutation of A:  0.2668929351057159\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": 187,
      "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": 188,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "tpzF6L3TjAmj",
        "outputId": "0b2bc892-94c4-46cb-a569-2042d28afde3"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model output for permutation of A, and with permuted observable:  0.3188169707718771\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": "c25bae1e-97a1-4e4c-9ba4-a1f4c5ec2cb3"
      },
      "execution_count": 189,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Time in seconds since end of run: 1695111145.7981899\n",
            "Tue Sep 19 08:12:25 2023\n"
          ]
        }
      ]
    }
  ],
  "metadata": {
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      "nbconvert_exporter": "python",
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    "colab": {
      "provenance": [],
      "gpuType": "V100"
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    "accelerator": "GPU"
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}