145 lines (145 with data), 38.6 kB

{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "id": "D7avgus1L7Ea",
        "tags": []
      },
      "outputs": [],
      "source": [
        "# !pip install pennylane --quiet # run once"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 352
        },
        "id": "lcrQHMBnMgTz",
        "outputId": "79a869e0-d984-4c37-cba1-b81134a8fffc"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(<Figure size 1500x300 with 1 Axes>, <Axes: >)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1500x300 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "import pennylane as qml\n",
        "\n",
        "dev = qml.device(\"default.qubit\", wires=2)\n",
        "\n",
        "@qml.qnode(dev)\n",
        "def circuit(params):\n",
        "    qml.RY(params[0], wires=0)\n",
        "    qml.RY(params[0], wires=1)\n",
        "    qml.RY(params[0], wires=0)\n",
        "    qml.RY(params[0], wires=1)\n",
        "    qml.RY(params[0], wires=0)\n",
        "    qml.RY(params[0], wires=1)\n",
        "    qml.RY(params[0], wires=0)\n",
        "    qml.RY(params[0], wires=1)\n",
        "    qml.RY(params[0], wires=0)\n",
        "    qml.RY(params[0], wires=1)\n",
        "    qml.RY(params[0], wires=0)\n",
        "    qml.RY(params[0], wires=1)\n",
        "    qml.RY(params[0], wires=0)\n",
        "    qml.RY(params[0], wires=1)\n",
        "    qml.RY(params[0], wires=0)\n",
        "    qml.RY(params[0], wires=1)\n",
        "    qml.RY(params[0], wires=0)\n",
        "    qml.RY(params[0], wires=1)\n",
        "    qml.RY(params[0], wires=0)\n",
        "    qml.RY(params[0], wires=1)\n",
        "    qml.RY(params[0], wires=0)\n",
        "    qml.RY(params[0], wires=1)\n",
        "    qml.RY(params[0], wires=0)\n",
        "    qml.RY(params[0], wires=1)\n",
        "    return qml.expval(qml.PauliZ(0)), qml.expval(qml.PauliZ(1))\n",
        "params =[0]\n",
        "\n",
        "print(qml.draw_mpl(circuit, style='sketch')(params))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "ovTzqCRW3ot6",
        "outputId": "eb2171c2-8c23-42e2-b69e-9d133322a173",
        "tags": []
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'resources': Resources(num_wires=2, num_gates=24, gate_types=defaultdict(<class 'int'>, {'RY': 24}), gate_sizes=defaultdict(<class 'int'>, {1: 24}), depth=12, shots=Shots(total_shots=None, shot_vector=())),\n",
              " 'num_observables': 2,\n",
              " 'num_diagonalizing_gates': 0,\n",
              " 'num_trainable_params': 0,\n",
              " 'num_device_wires': 2,\n",
              " 'device_name': 'default.qubit',\n",
              " 'expansion_strategy': 'gradient',\n",
              " 'gradient_options': {},\n",
              " 'interface': 'auto',\n",
              " 'diff_method': 'best',\n",
              " 'gradient_fn': 'backprop'}"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ],
      "source": [
        "qml.specs(circuit)(params)"
      ]
    }
  ],
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3 (ipykernel)",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.10.8"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}