129 lines (129 with data), 19.7 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": 255
        },
        "id": "lcrQHMBnMgTz",
        "outputId": "6dfe78e2-9c22-43cd-8e05-cefcffa5bf1a"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(<Figure size 1100x200 with 1 Axes>, <Axes: >)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1100x200 with 1 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ],
      "source": [
        "import pennylane as qml\n",
        "\n",
        "dev = qml.device(\"default.qubit\", wires=1)\n",
        "\n",
        "@qml.qnode(dev)\n",
        "def circuit(params):\n",
        "    qml.RY(params[0], wires=0)\n",
        "    qml.RY(params[0], wires=0)\n",
        "    qml.RY(params[0], wires=0)\n",
        "    qml.RY(params[0], wires=0)\n",
        "    qml.RY(params[0], wires=0)\n",
        "    qml.RY(params[0], wires=0)\n",
        "    qml.RY(params[0], wires=0)\n",
        "    qml.RY(params[0], wires=0)\n",
        "    return qml.expval(qml.PauliZ(0))\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": "269b46c9-53da-4d26-bf6a-7dd9d2877ab6",
        "tags": []
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'resources': Resources(num_wires=1, num_gates=8, gate_types=defaultdict(<class 'int'>, {'RY': 8}), gate_sizes=defaultdict(<class 'int'>, {1: 8}), depth=8, shots=Shots(total_shots=None, shot_vector=())),\n",
              " 'num_observables': 1,\n",
              " 'num_diagonalizing_gates': 0,\n",
              " 'num_trainable_params': 0,\n",
              " 'num_device_wires': 1,\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
}