Colab_21_Ansatze_e_Stima.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "authorship_tag": "ABX9TyMs3E8aoRWsPsgx4rvezIYS",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/EmmanueleVilla/quantum_computing/blob/main/Colab_21_Ansatze.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tPEiggyRiXkl",
        "outputId": "762f1e03-2025-4eb1-cf6a-06b35e183936"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "  Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n",
            "  Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n",
            "  Installing backend dependencies ... \u001b[?25l\u001b[?25hdone\n",
            "  Preparing metadata (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m6.1/6.1 MB\u001b[0m \u001b[31m14.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m2.0/2.0 MB\u001b[0m \u001b[31m23.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m49.6/49.6 kB\u001b[0m \u001b[31m3.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m115.3/115.3 kB\u001b[0m \u001b[31m8.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m49.6/49.6 kB\u001b[0m \u001b[31m2.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m37.5/37.5 MB\u001b[0m \u001b[31m15.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m112.7/112.7 kB\u001b[0m \u001b[31m8.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h  Building wheel for qiskit (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m162.6/162.6 kB\u001b[0m \u001b[31m2.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Building wheel for pylatexenc (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m12.8/12.8 MB\u001b[0m \u001b[31m30.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m4.1/4.1 MB\u001b[0m \u001b[31m19.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m47.2/47.2 MB\u001b[0m \u001b[31m10.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h"
          ]
        }
      ],
      "source": [
        "!pip install qiskit --quiet && pip install pylatexenc --quiet && pip install qiskit-aer --quiet && pip install qiskit[nature] --quiet && pip install pyscf --quiet"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from qiskit.circuit.library import TwoLocal\n",
        "from math import pi"
      ],
      "metadata": {
        "id": "XgSaiT1Fic1T"
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Inizializzazioni di stato"
      ],
      "metadata": {
        "id": "jP7ceGHVi5-l"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Stato di Hartree-Fock per un atomo con 2 orbitali spaziali e 2 elettroni\n",
        "\n",
        "from qiskit_nature.second_q.circuit.library import HartreeFock\n",
        "from qiskit_nature.second_q.mappers import JordanWignerMapper\n",
        "\n",
        "num_spatial_orbitals = 2\n",
        "num_particles = (1, 1)\n",
        "\n",
        "mapper = JordanWignerMapper()\n",
        "\n",
        "h2_reference_state = HartreeFock(\n",
        "    num_spatial_orbitals=num_spatial_orbitals,\n",
        "    num_particles=num_particles,\n",
        "    qubit_mapper=mapper,\n",
        ")\n",
        "\n",
        "h2_reference_state.decompose().draw(\"mpl\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 376
        },
        "id": "4aoSxDgki7LQ",
        "outputId": "2ed25625-2011-4b5d-9b9d-d3dc5bf97580"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/qiskit_nature/deprecation.py:297: PauliSumOpDeprecationWarning: PauliSumOp is deprecated as of version 0.6.0 and support for them will be removed no sooner than 3 months after the release. Instead, use SparsePauliOp. You can switch to SparsePauliOp immediately, by setting `qiskit_nature.settings.use_pauli_sum_op` to `False`.\n",
            "  return func(*args, **kwargs)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Figure size 203.885x367.889 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {},
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Stato di Hartree-Fock per una molecola di Idruro di litio\n",
        "\n",
        "from qiskit_nature.second_q.drivers import PySCFDriver\n",
        "from qiskit_nature.second_q.circuit.library import HartreeFock\n",
        "from qiskit_nature.second_q.mappers import JordanWignerMapper\n",
        "from qiskit_nature.second_q.formats.molecule_info import MoleculeInfo\n",
        "\n",
        "molecule = MoleculeInfo(\n",
        "    # Coordinates in Angstrom\n",
        "    symbols=[\"Li\", \"H\"],\n",
        "    coords=([0.0, 0.0, 0.0], [0.2, 0.0, 0.0]),\n",
        "    multiplicity=1,  # = 2*spin + 1\n",
        "    charge=0,\n",
        ")\n",
        "\n",
        "driver = PySCFDriver.from_molecule(molecule)\n",
        "LiH_problem = driver.run()\n",
        "\n",
        "mapper = JordanWignerMapper()\n",
        "LiH_reference_state = HartreeFock(\n",
        "    num_spatial_orbitals=LiH_problem.num_spatial_orbitals,\n",
        "    num_particles=LiH_problem.num_particles,\n",
        "    qubit_mapper=mapper,\n",
        ")\n",
        "\n",
        "LiH_reference_state.decompose().draw(\"mpl\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 891
        },
        "id": "riBvgI2GjUT9",
        "outputId": "49f7183b-4355-41de-b294-2cfeb7ed4ead"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/pyscf/dft/libxc.py:772: UserWarning: Since PySCF-2.3, B3LYP (and B3P86) are changed to the VWN-RPA variant, the same to the B3LYP functional in Gaussian and ORCA (issue 1480). To restore the VWN5 definition, you can put the setting \"B3LYP_WITH_VWN5 = True\" in pyscf_conf.py\n",
            "  warnings.warn('Since PySCF-2.3, B3LYP (and B3P86) are changed to the VWN-RPA variant, '\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Figure size 221.914x1036.78 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Stato di preparazione per quantum machine learning\n",
        "\n",
        "from qiskit.circuit.library import ZZFeatureMap\n",
        "\n",
        "data = [0.1, 0.2]\n",
        "\n",
        "zz_feature_map_reference = ZZFeatureMap(feature_dimension=2, reps=2)\n",
        "zz_feature_map_reference = zz_feature_map_reference.bind_parameters(data)\n",
        "zz_feature_map_reference.decompose().draw(\"mpl\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 191
        },
        "id": "9z2WWjX_lEU5",
        "outputId": "1b832027-5202-4144-bad5-24a58b32408b"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Figure size 956.183x200.667 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "RluT511GpbHS"
      },
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Preparazione dell'Ansatz"
      ],
      "metadata": {
        "id": "SPen5LaLqOXH"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Circuito parametrizzato\n",
        "from qiskit.circuit import QuantumCircuit, Parameter\n",
        "\n",
        "theta = Parameter(\"θ\")\n",
        "\n",
        "qc = QuantumCircuit(3)\n",
        "qc.rx(theta, 0)\n",
        "qc.cx(0, 1)\n",
        "qc.x(2)\n",
        "\n",
        "qc.draw(\"mpl\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 255
        },
        "id": "e3fMJMGbqP4D",
        "outputId": "c7df4d3b-b6fc-41ca-d816-5ce6c5c23b0c"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Figure size 287.496x284.278 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Bindo i parametri\n",
        "from math import pi\n",
        "\n",
        "theta = Parameter(\"θ\")\n",
        "\n",
        "qc = QuantumCircuit(3)\n",
        "qc.rx(theta, 0)\n",
        "qc.cx(0, 1)\n",
        "qc.x(2)\n",
        "\n",
        "angle_list = [pi / 3, pi / 2]\n",
        "circuits = [qc.bind_parameters({theta: angle}) for angle in angle_list]\n",
        "\n",
        "for circuit in circuits:\n",
        "    display(circuit.draw(\"mpl\"))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 493
        },
        "id": "v6RxW8SuqS4R",
        "outputId": "7e4a2542-5bee-4c45-c6cd-2d3e15c44502"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 287.496x284.278 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 287.496x284.278 with 1 Axes>"
            ],
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAPEAAADuCAYAAADoS+FHAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAAA9hAAAPYQGoP6dpAAAW20lEQVR4nO3de3TU5b3v8fdMIOQOhqQGSLgEiJBAghJTgi0ajN0gIFWhmzbi5eD21EphHZGcrno8p66uLU2Nl82hVaj1YPfe0mxpywZStrgE20gRgxFFiEQCwdxGHbkmBCSZ3/ljJJqSQCaZS55fPq+1siC/2/OFmc88z+86DsuyLETEWM5QFyAivaMQixhOIRYxnEIsYjiFWMRwCrGI4RRiEcMpxCKGU4hFDKcQixhOIRYxnEIsYjiFWMRwCrGI4RRiEcMpxCKGU4hFDKcQixhOIRYxnEIsYjiFWMRwCrGI4RRiEcMpxCKGU4hFDKcQixhOIRYxnEIsYjiFWMRwCrGI4RRiEcMpxCKGU4hFDKcQixhOIRYxnEIsYjiFWMRwA0JdgHTOssBzIdRVdJ9zIDgcoa6if1KI+yjPBdi5OtRVdF/eMggLD3UV/ZOG0yKGU4hFDKcQixhOIRYxnEIsYjiFWMRwCrGI4XSe2Ebeq36DR57P6zAtIjya5MQ08q9bzHdv+DFhYXrJ7UavqA3lTfk+ORNuxcLixBkXr73zO57f8jAff1rJ/1iwLtTliZ8pxDY0fsR15E+9q/33edN/xJJfTmDb2y9w36x/ZkhMYgirE3/TPnE/EBkezYRR07Asi4bPq0NdjviZQtxPNH4Z3rio+BBXIv6m4bQNnbtwllPNbizLu0+8ZffzHK5/lwkpOSQnpoW6PPGzftETu91uCgsLGTduHBEREaSkpLB8+XKam5tZsmQJDoeDNWvWhLpMv/nd9v/Dgp8lsvDxb/DA05ls2f1rvjXpDh6/9z9DXVpIWBZUfwL/sQde+Au8+FfY/C58ejrUlfmH7Xviffv2MXv2bFwuF9HR0aSnp9PQ0MDq1auprq7m+PHjAEyZMiW0hfrRnG8+wIzMhbR6LnC0cT8lbxThPlVH+MCI9mW+aD3Pj569jrxrf0DBzY+2T//l7+/lZNMnPHH/tlCU7neVDfCfFeA6dem8HQfhmiRYkAOJscGvzV9s3RO73W7mzZuHy+VixYoVNDY2UlFRgcvloqioiNLSUsrLy3E4HGRmZoa6XL8ZkTCe69LyyZkwm3/MK+Tn923hUF05//KHH7YvEz5gEIWLfsfvX3+C6ob3ANj1wSbeqtzCwwt/G6rS/eqdGvjNG50H+KJDLnj2VWg4Eayq/M/WIV62bBl1dXUsXbqU4uJiYmO/+rgtLCwkKyuL1tZWRo8eTVxcXAgrDayM0dPJv24xb7xXwoGav7VPT0ueyoIbH+GXv7+bz07W8ezGB/jx7b8iYfDwEFbrH8fc8PJu8FhXXrb5PKx7A1q+CHhZAWHbEFdWVlJSUkJCQgKrVq3qdJmpU6cCkJWV1WH60aNHue2224iNjeWqq67i7rvv5vPPPw94zYFUkP8YTmcYL736v/9u+v8izDmAB5+9lqxxeeRNWRSiCv1rRyW0ebq//MmzUH40cPUEkm1DvGHDBjweDwUFBcTExHS6TGRkJNAxxGfOnCEvL4+6ujo2bNjAunXrKCsrY+7cuXg8Prwr+pgRCePIy1rEu4dfZ/+RsvbpA8IGkj56Oqea3fxD9n0hrNB/Tp2F/bW+r/dmlfcgmGlsG+IdO3YAkJeX1+UydXV1QMcQr1u3jvr6ejZt2sTcuXNZuHAhL7/8Mm+99RabN28ObNEB9v2bH8XpcPLS9q964/1Hyti+dz3zb1jKrzcv5/yFlhBW6B+HXN0bRv+9T0/D8Wb/1xNoDssy8bPnylJSUqirq+Pdd9/t9Mhza2srw4YNw+12U11dTWpqKvBV6Hfu3Nlh+bFjx3LTTTfx29/6ftAnOzsbl8vl0zrhAyJZt/Qjn9vyRcv5Jv7701ncOeNh5uU+yIrnbyQtOZsHb3vG5209sGY8X7T2jQ+AsdPv49r5P+/Ruq89+x1ONR70c0VXlpSUxN69e3u0rm1PMTU3ez9SW1o6f2OVlJTgdruJjY1lzJgx7dMPHjzIwoULL1k+IyODgwd79uK6XC7q6+t9WidiYFSP2vLF2i0rSIofw23Tf4TD4WDl99bzw2encMOk28lMneHTthobGjh34WyAKvXN4E98+7/+uvraI5zqxfqhYNsQJyUlceLECSoqKsjNze0wr7GxkZUrVwKQmZmJ42sPTD5x4gRDhgy5ZHvx8fEcOnSox7X4KnxAZI/a6q63P9zGG++VsO7h99v//cMTxrJk9i8oLrmPtSveJzI8utvbGzZ8eJ/pia2mY94/LavDa3sl5858RuygNmJGjAhUaV3qyXvkItuGOD8/n8rKSoqKirjllltIS/NeblheXs7ixYtxu91AcC7y6Mkwqe2LwD53OmfCbDb9/OQl0+ff8BDzb3jI5+19VPVRn3ru9OrtcOQz355mP29aIs8fM+8QtW0PbBUWFjJ06FBqa2vJyMhg8uTJjB8/npycHFJTU5k5cyZw6emlq666ipMnT16yvePHjxMfr5sHTHHjBN+WHxAGueMCU0ug2TbEycnJlJWVMWfOHCIiIqipqSE+Pp61a9dSWlpKVVUVcGmIJ06c2Om+78GDB5k4cWJQapfeyxoJed18uRzA4ukQ3/mZyD7PtsNp8AZy69atl0xvamqipqYGp9PJpEmTOsybO3cuP/3pT6mrqyM5ORmAPXv2UF1dzZNPPhmUusU/brsWIsPh1f1dX/gRPQh+kAsZwd8N9hvbnmK6nD179jBt2jSuueYaPvzwww7zTp8+zeTJk0lISODxxx/n3LlzFBYWkpiYyO7du3E6gzN4CfQ+sb/15e9iajoHbx+BvTXQeAIsIMwJ35/m7bEHhoW6wt6x7XD6cvbv3w9cOpQGiIuLY8eOHQwbNoxFixZx//33M336dLZu3Rq0AIt/xUTAzHQovBXivjzoHzMIsseYH2Cw+XC6K5cLMXgv7OhsGC7SFynE/cxf39/Iux+9zoO3PcM///sijn1ykEEDIxkS8w2W3fEcIxIMPUTbj/XLEF+8rro/2vXBn8ifejcAt37zAXImzMbhcLBp1xqefuV+nnrwjdAWKD7rlyG2s6aWk/zTU5M4f6GFxMEpXGg7j+vzI9w8dTHL73iOAzW7WPmP6xkQNpBvTry1fb2JI6ex8S/FIaxcekohtpmYyCHMnPIDIgfFctctj1F+6FU27HiCFQtfYO+h7aSPms6AsIGXrPenN/+F3Iz5IahYekuHW23ocMM+xo24FoCP6t5h3HDv3/92YBM3TLr9kuVffv0JGtyHWTK784cnSN+mENvQkb8P8YhrsSyLvYdeJWfC7A7LvvJGMW9+8EeeuH8bEeGBv3NK/E8hthn3qXpwOEgY7L0E6YjrfcYkTebD2rcZefVEIgd9dW3hxr88zc59Gyj6p9eIiRwSooqlt7RPbDOH699tHz4DxEQMYfPuXzM4OoHpGd9tn/7ZyTrWbl3BsPjU9m9SDB8wiP+7bE+wS5ZeUohtZlr6XKalz23//VfLywG4vziDJ3/41dNKEock89qT/e6KW1tSiPuJFx45EOoSJEC0TyxiOIVYxHAKsYjhtE/cRzkHeu/RNYXz0ovAJEgU4j7K4ei7N9lL36LhtIjhFGIRwynEIoZTiEUMpxCLGE4hFjGcQixiOIVYxHAKsYjhFGIRwynEIoZTiEUMpxCLGE4hFjGcQixiOIVYxHAKsYjhFGIRwynEIoZTiEUMpxCLGE4hFjGcQixiOIVY+g2PBdaXXwRp2egLIR2WZad/jshXTp6FfR9D7edQexw+Ow1ff7OnxHt/RidCVgoMMvRbLBRisZ2PPoGyQ/BBnbf37Y5BA+D6VPj2NXB1XGDr8zeFWGyj+Tz8cS+8U9PzbYQ54R8mw83p3r+bQCEWW6hywb/ugjPn/LO9lHi499swNMY/2wskhViM934tvPQmtHn8u93BkfCjm+Hqwf7drr8ZMmAQ6VxlQ2ACDHCqBX79Onze5P9t+5NCLMY60wL/9rfABPiiU1+24QlgG72l7ycWI1kWvFLuPZjli4dnQVwknG6Bp/+re+sc/Qz+WgU3TfC9zmDoFz2x2+2msLCQcePGERERQUpKCsuXL6e5uZklS5bgcDhYs2ZNqMsUHxyo9+4L+youEoZEef/0Rek+OHXW9/aCwfY98b59+5g9ezYul4vo6GjS09NpaGhg9erVVFdXc/z4cQCmTJkS2kLFJ3/5MLjtXWiD3YdhVmZw2+0OW/fEbrebefPm4XK5WLFiBY2NjVRUVOByuSgqKqK0tJTy8nIcDgeZmX3w1ZFOuU55L+gItt2HA7v/3VO2DvGyZcuoq6tj6dKlFBcXExsb2z6vsLCQrKwsWltbGT16NHFxhl2m04+9czQ07Z5q8Z6P7mtsG+LKykpKSkpISEhg1apVnS4zdepUALKystqnXQx9Tk4OgwYNwuFwBKVe6b6PPw9d27UhbLsrtg3xhg0b8Hg8FBQUEBPT+WU3kZHeoxtfD/Hhw4f5wx/+QFJSEtdff31QapXusyzvzQyh8nEI2+6KbUO8Y8cOAPLy8rpcpq6uDugY4hkzZtDY2MjmzZvJz88PbJHis9Pn4OwXoWvfdSp0bXfFtkenjx07BsCoUaM6nd/a2squXbuAjiF2Ov3/uZadnY3L1Qd3pgwUM3QMswrLupx/8TxwV+IivvrzZ7d3vVxX55Fr6z8hOXlqN6vtvqSkJPbu3dujdW0b4ubmZgBaWlo6nV9SUoLb7SY2NpYxY8YEtBaXy0V9fX1A2+gvBrdGXHb+xfPAV+J0dm+5v9fW1tbnXkvbhjgpKYkTJ05QUVFBbm5uh3mNjY2sXLkSgMzMzIAfvEpKSgro9vuTQTGxl51/uvPP7HZxEd4Aezzeobmv27HazjNixIgrVOm73rxHbBvi/Px8KisrKSoq4pZbbiEtLQ2A8vJyFi9ejNvtBoJzkUdPh0lyKcuCRzd2vV98pUspf3a7twc+fQ5+9iff258+ZQy//fJYSl9h2wNbhYWFDB06lNraWjIyMpg8eTLjx48nJyeH1NRUZs6cCXTcH5a+z+Hw3usbKqFsuyu2DXFycjJlZWXMmTOHiIgIampqiI+PZ+3atZSWllJVVQUoxCYalRC6tkeGsO2u2HY4DTBx4kS2bt16yfSmpiZqampwOp1MmjQpBJVJb0wdA9s/CH67gyNh/NXBb/dKbB3irhw4cADLskhLSyMq6tJDlBs3bgTg4MGDHX4fPXo02dnZwStUOnV1HKQlBf8SyOnj++Zzt/pliPfv3w90PZReuHBhp7/fc889rF+/PqC1SffcOCG4IQ4Pg2njgteeLxTiTuixY31fxgiYMtL7XOlgmHutdzjdF/XBwUHgXSnEYoYF10P0IN/WOd3ifaj8lc4nf93Yb8C30nxrJ5j0tEsxWpUL1u4M3H2+Q6Jg+XfgqujAbN8f+mVPLPaRlgT3fTswB5yGRHkfWduXAwzqicUmPvoE/m2X98Z9fxg5FO79FsTr4fEiwXP2PGyqgLeP9HwbA5wwOxNumtg3Tyd1RiEW2znyKZRVwXsfd/8L1SLDIScVvp0GCZe/x6LPUYjFtk63eINce9z78+nprw6AhQ+A4UO810KPSoDMFO80EynE0q+0ebw3UTht9Og0hVjEcIbsuotIVxRiEcMpxCKGU4hFDKcQixhOIRYxnEIsYjiFWMRwCrGI4RRiEcMpxCKGU4hFDKcQixhOIRYxnEIsYjiFWMRwCrGI4RRiEcMpxCKGU4hFDKcQixhOIRYxnEIsYjhDn3lvf5ZlcdbTFuoyui3KGYbDYaMnshtEIe6jznrauGrHa6Euo9tOzLyF6DC9nUJBw2kRwynEIoZTiEUMpxCLGE4hFjGcQixiOIVYxHAKsYjhFGIRwynEIoZTiEUMpxCLGE4hFjGcQixiuH4RYrfbTWFhIePGjSMiIoKUlBSWL19Oc3MzS5YsweFwsGbNmlCXGRBtL67nwnduxfNf2y+ZZ1kWrY/8Ty7MuQ3raE3wixO/sP0NoPv27WP27Nm4XC6io6NJT0+noaGB1atXU11dzfHjxwGYMmVKaAsNEOfiAjxv7aFt7W9wTL0OR2JC+zzPHzdhvb8f53+7F8eY0aErUnrF1j2x2+1m3rx5uFwuVqxYQWNjIxUVFbhcLoqKiigtLaW8vByHw0FmZmaoyw0Ix8CBDFi5As6do+3pZ9unW7V1eNb/DseEa3AuvDN0BUqv2TrEy5Yto66ujqVLl1JcXExsbGz7vMLCQrKysmhtbWX06NHExcWFsNLAcowfh3PR97DeqcBTug2rrY22XxaDZRG2cgWOsLBQlyi9YNsQV1ZWUlJSQkJCAqtWrep0malTpwKQlZXVPm3jxo3ceeedjBo1iqioKCZMmMCjjz5KU1NTUOoOFGfB9yE1lbbfvIDnV89jHarCee/dOFKSQ12a9JJtQ7xhwwY8Hg8FBQXExMR0ukxkZCTQMcTFxcWEhYXxxBNPsG3bNh588EGee+45Zs2ahcfjCUrtgeAYMIABKx+GLy7g2VqKY1IGzju+G+qyxA9se2Brx44dAOTl5XW5TF1dHdAxxFu2bCExMbH99xtvvJHExEQKCgp48803mTFjhs+1ZGdn43K5fFrHCg+Htb/yua3Lio6GgQOhtRXH9dk4nP77DE8bn4bjiy/8tr3+Jikpib179/ZoXduG+NixYwCMGjWq0/mtra3s2rUL6Bjirwf4ouzsbADq6+t7VIvL5fJ93YhBDOxRa52zLIu2p56B1gswMgXPy7/HeeMMHMOH+WX7DY0NcO68X7YlvrFtiJubmwFoaWnpdH5JSQlut5vY2FjGjBlz2W3t3LkTgIkTJ/aolqSkJJ/XscLD+axHrXXOs2kz1nvv47zvHpy502h96Me0PfUMYcVFfnle9PBhw9UT90JP3iMX2TbESUlJnDhxgoqKCnJzczvMa2xsZOXKlQBkZmZe9k1cX1/PY489xqxZs3p8Lrknw6Tmtla/PXfaqq/H8+J6HNek4fzeAhxhYTjvKsDz/17Cs2kzYbfP73UbVR9V6bnTIWLbA1v5+fkAFBUVUVVV1T69vLycvLw83G43cPmLPJqampg/fz7h4eG8+OKLAa03UCyPh7YnnwaPh7CVD7efTnJ+bwGOtPF4XlyP1dAY4iqlN2wb4sLCQoYOHUptbS0ZGRlMnjyZ8ePHk5OTQ2pqKjNnzgQ67g9/XUtLC/PmzePo0aNs376dYcP8s+8YbJ6Nf8Q6WInznrtwjBzZPt0RFkbYIw+Dp422p57BsqwQVim9YdsQJycnU1ZWxpw5c4iIiKCmpob4+HjWrl1LaWlpe+/cWYgvXLjAggUL2Lt3L9u2bSM9PT3Y5fuF9fHHeF76VxwTJ+C8845L5jtGj8J5VwHW/g/wbNocggrFHxxWP/wIbmpqIi4uDofDwZkzZ4iKimqf5/F4WLRoEZs3b+bPf/5ze48dbP7cJw4GfRdT6PTL//UDBw5gWRZpaWkdAgzw0EMP8corr/CTn/yEqKgo3nrrrfZ5Y8eO7fQUlEgo2XY4fTn79+8HOh9Kb9u2DYBf/OIX5ObmdvgpLS0Nap0i3dEve+LLhbimpibI1Yj0jnpiEcP1y5744nXVInbQL3tiETtRiEUMpxCLGE4hFjGcQixiOIVYxHAKsYjhFGIRwynEIoZTiEUM1y/vJzaBZVmc9bSFuoxui3KG+eWBe+I7hVjEcBpOixhOIRYxnEIsYjiFWMRwCrGI4RRiEcMpxCKGU4hFDKcQixhOIRYxnEIsYjiFWMRwCrGI4RRiEcMpxCKGU4hFDKcQixhOIRYxnEIsYjiFWMRwCrGI4RRiEcMpxCKGU4hFDKcQixju/wMDrMNgJirbagAAAABJRU5ErkJggg==\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Circuiti n-locali\n",
        "\n",
        "from qiskit.circuit.library import NLocal, CCXGate, CRZGate, RXGate\n",
        "from qiskit.circuit import Parameter\n",
        "\n",
        "theta = Parameter(\"θ\")\n",
        "ansatz = NLocal(\n",
        "    num_qubits=5,\n",
        "    rotation_blocks=[RXGate(theta), CRZGate(theta)],\n",
        "    entanglement_blocks=CCXGate(),\n",
        "    entanglement=[ [0, 1, 2], [0, 2, 3], [4, 2, 1], [3, 1, 0] ],\n",
        "    reps=2,\n",
        "    insert_barriers=True,\n",
        ")\n",
        "ansatz.decompose().draw(\"mpl\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 384
        },
        "id": "kulefRKoqVmS",
        "outputId": "4d6e5f12-07ec-4cd6-b86a-bc799788b460"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Figure size 1625.27x451.5 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from qiskit.circuit.library import TwoLocal\n",
        "\n",
        "ansatz = TwoLocal(\n",
        "    num_qubits=5,\n",
        "    rotation_blocks=[\"rx\", \"rz\"],\n",
        "    entanglement_blocks=\"cx\",\n",
        "    entanglement=\"linear\",\n",
        "    reps=2,\n",
        "    insert_barriers=True,\n",
        ")\n",
        "ansatz.decompose().draw(\"mpl\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 384
        },
        "id": "TEvnA91IXENC",
        "outputId": "c34c9299-114d-44bc-bc55-9a077fceb9b8"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Figure size 1625.27x451.5 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Efficient SU 2\n",
        "from qiskit.circuit.library import EfficientSU2\n",
        "\n",
        "ansatz = EfficientSU2(4, su2_gates=[\"rx\", \"y\"], entanglement=\"linear\", reps=1)\n",
        "ansatz.decompose().draw(\"mpl\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 320
        },
        "id": "1fkgNfOgX45b",
        "outputId": "4ca78031-0763-4132-b6dc-98ab35668460"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Figure size 705.552x367.889 with 1 Axes>"
            ],
            "image/png": 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/zOVyUeQs912DAVDb7qjxk7A9PxdOnIa6EfDi7UZH8/PM1gfAt/1AfUDEc7pnpoay2cBx/hQxQcNmsxHp0I9nMFMfUB8Q8ZQuM4mIiIipqZgRERERU1MxIyIiIqamYkZERERMTcWMiIiImJqKGRERETE1FTMiIiJiaipmRERExNRUzIiIiIipqZgRERERU1MxIyIiIqamYkZERERMTcWMiIiImJqKGRERETE1FTMiIiJiaipmRERExNRUzIiIiIipqZgRERERUwsxOgCpmssFzlKjo/CcPRRsNt+153K5KHKW+67BAKhtd2Dz5T+CBDWz5QDwbR5QDhBvqJipoZylsGKm0VF4LnUMOMJ8116Rs5z6y5f6rsEAyO/dh0iHupT4htlyAPg2DygHiDd0mUlERERMTcWMiIiImJqKGRERETE1FTMiIiJiaipmRCzO6YIjJ2HjfjhT5t5WUga7D0GxyZ6WERGpim67FrGgcidsPwhf74G9R84vWk6Xwuufu//eqA50agrdW0PDqMDHKiJyqVTMiFiI0wVf7YLlO+B4kWfHHDkFy3a4j2kXD7d2hsbR/o1TRMSXVMxYyObMlTz5ZmqlbeFhkSQ0SiKt81B+2eMxHJoDwbKOnII5X8MPR6p3vAv3aM6uXLg5GXpdAXZdiDYd5QEJRvqJtqDUlHvo1uZmXLjIP5XL0u8+4M0F4/jx8E6euHO20eGJH2w/CO+tglIfTJhaWg7/3gD/yYZf9YRaoZfepgSe8oAEE33usqDW8Z1J63IffboM5a7rJzDzsbU0qpvA4m/f4nhBNT+2S4215QC8/YVvCpmf+j4X3lx+7qZhMRflAQkmKmaCQERYJG2aXYPL5SL7aKbR4YgP7cuD979y3yvjD3vz4P1V7nWCxNyUB8TKdJkpSOT8N3lF125gcCTiKyVl8Lc17ieXPDWuL0RHwMnTMO1Tz47ZkQ1r9kCP1tWLU2oO5QGxqqAYmcnLyyM9PZ3ExETCw8Np2rQpY8eOpbCwkBEjRmCz2XjttdeMDtNnikuLOFGYx/GCI+zN2crMuaPYc3AjbZp2I6FRktHhiY98stl90683oiOgXm33V2/M3wBHC7w7RoylPCDBxPIjM5s2baJfv37k5uYSGRlJu3btyM7OZubMmWRmZnLs2DEAUlJSjA3Uhz5Y8jwfLHm+0rbrOtzOYwNfNygi45T9/iVca77GMXUS9g7tz3vduW075U/+Blv3awn53bMGRFg9+YXwxfeBO9+ZMvhsK9x7beDO6Utl5e57i3YcdM+xExYCTRtAt5YQFW50dP6hPHCOVfOAnGPpkZm8vDwGDBhAbm4u48ePJycnhw0bNpCbm8ukSZNYtGgR69atw2az0alTJ6PD9Zlbrh7JpAeX8tKIT/j1zZOoU7sBeSeyCAs9l7VLys7w66nt+duylyodO/nDYTzzVr9Ah+w3jjGjITqa8qnTcJ0urvSaq7iY8qnTIDoax9jRBkVYPWt2B/4+lo37ofBMYM95qVwu+OI/8OI8+GA1rN/nfvJr436YvxFe+Bd8uBbOWHAmZOWBc6yaB+QcSxczY8aMISsri9GjRzN16lTq1KlT8Vp6ejrJycmUlZXRvHlzoqOtM0tYfExrOiel0a1NP+5OTecPwxfwfdY6/vTxwxX7hIXUIn3wB3y47GUyszcDsHrbPNbuXMC4QW8bFbrP2erVdSeo7Bycb1V+X86334XsHByPP4atbl2DIvReWTl8bcD9m6Xl8I2J7ht1uWDud/Cv7+BUcdX7lDlhbSa89jmcLglsfP6mPHCOFfOAVGbZYmbnzp1kZGQQExPDK6+8UuU+Xbp0ASA5Obli29nip1u3btSqVQubzRaQeP2pffPupHUeysrNGWzft6Zie1JCF+7s9SSTP7yfI8ezmPHRSB4b+DoxdZsYGK3v2Xt0x5bWG+fCT3Bu3ASAc/MWnPMXYku7AXt3c107OXAMCi7wy9nfdmQbc97q+GoXrPLwUtyBY/CX1f6Nx2jKA9bKA1KZZYuZOXPm4HQ6GTJkCFFRVS84ExHhvgvyp8XMnj17+Pjjj4mNjeWqq64KSKyBMCTtOex2B+9/9rv/2f5bHPYQHplxJcmJqaSmDDYoQv9yPPowNGxI+R+n4zp6lPI/zoCGDXGMeviix9Y0B44Zd+6sY/57DNyXyp3w+XbvjtmR7X5/VqY8YJ08IJVZtphZvnw5AKmpqRfcJysrC6hczPTs2ZOcnBzmz59PWlqaf4MMoPiYRFKTB7NxzzK2/rCqYnuII5R2zbtzojCPm7oONzBC/7JFReEY9zgcPkLZw6Pg0CEc4x/HFhlpdGheM7KYKS6Fo14+QWWEbVlw4rT3x63e7ftYahLlAevkAanMssXM/v37AWjWrFmVr5eVlbF6tXtc+afFjN3Ci9Hcc8Oz2G123l9y7lPZ1h9WsWT9e9zWYzRvzB/LmdJq/AYwCXvXzthv7gcnTmLv1xd7l85Gh1QtxwuNPX++hwtYGmlnNS+HVfc4M1EesEYekMos+2h2YaE7458+XXWnzMjIIC8vjzp16tCiRQu/xtK1a1dyc3O9OiYsJILZo737mJjc6nqWTrnwNYBmjdvy2eRzc96fPlPAlIxhjOg3kQHXPsL4N3vxzuJneOTW6V6dF6B1UmtKynyXAF1hYTDL94+Q2tq1gU8Wu7/6WFLrJGwl/r+L9PpH/kVM86ovgZ6dFO9CosPPfX1h4M+f50IT69075H5yv1/uYbTGuHrIn2naaYDXxx3JLyAhwfc/G9VRnRwA1skD/soB4L88EKgcYFWxsbGsX7++WsdatpiJjY0lPz+fDRs2cO21lW/sysnJYcKECQB06tTJ7zf55ubmcvDgQa+OCQ+t7adozpm1YDyxDVpwa/dHsdlsTLjrPR6ekUKPDgPp1LKnV23lZGdTXOrDj+zhtTDb+obZOdlQ7P9nl08XXXj2urOT4l2M3e7ZflU5fCjb65/nQDt1/Gi1jis5farGvLdA5ACowXlAOUC8YNliJi0tjZ07dzJp0iT69OlDUpJ7xst169YxdOhQ8vLygMBMlhcbG+v1MWEhXk7R6qVv/7OYlZszmD1uS0Ux1ySmFSP6TWRqxnBmjd9CRJjn15HjmjTx+ciM2ZbCaxLXJCCfymxlF77OdPIi/wXR4e5CxumEkxd5IupCbdWJsBMfH3+RKI1VfHRXtY47cXBLjXlv/s4BULPzgHJA8KnO78qzbC6XNZeQy8rKIiUlhaNHjxISEkKbNm0oLi5mz5499OvXD6fTyWeffcbs2bN58MEHq2zjhRde4MUXX8SIf6LyElgxM+CnrbbUMeAI8117heVl1F++1HcN/pdzyVLKp07H8eQT2G/s49O283v3IdLh/88Hn2+HhZuqd+wLA90jMseL3BPGeSvEDhPvghBH9c4fKMWl8Pxc71f8figV2taQJ5LNlgPAt3nAXzkA/JcHApUD5HyWvds1ISGBVatWccsttxAeHs6+ffto0KABs2bNYtGiReza5f7k9tObf0XMoKmBawQ2qV/zCxmA8FC4upV3xzSOhivi/BOPiPiXpUvItm3bsnDhwvO2FxQUsG/fPux2Ox06dDAgMpHqaxYDoQ73jLyBltg48OesrgFXwsF8yDx88X0ja8GIXmA3/xyZIkHJ0sXMhWzfvh2Xy0VSUhK1a59/k91HH30EwI4dOyp937x5c7p27Rq4QMXn7Df28fnlpUALD4Uuzd3T8AeSDeieGNhzXopQh/uy0Zy17rWYLiS2LvyqJ1xmnRVN5CKskAeksqAsZrZu3Qpc+BLToEGDqvz+gQce4L333vNrbCKe6JEU+GKmTROIqXPx/WqSsBB44Dro28m9OOeOg5B3Cly47/8ZmQqtG4MFVi0RCWoqZqpg0XuixUKaNoAOCe6ZbgPBBtxo4iuyjaNhYBf3n+fnumcHjqwFSdV/eEJEahAVMxaSmb2Z6R89SNGZUzSu14zf3PMX9h/azjNv9SOh0RVMHLmE+lGX8em37/Dxqun8eHgnD/Wfyu2/eLyijdkLJ7Bycwat4zvz4rB5hr0XubhBV7nvBwnEas+92kCLRv4/j1waT3PA5A+HsWH3UupGuv9TuyT1YWT/KYBygJhTUBYzZ9dtspopGcN48q53SYxP4dNv32H2wie56arhJDS6glnjNlXs1zqhC7+97x98uPz81cRH9p9Cs8btWbN9XuACl2qpWxvu6Ap/XXPxfc86O3fMxeaj+anLouFma9X9luVpDgC46/oJlT7InKUcIGZk2Uezg82egxuJqBVFYnwKAH26PsDXO+ZTWnb+x/ZWTZJp1rgtNpv++82uawu4qaPn+0/71D2/TFXLFFSlboT7JtqwoPzYYy7e5AARq1GKsoicY3vZm7OVh6alVGw7U1JE3smaMTW7+E/fju5Hihdv8W27DSLhkRugYZRv2xX/8DYH/GvVn/j023e4rP7lDLvp/yqKIBEzUjFjIW0uv5qJD35W8f2dL+gmh2Bgs7lHZ+LqwT++hYKLLFPgieTL4c6roE74pbclgeNpDvhVv5doUCcOu93OV1v/xbNv9+O93+wmopYqVzEnXWewiLgGLTl8/MeK7wuLT1JcUkhMdM1YZ0b8r1NTeOoW9xw01X3SODrC/Sjz8F+okDEbb3JATN147HZ3+r+u40Bqh0dz4Mj3AYtVxNdUzFhEYnwKIfZQvtvlXstkwZo36JV8N6EhPlwwSWq8qHAY2gN+exvc0M79+LEnEhvDsOvg+V/Clc38GqL4iTc54Mjxc8/079i/lpOFR4lvaKIZEUX+hy4zWcjT9/6NKf8Yzsy5j9CkYSJP3ftX9uVuO2+/z9a9x3uf/ZaConzWbJ/HP7+Yyh+GLyAx/koDohZ/aBjlns7/lmQ4fAoOHIWsfPdj3OVO9+y4MXXc89UkNPC86JGazdMcMCVjGPkFh7DbHNQKjeC5of8kMqKuARGL+IaKGQtpEdeRN8auv+h+N101jJuuGub/gMRwdrt7uv7YunCV0cGI33maAyY/9HkAohEJHF1msrgQRxinio7y0LQU8gsuvuLe7IUT+HDFK0RF1A9AdCLib8oBEgxsLs3dXyOVl8CKmUZH4bnUMeDw4e05heVl1F++1HcNBkB+7z5EOjTYWdOdXc6gbgS8eLvR0VyY2XIA+DYPKAeINzQyIyIiIqamYkZERERMTcWMiIiImJrumamhXC5wlhodhefsoe6ZaH3F5XJR5Cz3XYMBUNvuwObLfwTxC7PcM2O2HAC+zQPKAeIN3alUQ9lsvr2h1mxsNptupJOgphygHCCe02UmERERMTUVMyIiImJqKmZERETE1FTMiIiIiKmpmBERERFTUzEjIiIipqZiRkRERExNxYyIiIiYmooZERERMTUVMyIiImJqKmZERETE1FTMiIiIiKmpmBERERFTUzEjIiIipqZiRkRERExNxYyIiIiYmooZERERMbUQowOQqrlc4Cw1OgrP2UPBZvNdey6XiyJnue8aDIDadgc2X/4jiAQxs+VAUB4E4/KgipkaylkKK2YaHYXnUseAI8x37RU5y6m/fKnvGgyA/N59iHSoS4n4gtlyICgPgnF5UJeZRERExNRUzIiIiIipqZgRERERU1MxIyIiIqamYkZEgobT6X5KBs59FRHz06MXImJZWcdg+0E4cAwOHIUTp8+9drIYXvscmjaAxMugbROw6+OdiCmpmBERSyl3wsb98NUu2Jf38/vuOeT+s2In1K8N3Vu7/0TWCkysIuIbKmYsZHPmSp58M7XStvCwSBIaJZHWeSi/7PEYDs2DIhaWewL+/jX8eNT7Y/OLYNFm+OI/MKgbJF/u+/jE/5QHg5P+Ry0oNeUeurW5GRcu8k/lsvS7D3hzwTh+PLyTJ+6cbXR4In7xxX9g/kb3yMylKDgD766Czs1g8DUQpixpSsqDwUXd1IJax3cmrct9Fd8P6P4oIya3YfG3bzG870vUi2pkYHQivuVywcJNsGyHb9vdsB+OF8HIVAgP9W3b4n/Kg8FFt7sFgYiwSNo0uwaXy0X20UyjwxHxqc+2+b6QOeuHI/DWF1BqruVxpArKg9amYiZI5Py380bXbmBwJCK+sysXPt3i33PsOeT/c0hgKA9aly4zWVBxaREnCvNwudzXihd8/SZ7Dm6kTdNuJDRKMjo8EZ84UwofrvX+uHF9IToCTp6GaZ96dszyndCxKTSP8f58YgzlweASFCMzeXl5pKenk5iYSHh4OE2bNmXs2LEUFhYyYsQIbDYbr732mtFh+swHS57nzhcaMejFyxg5rRMLvn6D6zrczovD/m10aAFX9vuXKO3bH+e27VW+7ty2ndK+/Sn7/UsBjkwu1adb4Vih98dFR0C92u6vnnK5IOMbc0+053JBdj5sz4JtWe45eMz8fi5GefCcYMiDlh+Z2bRpE/369SM3N5fIyEjatWtHdnY2M2fOJDMzk2PHjgGQkpJibKA+dMvVI+nZaRBlzlL25mwlY+Uk8k5kERYaXrFPSdkZHp3RmdQr72XIDc9WbJ/84TCOFxzi5V8vNiJ0n3OMGU3Ztu2UT52G7c+vY4s492/gKi6mfOo0iI7GMXa0gVGKt86Uwdd7AnvOnOOw+xAkxQb2vJeqrBzW7YXVu90FzE/F1YPrWkO3VhDqMCQ8v1EePCcY8qClR2by8vIYMGAAubm5jB8/npycHDZs2EBubi6TJk1i0aJFrFu3DpvNRqdOnYwO12fiY1rTOSmNbm36cXdqOn8YvoDvs9bxp48frtgnLKQW6YM/4MNlL5OZvRmA1dvmsXbnAsYNetuo0H3OVq+uu4Nm5+B8q/L7cr79LmTn4Hj8MWx16xoUoVTHhn1QXBr4867eFfhzXorTJTBrhXtU6X8LGXAXaP9cB298DoVnAh6eXykPnhMMedDSxcyYMWPIyspi9OjRTJ06lTp16lS8lp6eTnJyMmVlZTRv3pzo6GgDI/Wv9s27k9Z5KCs3Z7B935qK7UkJXbiz15NM/vB+jhzPYsZHI3ls4OvE1G1iYLS+Z+/RHVtab5wLP8G5cRMAzs1bcM5fiC3tBuzdrzU2QPHauh+MOe/WLGOKqOooK4d3vnSPJl3M3jx4a6W1n9pSHrR2HrRsMbNz504yMjKIiYnhlVdeqXKfLl26AJCcnFyx7aOPPuKOO+6gWbNm1K5dmzZt2vDss89SUFAQkLj9ZUjac9jtDt7/7Hf/s/23OOwhPDLjSpITU0lNGWxQhP7lePRhaNiQ8j9Ox3X0KOV/nAENG+IY9fBFj5WaxemsepQhIOd2GXdub23Y71khc9bePPjG4k8sKw9aNw9atpiZM2cOTqeTIUOGEBUVVeU+ERHuOwB/WsxMnToVh8PByy+/zOLFi3nkkUf485//TN++fXE6L3FqUQPFxySSmjyYjXuWsfWHVRXbQxyhtGvenROFedzUdbiBEfqXLSoKx7jH4fARyh4eBYcO4Rj/OLbISKNDEy8dOgklBo4gHDBJMfNVNS6Jrd5l7ZuClQetmwctW8wsX74cgNTU1Avuk5WVBVQuZhYsWMA//vEPhgwZQq9evRg7diyvvfYaq1ev5quvvvJv0H52zw3PYrfZeX/JuU8lW39YxZL173Fbj9G8MX8sZ0pP/0wL5mbv2hn7zf3gxEns/fpi79LZ6JCkGnJPGHv+nOPGnt8Th05Ub32qnBPmGXmqLuVBa+ZByz7NtH//fgCaNWtW5etlZWWsXr0aqFzMNGp0/hTXXbt2BeDgwYPViqVr167k5uZ6dUxYSASzR+/26pjkVtezdMqFP1Y1a9yWzyaf+0h7+kwBUzKGMaLfRAZc+wjj3+zFO4uf4ZFbp3t1XoDWSa0pKfNdAnCFhcGs133W3lm2dm3gk8Xurz6W1DoJW0mJz9uVypp3vZuug/5Y5Wtn55D5OdHh576+MPDC+11oHpqP5y0gfdAjHkZrjMta/4Kev55TrWPvuGcE2Ts+83FE3qtODgTlQU/U1DwYGxvL+vXrq3WsZYuZwkL3BBSnT1f9g5WRkUFeXh516tShRYsWP9vWihUrAGjbtm21YsnNzfW6EAoPrV2tc3lj1oLxxDZowa3dH8VmszHhrvd4eEYKPToMpFPLnl61lZOdTXFpke+CC6+F2ZbDyc7JhmKLPRJSA9VtdeGhg7NzyHjCbvd83586XVRU7Q82gWKvd6Taxx49mlcj3l8gciAoD/qaUXnQssVMbGws+fn5bNiwgWuvrXyXdk5ODhMmTACgU6dO2Gy2C7Zz8OBBnnvuOfr27VvtuWhiY72fmCIsxIsZvarh2/8sZuXmDGaP21Lx/pvEtGJEv4lMzRjOrPFbiAjz/DpqXJMmPv9EUv10bIwmcU00MhMAUbXDLvjaSQ9+BKPD3YWM0wkniy+834XaCnW4iI+Pv/iJDFQ7xP1z6HK5fja//dTZfSMcZ2rE+/N3DgTlQX+4lDxYnd+VZ9lcLmve7jVmzBheffVVmjZtyueff05Sknv66nXr1jF06FB++OEHSktLGTVq1AVn/y0oKOD6668nNzeXdevWERcXF7D4y0tgxcyAne6SpY4Bx4V/x3itsLyM+suX+q7B/3IuWUr51Ok4nnwC+419fNp2fu8+RDos+/mgxsg9ARMXVv/4Fwa6R2SOF8EL//L++Ns6Q2r1BmkDavpnsD/Pu2Oa1IMJN4OH9Y9fmS0HgvIgGJcHLXsDcHp6Og0bNuTAgQO0b9+ejh070rp1a7p160bLli3p3bs3UPl+mZ86ffo0AwYMYO/evSxZsiSghYyIXNhldSDMwJqxqUnWKLyutffH9EiqGYWMiLcsW8wkJCSwatUqbrnlFsLDw9m3bx8NGjRg1qxZLFq0iF273M8tVlXMlJaWcuedd7J+/XoWL15Mu3btAh2+iFyA3W5cQWG3QYJJipnOzeEKLz6DtboMrm7pt3BE/MrSY+Jt27Zl4cLzx6MLCgrYt28fdrudDh06VHrt7Nw0y5Yt45NPPqFbt26BClcCwH5jH58Pq0rgXdUCMg8H/rydmkK4Se7IdNhh+C/g3S/h+4s8TNnqMhjRE0Istj6TVM2KedDSxcyFbN++HZfLRVJSErVrV75jftSoUfzzn//kqaeeonbt2qxdu7bitVatWlX56LaIBFbn5vDvje61hwKpR1Jgz3epwkNhZCp8t889Id7+/5l7JqEBXJcEXZurkBFzC8piZuvWrUDVl5gWL3avkjpx4kQmTpxY6bV3332XYcOG+T0+Efl5YSHQPRGW7QjcOZvUh8TLAnc+X3HYoVtL95/cEzBzCRSVQFQtGN9X98iINaiY+R/79u0LcDQiUh03doRNP8LRACybZrfBPVeb/xd/bF0I/e8IjMNu/vcjcpaKGQvJzN7M9I8epOjMKRrXa8Zv7vkL+w9t55m3+pHQ6AomjlxC/ajLeHvxM6zeOpfQkFo4HKEM7/sSV11xEwAffzmd+WteJzwsilnjNhn7hkR+Rq0QuOcaeO1z7447O3+MJ3PSnHVDO2ja0LvziDE8zYMA89e8wbzVr+Kwh2C32Xn1sW8ICw1n9sIJrNycQev4zrw4bJ6xb0g8EpTFzNl1m6xmSsYwnrzrXRLjU/j023eYvfBJbrpqOAmNrqhUmHRs8QvuS3uOWqERZGZvZtyfe/Lhc9lEhEVyR88nSIy/kjf+/bhh70PEU4mN4ZZkWLTZ82OqWqLg5yTFwk0dvTtGjONpHlyz7d8s2/A3Xh29lsiIuhwvOILD4b67e2T/KTRr3J412+cZ8ybEa5Z9NDvY7Dm4kYhaUSTGpwDQp+sDfL1jPqVl598h2a1NP2qFumfXbBHbEVwuThSYbZ5JEbe09nBjh4vvVx16ysdcvMmD//hiCkP7PE9kRF0A6kU1wmHXf7RZBeXIjBXlHNvL3pytPDQtpWLbmZIi8k7+/Born61/l9gGLWlcv+oFOUVqOpsNbk6GOuEwfyOUll/8GE90bQF3dTN2gj7xjjd58MdDO9iVtZ6/LH2R0vIz9OlyPwOvGxPAaMWX1E0tpM3lVzPxwXOr3d75ws8/Rr5h9zL+svRFJj241OP1W0Rqql9cAUlxMOdr2OflNP4/FR0Od10NHRJ8F5sEjqd5sNxZRu6xvUx79EsKTucz/s+9iGvQkmva9Q9UqOJDusxkEXENWnL4+I8V3xcWn6S4pJCY6KoXjNuc+QVT/zGcPwxfQNPLrghUmCJ+1TgaxvSB+3u4LxF5o2EU3HolPNVfhYxZeZMHL6t3OalX3oPD7qBuZAzd2tzMzh/XnrefmINGZiwiMT6FEHso3+1aSpekPixY8wa9ku8mNOT8Vc+2/PAlkz4cyu+H/ZtWTaz1RJeI3e6eVK9zc8g5DtsPwoFjkHUMjhXC2aV1I2u5J41LqO++kfiKOPcj2GJe3uTB1CvvZf1/PuXKxN6cKT3N5syV3HV9ugFRiy+omLGQp+/9G1P+MZyZcx+hScNEnrr3r+zL3Xbefn/85whKy84wJWN4xban7vkLLeL0yIZYS1w995+zXC5wutxFi66sWpOnefDOnuOY8fFDjJjSDpvNxnUd76BX8iADIhZfUDFjIS3iOvLG2PUX3e/93+wOQDQiNY/NBg4VMZbmaR4MCw0nffD7AYhIAkH3zFhciCOMU0VHeWhaCvkFF1+Z7+MvpzNz7qPUjYwJQHQiIv7nbR6cvXACH654haiI+gGITnxBIzMW1755d/7+2wMe739Hzye4o+cTfoxIRCSwvM2DI/tPYWT/KX6MSHxNIzMiIiJiaipmRERExNRsLtfZBxWlJnG5wFlqdBSes4f69ukQl8tFkdNHU7kGSG27Q5MPSo33/Fw4cRrqRsCLtxsdzYWZLQeC8iAYlwd1z0wNZbOB4/ypEYKGzWYj0qEfT5FgFew5EJQHvaHLTCIiImJqKmZERETE1FTMiIiIiKmpmBERERFTUzEjIiIipqZiRkRERExNxYyIiIiYmooZERERMTUVMyIiImJqKmZERETE1FTMiIiIiKmpmBERERFTUzEjIiIipqZiRkRERExNxYyIiIiYmooZERERMTUVMyIiImJqIUYHIFVzucBZanQUnrOHgs3mu/ZcLhdFznLfNRgAte0ObL78RxAJYmbLgaA8CMblQRUzNZSzFFbMNDoKz6WOAUeY79orcpZTf/lS3zUYAPm9+xDpUJcS8QWz5UBQHgTj8qAuM4mIiIipqZgRERERU1MxIyIiIqamYkZERERMTcWMiIiImJoevRARsbAzpXAwHw4cg9wTUFTi3l5UAit3QtMGEN8AwkONjVPkUqiYERGxGJcL9h6Br3bB5gNQ7jx/n9JymLfB/Xe7DTo2hetaQ2Jj386VIhIIKmYsZHPmSp58M7XStvCwSBIaJZHWeSi/7PEYDs2DImJpWccg4xv3SIynnC7Y/KP7T5N6MPgauLyh30L0K+XB4KT/UQtKTbmHbm1uxoWL/FO5LP3uA95cMI4fD+/kiTtnGx2eiPhBuROWbIOl29zFSXVlH4fpn0HvttCvE4Q4fBZiQCkPBhcVMxbUOr4zaV3uq/h+QPdHGTG5DYu/fYvhfV+iXlQjA6MTEV8rKYN3voT/5PimPZcLlu2A/Ufh173MeT+N8mBw0dNMQSAiLJI2za7B5XKRfTTT6HBExIfKyuGtL3xXyPzUnkMwa4W7WDI75UFrUzETJHL+23mjazcwOBIR8aWP1sGuXP+1v/cIzFnrv/YDSXnQunSZyYKKS4s4UZiHy+W+Vrzg6zfZc3AjbZp2I6FRktHhiYiP7DgIa70cZBjXF6Ij4ORpmPapZ8ds3A8pl0Py5d7HaBTlweASFMVMXl4ekydPZu7cuWRlZdGoUSNuv/12Xn75ZcaMGcM777zDq6++yujRo40O1Sc+WPI8Hyx5vtK26zrczmMDXzcoIhHxteJS91NL3oqOgHq1vT/un9+6H9uOrOX9sUZQHgwulr/MtGnTJjp27MiUKVPIzc2lXbt2lJaWMnPmTO6++2527twJQEpKirGB+tAtV49k0oNLeWnEJ/z65knUqd2AvBNZhIWGV+xTUnaGX09tz9+WvVTp2MkfDuOZt/oFOmS/Kfv9S5T27Y9z2/YqX3du205p3/6U/f6lKl8XqanWZsKJ04E7X8EZWL07cOe7VMqD5wRDHrR0MZOXl8eAAQPIzc1l/Pjx5OTksGHDBnJzc5k0aRKLFi1i3bp12Gw2OnXqZHS4PhMf05rOSWl0a9OPu1PT+cPwBXyftY4/ffxwxT5hIbVIH/wBHy57mczszQCs3jaPtTsXMG7Q20aF7nOOMaMhOpryqdNwnS6u9JqruJjyqdMgOhrHWGuMyklwcLpg9a7An3fN7qon4KuJlAfPCYY8aOliZsyYMWRlZTF69GimTp1KnTp1Kl5LT08nOTmZsrIymjdvTnR0tIGR+lf75t1J6zyUlZsz2L5vTcX2pIQu3NnrSSZ/eD9Hjmcx46ORPDbwdWLqNjEwWt+y1avr7qDZOTjfqpycnG+/C9k5OB5/DFvdugZFKOK9PYfgyKnAn/d4EezMDvx5fUF50Np50LLFzM6dO8nIyCAmJoZXXnmlyn26dOkCQHJycsW2VatWkZaWRlxcHLVq1SIhIaHS5SizGpL2HHa7g/c/+93/bP8tDnsIj8y4kuTEVFJTBhsUof/Ye3THltYb58JPcG7cBIBz8xac8xdiS7sBe/drjQ1QxEt7Dhl37t0GnvtSKQ9aNw9atpiZM2cOTqeTIUOGEBUVVeU+ERERQOViJj8/n44dOzJz5kyWLFnCpEmT2L59O9deey1ZWVkBid0f4mMSSU0ezMY9y9j6w6qK7SGOUNo1786Jwjxu6jrcwAj9y/How9CwIeV/nI7r6FHK/zgDGjbEMerhix4rUtN4s1SBr2UZeO5LpTxo3Txo2WJm+fLlAKSmpl5wn7PFyU+LmVtvvZXp06czaNAgevXqxZAhQ5g7dy4nTpzg448/9m/QfnbPDc9it9l5f8m5TyVbf1jFkvXvcVuP0bwxfyxnSgN4R2EA2aKicIx7HA4foezhUXDoEI7xj2OLjDQ6NBGvGVlQZB1zzxBsVsqDj1syD9pcLjP/WF5Y06ZNycrKYuPGjVU+qVRWVkZcXBx5eXlkZmbSsmXLC7Z19OhRYmJieO211xg1apTXsXTt2pXcXO9mtQoLiWD2aP8+OnD6TAEPTUvmjp7jGHDtI4x/sxdJCV155NbpXrc18rXWlJT5LgG4wsI4Msv3j1CWz3gV5yeLsd/cD8fjj/m07UYPjcJWUuLTNkWqcscr+7HZq1406ew8MhcSHQ52OzidcLL4wvv93Dw0c59pibPcvz/rgciBoDxYk/JgbGws69evr9axlp1nprCwEIDTp6v+wcrIyCAvL486derQokWL814vLy/H6XSyf/9+nn76aWJjY7nrrruqFUtubi4HDx706pjw0GpMBOGlWQvGE9ugBbd2fxSbzcaEu97j4Rkp9OgwkE4te3rVVk52NsWlRb4LLrwW/lgOxtauDXyy2P3Vx7JzsqH4jM/bFanEZrtgIQOezyNjt1dvvhmA3MNHKC0uqN7BHgpEDgTlQV8zKg9atpiJjY0lPz+fDRs2cO21lW9sysnJYcKECQB06tQJm8123vG9evVi9erVACQmJrJ8+XIaNarewmSxsbFeHxMW8jMfrXzg2/8sZuXmDGaP21Lx/pvEtGJEv4lMzRjOrPFbiAjzfOgxrkkT338i8VlrgdEkrolGZiQgnGUl2EPCqnzt5EW6oTcjMxfSuFFDXE7/Pvni7xwIyoP+cCl5sDq/K8+ybDGTlpbGzp07mTRpEn369CEpyT199bp16xg6dCh5eXnAhSfLe/vttzl+/Dh79+5lypQp3HjjjaxevZrLL/d+Pu/qDJuVl8CKmV4f5rFubfox7w/Hz9t+W49R3NbD+0tpu3ftxlF1bq2WwvIy6i9f6rsGA2DX7l1EOizbpaQG+b9/Q94FBkYutkTBCwPdIzIni+GFf3l/7uhwOPDjPu8P9JK/cyAoD/qDUXnQsjcAp6en07BhQw4cOED79u3p2LEjrVu3plu3brRs2ZLevXsDlW/+/akrrriCq6++msGDB7Ns2TJOnTrF5MmTA/kWRESqlGDgOolGnlvkQixbzCQkJLBq1SpuueUWwsPD2bdvHw0aNGDWrFksWrSIXbvc02deqJj5qXr16pGYmMiePXv8HbaIyEU1NbCgaNrQuHOLXIilx8Tbtm3LwoULz9teUFDAvn37sNvtdOjQ4aLtHD58mO+//56rr77aH2GKiHilfQIs2GTQueONOa/Iz7F0MXMh27dvx+VykZSURO3ale+Yv++++0hMTCQlJYV69eqxe/dupk+fTkhICE888YRBEYuv2G/sg/3GPkaHIXJJYuu6V7AO9EzAlzd0/xFzs2IeDMpiZuvWrUDVl5iuueYaPvjgA/70pz9RXFxM06ZNSU1N5ZlnnqFZs2aBDlVEpErXtQ58MdOjdWDPJ+IpFTP/Y/To0Ywebd6VQ0UkOHRqCi0awd4APbub0AC6nj8ll0iNoGLGQjKzNzP9owcpOnOKxvWa8Zt7/sL+Q9t55q1+JDS6gokjl1A/6jLeWfwsX++Yj93mnnhrcO+nKhZWm71wAis3Z9A6vjMvDptn4LsRkZ9jt8M918CUT6C03L/nctjh3mvcX2s6T/PgC+8NJOfY3orj9uZu4YUH5tG9/a18/OV05q95nfCwKGaN22TcmxGPBWUxc3bdJquZkjGMJ+96l8T4FD799h1mL3ySm64aTkKjKyp1yLuun8Cv+r0EQN6Jg4yY0pbOrdOoGxnDyP5TaNa4PWu2zzPmTYiIxy6Lhts6w0frPD/m7GR4F5tc76du7gRN6nsXm1E8zYMvDDs3yc73B9bzzFt9ueqKvgDc0fMJEuOv5I1/Px7g6KW6TFBniyf2HNxIRK0oEuNTAOjT9QG+3jGf0rLzZ2KMiqhX8ffTZwpw4cLpcgYoUhHxpeuS4MaLP5RZYdqn7snyLja53lm92kDvdtWLLdC8yYM/9em3b3ND5/sIvcCsylLzBeXIjBXlHNvL3pytPDQtpWLbmZIi8k5WvSbUv76ayfw1r5N3PIsnBr1F/ajLAhSpiPhav04Q6oBFm33b7o0d3G1XseJLjeRtHgQ4U3qaFZvmMP3RVQGIUPxFxYyFtLn8aiY++FnF93e+cOG1pAZeN4aB140hM3szE+fcR9ekG4mO1DOXImZks0GfDu4bgueshaOXuAZkvdru+3GuiPNNfIHkTR4E+HLLRyQ0SqJFXEd/hyZ+pMtMFhHXoCWHj/9Y8X1h8UmKSwqJif75Ga5aNUkmJjqezZkr/RyhiPhbYmNIv8V9WSi8Gsst1wpxX1Z6qr85C5nq5MFPv32bvleNCER44kcqZiwiMT6FEHso3+1yL0q2YM0b9Eq+u8prwPsP7aj4e3ZeJnuyN3J5Y5NcFBeRn1UrBG69El68HQZfDS0buS9BXUioA5rHwKCr3McM7FK9Qqgm8CYPAhzM28OurPWkXnlPIMMUP9BlJgt5+t6/MeUfw5k59xGaNEzkqXv/yr7cbeft9/8WpZN7bC8OeygORwijf/kazRq3NSBiEfGXWiFwTaL7T7kTDp+EnONQUgYuIMwBcfWgcV1zPHLtKU/zIMCn697hFx3vIDI8OsBRiq+pmLGQFnEdeWPs+ovu93+/On+9KhGxLofdXbjE1TM6Ev/zNA8CjOj3sp+jkUCxUD0uVQlxhHGq6CgPTUshv+DwRfefvXACH654hagIk0wqISJyEd7mwY+/nM7MuY9SNzImANGJL9hcLpfL6CDkfOUlsGKm0VF4LnUMOHw4RUNheRn1ly/1XYMBkN+7D5EODXaK+ILZciAoD4JxeVAjMyIiImJqKmZERETE1HSZqYZyucBZanQUnrOH+naWUJfLRZHTz6vn+VhtuwObWaZKFanhzJYDQXkQjMuDKmZERETE1HSZSURERExNxYyIiIiYmooZERERMTUVMyIiImJqKmZERETE1FTMiIiIiKmpmBERERFTUzEjIiIipqZiRkRERExNxYyIiIiYmooZERERMTUVMyIiImJqKmZERETE1FTMiIiIiKmpmBERERFTUzEjIiIipqZiRkRERExNxYyIiIiYmooZERERMTUVMyIiImJqKmZERETE1FTMiIiIiKmpmBERERFTUzEjIiIipqZiRkREREzt/wMcCo06lUMDxgAAAABJRU5ErkJggg==\n"
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Ansatz per idruro di litio\n",
        "from qiskit_nature.second_q.drivers import PySCFDriver\n",
        "from qiskit_nature.second_q.circuit.library import HartreeFock, UCC\n",
        "from qiskit_nature.second_q.mappers import JordanWignerMapper\n",
        "from qiskit_nature.second_q.formats.molecule_info import MoleculeInfo\n",
        "\n",
        "molecule = MoleculeInfo(\n",
        "    # Coordinates in Angstrom\n",
        "    symbols=[\"Li\", \"H\"],\n",
        "    coords=([0.0, 0.0, 0.0], [0.2, 0.0, 0.0]),\n",
        "    multiplicity=1,  # = 2*spin + 1\n",
        "    charge=0,\n",
        ")\n",
        "\n",
        "driver = PySCFDriver.from_molecule(molecule)\n",
        "LiH_problem = driver.run()\n",
        "\n",
        "mapper = JordanWignerMapper()\n",
        "LiH_reference_state = HartreeFock(\n",
        "    num_spatial_orbitals=LiH_problem.num_spatial_orbitals,\n",
        "    num_particles=LiH_problem.num_particles,\n",
        "    qubit_mapper=mapper,\n",
        ")\n",
        "\n",
        "ansatz = UCC(\n",
        "    num_spatial_orbitals=LiH_problem.num_spatial_orbitals,\n",
        "    num_particles=LiH_problem.num_particles,\n",
        "    qubit_mapper=mapper,\n",
        "    initial_state=LiH_reference_state,\n",
        "    excitations=2,\n",
        ")\n",
        "\n",
        "ansatz.decompose().draw()"
      ],
      "metadata": {
        "id": "NxVCY1_UYIW9"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from qiskit.circuit.library import ZZFeatureMap, TwoLocal\n",
        "\n",
        "data = [0.1, 0.2]\n",
        "\n",
        "zz_feature_map_reference = ZZFeatureMap(feature_dimension=2, reps=2)\n",
        "zz_feature_map_reference = zz_feature_map_reference.bind_parameters(data)\n",
        "\n",
        "variation_form = TwoLocal(2, [\"ry\", \"rz\"], \"cz\", reps=2)\n",
        "vqc_ansatz = zz_feature_map_reference.compose(variation_form)\n",
        "vqc_ansatz.decompose().draw(\"mpl\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 191
        },
        "id": "nb4rgFzTY_KR",
        "outputId": "5d091134-f86e-4385-bb83-bf51e01764c0"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Figure size 1625.07x200.667 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Funzione di costo"
      ],
      "metadata": {
        "id": "cOLMcRw9fOcx"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from qiskit import QuantumCircuit\n",
        "from qiskit.quantum_info import SparsePauliOp\n",
        "\n",
        "# Creo lo stato +\n",
        "original_circuit = QuantumCircuit(1)\n",
        "original_circuit.h(0)\n",
        "\n",
        "# Creo l'osservabile\n",
        "H = SparsePauliOp([\"X\", \"Z\"], [2, -1])\n",
        "\n",
        "# Creo circuiti aggiuntivi per ogni porta della scomposizione dell'osservabile\n",
        "aux_circuits = []\n",
        "for pauli in H.paulis:\n",
        "    aux_circ = original_circuit.copy()\n",
        "    aux_circ.barrier()\n",
        "    if str(pauli) == \"X\":\n",
        "        aux_circ.h(0)\n",
        "    else:\n",
        "        aux_circ.i(0)\n",
        "    aux_circ.measure_all()\n",
        "    aux_circuits.append(aux_circ)\n",
        "\n",
        "print(\"Circuito base\")\n",
        "display(original_circuit.draw(\"mpl\"))\n",
        "for (circuit, pauli) in zip(aux_circuits, H.paulis):\n",
        "    print(f\"Circuito per misurazione in {str(pauli)}\")\n",
        "    display(circuit.draw(\"mpl\"))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 529
        },
        "id": "714eEC0VfPoI",
        "outputId": "97711251-ddd6-491f-dcfa-38ad3d36978c"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Circuito base\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 185.453x117.056 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Circuito per misurazione in X\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 580.387x200.667 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Circuito per misurazione in Z\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 580.387x200.667 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from qiskit.primitives import Sampler, Estimator\n",
        "from qiskit.circuit.library import IGate, ZGate\n",
        "import numpy as np\n",
        "\n",
        "# Sampler\n",
        "sampler = Sampler()\n",
        "job = sampler.run(aux_circuits)\n",
        "probability_dists = job.result().quasi_dists\n",
        "\n",
        "expvals = []\n",
        "for dist, pauli in zip(probability_dists, H.paulis):\n",
        "    val = 0\n",
        "    if str(pauli) == \"I\":\n",
        "        Lambda = IGate().to_matrix().real\n",
        "    else:\n",
        "        Lambda = ZGate().to_matrix().real\n",
        "    val += Lambda[0][0] * dist.get(0, 0)\n",
        "    val += Lambda[1][1] * dist.get(1, 0)\n",
        "    expvals.append(val)\n",
        "\n",
        "\n",
        "print(\"Risultati:\")\n",
        "for (pauli, expval) in zip(H.paulis, expvals):\n",
        "    print(f\"  >> Valore atteso di {str(pauli)}: {expval:.5f}\")\n",
        "\n",
        "total_expval = np.sum(H.coeffs * expvals).real\n",
        "print(f\"  >> Valore totale: {total_expval:.5f}\")\n",
        "\n",
        "\n",
        "# Estimator\n",
        "observables = [\n",
        "    *H.paulis,\n",
        "    H,\n",
        "]\n",
        "\n",
        "estimator = Estimator()\n",
        "job = estimator.run([original_circuit] * len(observables), observables)\n",
        "estimator_expvals = job.result().values\n",
        "\n",
        "print(\"Risultati\")\n",
        "for (obs, expval) in zip(observables, estimator_expvals):\n",
        "    if obs is not H:\n",
        "        print(f\"  >> Valore atteso di {str(obs)}: {expval:.5f}\")\n",
        "    else:\n",
        "        print(f\"  >> Valore totale: {expval:.5f}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "J56H8d4lgUZU",
        "outputId": "f445a6c6-8c0c-47ed-f6b0-2a2732a54ac2"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Risultati:\n",
            "  >> Valore atteso di X: 1.00000\n",
            "  >> Valore atteso di Z: 0.00000\n",
            "  >> Valore totale: 2.00000\n",
            "Risultati\n",
            "  >> Valore atteso di X: 1.00000\n",
            "  >> Valore atteso di Z: 0.00000\n",
            "  >> Valore totale: 2.00000\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "NjQw5CvshKbK"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}