TETRIS_Adaptive_VQE_for_BeH2.ipynb

{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lIoCMJG9S9EX",
        "outputId": "3f367002-0341-4a0e-c1ce-c47cd473524f"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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            "Requirement already satisfied: pbr!=2.1.0,>=2.0.0 in /usr/local/lib/python3.8/dist-packages (from stevedore>=3.0.0->qiskit-terra>=0.22.*->qiskit_nature) (5.11.1)\n",
            "Requirement already satisfied: mpmath>=0.19 in /usr/local/lib/python3.8/dist-packages (from sympy>=1.3->qiskit-terra>=0.22.*->qiskit_nature) (1.2.1)\n",
            "Installing collected packages: qiskit_nature\n",
            "Successfully installed qiskit_nature-0.5.2\n"
          ]
        }
      ],
      "source": [
        "!pip install pennylane\n",
        "!pip install qiskit\n",
        "!pip install qiskit_nature"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install pyscf"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-9bGLbmgV8xG",
        "outputId": "e1df19f5-93b2-4ed6-8df6-9da91b95e56d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting pyscf\n",
            "  Downloading pyscf-2.1.1-cp38-cp38-manylinux_2_12_x86_64.manylinux2010_x86_64.whl (38.2 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m38.2/38.2 MB\u001b[0m \u001b[31m25.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: h5py>=2.7 in /usr/local/lib/python3.8/dist-packages (from pyscf) (3.1.0)\n",
            "Requirement already satisfied: numpy!=1.16,!=1.17,>=1.13 in /usr/local/lib/python3.8/dist-packages (from pyscf) (1.22.4)\n",
            "Requirement already satisfied: scipy!=1.5.0,!=1.5.1 in /usr/local/lib/python3.8/dist-packages (from pyscf) (1.7.3)\n",
            "Installing collected packages: pyscf\n",
            "Successfully installed pyscf-2.1.1\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "e54X21XvLWOw",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "91d3359c-6a55-49e5-9c6e-85f4d895b886"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-3-345fc2c37443>:12: NatureDeprecationWarning: The qiskit_nature.algorithms.excited_state_solvers package is deprecated as of version 0.5.0 and will be removed no sooner than 3 months after the release. Instead use the qiskit_nature.second_q.algorithms.excited_state_solvers package.\n",
            "  from qiskit_nature.algorithms import GroundStateEigensolver, VQEUCCFactory\n",
            "<ipython-input-3-345fc2c37443>:12: NatureDeprecationWarning: The qiskit_nature.algorithms.pes_sampler package is deprecated as of version 0.5.0 and will be removed no sooner than 3 months after the release . Please refer to 'https://github.com/Qiskit/qiskit-nature/issues/750' for additional information regarding 'extrapolators'.\n",
            "  from qiskit_nature.algorithms import GroundStateEigensolver, VQEUCCFactory\n"
          ]
        }
      ],
      "source": [
        "import json\n",
        "import pennylane as qml\n",
        "from pennylane import qchem \n",
        "import pennylane.numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "from qiskit_nature.drivers.second_quantization import ElectronicStructureDriverType, ElectronicStructureMoleculeDriver\n",
        "from qiskit_nature.mappers.second_quantization import BravyiKitaevMapper, JordanWignerMapper, ParityMapper\n",
        "from qiskit_nature.drivers import Molecule\n",
        "from qiskit_nature.transformers.second_quantization.electronic import ActiveSpaceTransformer\n",
        "from qiskit_nature.problems.second_quantization.electronic import ElectronicStructureProblem\n",
        "from qiskit_nature.converters.second_quantization import QubitConverter\n",
        "from qiskit_nature.algorithms import GroundStateEigensolver, VQEUCCFactory\n",
        "from qiskit import Aer\n",
        "from qiskit.algorithms.optimizers import SLSQP\n",
        "from qiskit_nature.circuit.library import UCC\n",
        "\n",
        "import warnings\n",
        "warnings.filterwarnings('ignore')"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "r=1.7\n",
        "hydrogen_t = [[\"H\", [-r, 0.0, 0.0]], \n",
        "              [\"Be\", [0.0, 0.0, 0.0]], \n",
        "              [\"H\", [r, 0.0, 0.0]]]\n",
        "                  \n",
        "h3p = Molecule(\n",
        "    geometry=  hydrogen_t,\n",
        "    multiplicity=1, \n",
        "    charge=0, \n",
        ")\n",
        "\n",
        "driver = ElectronicStructureMoleculeDriver(h3p, basis=\"sto-3g\", driver_type=ElectronicStructureDriverType.PYSCF) \n",
        "\n",
        "properties = driver.run()"
      ],
      "metadata": {
        "id": "OMsnF8-RWH0i"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "num_alpha_electrons = properties.get_property('ParticleNumber').num_alpha\n",
        "num_beta_electrons = properties.get_property('ParticleNumber').num_beta\n",
        "num_spin_orbitals = int(properties.get_property('ParticleNumber').num_spin_orbitals)\n",
        "\n",
        "nuclear_rep_energy = properties.get_property('ElectronicEnergy').nuclear_repulsion_energy\n",
        "print(\"number of alpha electrons: \" , num_alpha_electrons)\n",
        "print(\"number of beta electrons: \" , num_beta_electrons)\n",
        "print(\"number of spin orbitals: \" , num_spin_orbitals)\n",
        "print(\"nuclear repulsion energy: \" , nuclear_rep_energy)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vVVD4Vu8WbcA",
        "outputId": "2dfedd16-fc1e-48a3-b2a7-19e41abd22e6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "number of alpha electrons:  3\n",
            "number of beta electrons:  3\n",
            "number of spin orbitals:  14\n",
            "nuclear repulsion energy:  2.6458860546\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "We set the number of active orbitals to 6 and the number of active electron to 4, because it took too long to get the exact result when we use  7 orbitals and 6 electrons. "
      ],
      "metadata": {
        "id": "N4TyMpn5sXfn"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "PN_property = properties.get_property(\"ParticleNumber\")\n",
        "\n",
        "transformer = ActiveSpaceTransformer(\n",
        "    num_electrons=4, \n",
        "    num_molecular_orbitals=6, #We set 6 active orbitals since 7 takes too long to run\n",
        ")\n",
        "\n",
        "problem_reduced = ElectronicStructureProblem(driver, transformers=[transformer]) \n",
        "\n",
        "second_q_ops_reduced = problem_reduced.second_q_ops()"
      ],
      "metadata": {
        "id": "2r0ptcUAWiVm"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "mapper = JordanWignerMapper()\n",
        "\n",
        "converter = QubitConverter(mapper)\n",
        "\n",
        "qubit_op = converter.convert(second_q_ops_reduced[\"ElectronicEnergy\"])"
      ],
      "metadata": {
        "id": "9EpBU8djWrFf"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Get the exact solution from qiskit."
      ],
      "metadata": {
        "id": "s-446Xt0WooF"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "vqe_factory = VQEUCCFactory( \n",
        "    quantum_instance=Aer.get_backend(\"aer_simulator_statevector\"),\n",
        "    optimizer=SLSQP(),\n",
        "    ansatz=UCC(excitations='sd')\n",
        ") \n",
        "\n",
        "from qiskit.algorithms import NumPyMinimumEigensolver\n",
        "\n",
        "numpy_solver = NumPyMinimumEigensolver()\n",
        "\n",
        "solver = GroundStateEigensolver(converter, vqe_factory)  # Define Numpy\n",
        "real_solution_t = solver.solve(problem_reduced).total_energies[0]    \n",
        "print('Reference energy : ', real_solution_t)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JPtB1F5qXJ4O",
        "outputId": "d3ed1ad9-0a80-4fc4-b56d-b6446aabce1a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Reference energy :  (-15.527759639497551+0j)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "4OgIgFqTS93o"
      },
      "outputs": [],
      "source": [
        "def hydrogen_hamiltonian(coordinates, charge, active_orb=6, num_e=4):\n",
        "    \"\"\"Calculates the qubit Hamiltonian of the hydrogen molecule.\n",
        "    \n",
        "    Args:\n",
        "        coordinates (list(float)): Cartesian coordinates of each hydrogen molecule.\n",
        "        charge (int): The electric charge given to the hydrogen molecule.\n",
        "\n",
        "    Returns:\n",
        "        (qml.Hamiltonian): A PennyLane Hamiltonian.\n",
        "    \"\"\"\n",
        "    return qml.qchem.molecular_hamiltonian(\n",
        "        [ \"H\",\"Be\", \"H\"], coordinates, charge, basis=\"sto-3g\",active_electrons=num_e, active_orbitals=active_orb\n",
        "    )[0]\n",
        "\n",
        "def num_electrons(charge):\n",
        "    \"\"\"The total number of electrons in the hydrogen molecule.\n",
        "    \n",
        "    Args:\n",
        "        charge (int): The electric charge given to the hydrogen molecule.\n",
        "\n",
        "    Returns: \n",
        "        (int): The number of electrons.\n",
        "    \"\"\"\n",
        "    return 4-charge\n",
        "\n",
        "def hf(electrons, num_qubits):\n",
        "    \"\"\"Calculates the Hartree-Fock state of the hydrogen molecule.\n",
        "    \n",
        "    Args:\n",
        "        electrons (int): The number of electrons in the hydrogen molecule.\n",
        "        num_qubits (int): The number of qubits needed to represent the hydrogen molecule Hamiltonian.\n",
        "\n",
        "    Returns:\n",
        "        (numpy.tensor): The HF state.\n",
        "    \"\"\"\n",
        "    # Put your solution here #\n",
        "    return qml.qchem.hf_state(electrons=electrons, orbitals=num_qubits)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "VLtw2muPkqxw"
      },
      "outputs": [],
      "source": [
        "def BeH2_data(r,threshold, selection=True):\n",
        "    a0=0.529177210903\n",
        "    coordinates=[ 0.0, 0.0, -r/a0, 0.0, 0.0, 0.0, 0.0, 0.0, r/a0] # in atomic unit\n",
        "    charge=0\n",
        "    hamiltonian = hydrogen_hamiltonian(np.array(coordinates), charge)\n",
        "\n",
        "    electrons = num_electrons(charge)\n",
        "    num_qubits = len(hamiltonian.wires)\n",
        "\n",
        "    hf_state = hf(electrons, num_qubits)\n",
        "    # singles and doubles are used to make the AllSinglesDoubles template\n",
        "    singles, doubles = qml.qchem.excitations(electrons, num_qubits)\n",
        "\n",
        "    dev = qml.device(\"default.qubit\", wires=num_qubits)\n",
        "    @qml.qnode(dev)\n",
        "    def circuit_1(params, excitations):\n",
        "        qml.BasisState(hf_state, wires=range(num_qubits))\n",
        "\n",
        "        for i, excitation in enumerate(excitations):\n",
        "            if len(excitation) == 4:\n",
        "                qml.DoubleExcitation(params[i], wires=excitation)\n",
        "            else:\n",
        "                qml.SingleExcitation(params[i], wires=excitation)\n",
        "        return qml.expval(hamiltonian )\n",
        "\n",
        "    params = [0.0] * len(doubles)\n",
        "    params=np.array(params )\n",
        "\n",
        "    dev = qml.device(\"default.qubit\", wires=num_qubits)\n",
        "    cost_fn = qml.QNode(circuit_1, dev)\n",
        "    circuit_gradient = qml.grad(cost_fn, argnum=0)\n",
        "    grads_d = circuit_gradient(params, excitations=doubles)\n",
        "\n",
        "    #for i in range(len(doubles)):\n",
        "    #    print(f\"Excitation : {doubles[i%len(doubles)]}, Gradient: {grads_d[i]}\")\n",
        "\n",
        "    params = [0.0] * len(singles)\n",
        "    params=np.array(params )\n",
        "\n",
        "    dev = qml.device(\"default.qubit\", wires=num_qubits)\n",
        "    cost_fn = qml.QNode(circuit_1, dev)\n",
        "    circuit_gradient = qml.grad(cost_fn, argnum=0)\n",
        "    grads_s = circuit_gradient(params, excitations=singles)\n",
        "\n",
        "    #for i in range(len(singles)):\n",
        "    #    print(f\"Excitation : {singles[i%len(singles)]}, Gradient: {grads_s[i]}\")\n",
        "\n",
        "    doubles_select = [(doubles*2)[i] for i in range(len(doubles)) if abs(grads_d[i]) > threshold]\n",
        "    singles_select = [(singles*2)[i] for i in range(len(singles)) if abs(grads_s[i]) > threshold]\n",
        "\n",
        "    if selection==True:\n",
        "        return doubles_select, singles_select, num_qubits, hf_state, hamiltonian\n",
        "    elif selection==False:\n",
        "        return doubles, singles, num_qubits, hf_state, hamiltonian"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Basic fixed layer VQE\n",
        "\n",
        "The basic fixed layer VQE is composed of all single qubit excitation gates[1] and double qubit excitation gates[2].\n",
        "\n",
        "[1] https://docs.pennylane.ai/en/stable/code/api/pennylane.SingleExcitation.html\n",
        "\n",
        "[2] https://docs.pennylane.ai/en/stable/code/api/pennylane.DoubleExcitation.html\n",
        "\n",
        "(Note: the circuit depth here only counts single qubit excitations and double qubit excitations without converting them into native gates in quantum computing.)"
      ],
      "metadata": {
        "id": "bN5g92ATKxLg"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "yfg8EbPzTo-9"
      },
      "outputs": [],
      "source": [
        "def run_VQE(r):\n",
        "    \"\"\"Performs a VQE routine for the given hydrogen molecule.\n",
        "\n",
        "    Args:\n",
        "        coordinates (list(float)): Cartesian coordinates of each hydrogen molecule.\n",
        "        charge (int): The electric charge given to the hydrogen molecule.:\n",
        "\n",
        "    Returns:\n",
        "        (float): The expectation value of the hydrogen Hamiltonian.\n",
        "    \"\"\"\n",
        "    doubles, singles, num_qubits, hf_state, hamiltonian=BeH2_data(r,1e-5, selection=False)\n",
        "    print(\"The basic vqe uses \",len(doubles)+ len(singles),\" parameters\")\n",
        "    print(\"The basic vqe uses \",len(doubles)*13 + len(singles)*2,\" cnot gates\")\n",
        "    print(\"The basic vqe uses \",len(doubles)*34 + len(singles)*10,\" gates\")\n",
        "    #print(\"The original vqe use \",len(doubles + singles),\" parameters\")\n",
        "    dev = qml.device(\"default.qubit\", wires=num_qubits)\n",
        "    @qml.qnode(dev)\n",
        "    def cost(weights):\n",
        "        \"\"\"A circuit with tunable parameters/weights that measures the expectation value of the hydrogen Hamiltonian.\n",
        "        \n",
        "        Args:\n",
        "            weights (numpy.array): An array of tunable parameters.\n",
        "\n",
        "        Returns:\n",
        "            (float): The expectation value of the hydrogen Hamiltonian.\n",
        "            \n",
        "        \"\"\"\n",
        "        #state=np.array([0]*(num_qubits//2)+[1]*(num_qubits//2))\n",
        "        qml.BasisState(hf_state, wires=list(range(num_qubits)))\n",
        "\n",
        "        for i in range(len(singles)):\n",
        "            #for j in range(E_len):\n",
        "            qml.SingleExcitation(weights[i+len(doubles)], wires=singles[i])\n",
        "\n",
        "        for i in range(len(doubles)):\n",
        "            qml.DoubleExcitation(weights[i], wires=doubles[i])\n",
        "\n",
        "        \n",
        "        return qml.expval(hamiltonian)\n",
        "    \n",
        "    #np.random.seed = 1234\n",
        "    weights =np.zeros(len(doubles + singles), requires_grad=True)\n",
        "    #weights = np.random.normal(0, 2*np.pi, len(doubles)+len(singles), requires_grad=True)\n",
        "    #weights =np.concatenate((params, params_d))\n",
        "    #opt = qml.GradientDescentOptimizer(stepsize=0.5)\n",
        "    opt = qml.AdamOptimizer(stepsize=0.05)\n",
        "    #eta = 0.05\n",
        "    #opt = qml.QNGOptimizer(0.05)\n",
        "\n",
        "    i=0\n",
        "    iter=[]\n",
        "    cost_val=[]\n",
        "    Lowest_E=0\n",
        "    best_weights=0\n",
        "    for _ in range(100):\n",
        "        weights = opt.step(cost, weights)\n",
        "        iter.append(i)\n",
        "        cost_fn=cost(weights)\n",
        "\n",
        "        if Lowest_E>cost_fn:\n",
        "            Lowest_E=cost_fn\n",
        "            best_weights=weights\n",
        "        cost_val.append(cost_fn)\n",
        "        print('iter:',i,' cost_fn:',cost_fn)\n",
        "        i=i+1\n",
        "\n",
        "    return Lowest_E, iter, cost_val, best_weights"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "76Bdr-AFwY5Y",
        "outputId": "c1975e39-129f-4329-813d-803deca1c68f"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The basic vqe uses  92  parameters\n",
            "The basic vqe uses  1020  cnot gates\n",
            "The basic vqe uses  2744  gates\n",
            "iter: 0  cost_fn: -15.500365087575855\n",
            "iter: 1  cost_fn: -15.516041543452497\n",
            "iter: 2  cost_fn: -15.523293972616555\n",
            "iter: 3  cost_fn: -15.523024937644562\n",
            "iter: 4  cost_fn: -15.520198816357777\n",
            "iter: 5  cost_fn: -15.517930739638686\n",
            "iter: 6  cost_fn: -15.517304199780671\n",
            "iter: 7  cost_fn: -15.51826416371182\n",
            "iter: 8  cost_fn: -15.520038629819455\n",
            "iter: 9  cost_fn: -15.521682087098602\n",
            "iter: 10  cost_fn: -15.522774281856913\n",
            "iter: 11  cost_fn: -15.523416095050774\n",
            "iter: 12  cost_fn: -15.523905037980633\n",
            "iter: 13  cost_fn: -15.52445386723022\n",
            "iter: 14  cost_fn: -15.52505567038314\n",
            "iter: 15  cost_fn: -15.525523950960503\n",
            "iter: 16  cost_fn: -15.525722988725972\n",
            "iter: 17  cost_fn: -15.525701990847992\n",
            "iter: 18  cost_fn: -15.525587483883314\n",
            "iter: 19  cost_fn: -15.525462785642718\n",
            "iter: 20  cost_fn: -15.525403802713416\n",
            "iter: 21  cost_fn: -15.525518150360762\n",
            "iter: 22  cost_fn: -15.525873505933726\n",
            "iter: 23  cost_fn: -15.526397675682652\n",
            "iter: 24  cost_fn: -15.526900409416466\n",
            "iter: 25  cost_fn: -15.527210934109657\n",
            "iter: 26  cost_fn: -15.527288027415198\n",
            "iter: 27  cost_fn: -15.527200915223704\n",
            "iter: 28  cost_fn: -15.527048373946384\n",
            "iter: 29  cost_fn: -15.526920099177675\n",
            "iter: 30  cost_fn: -15.526880084368567\n",
            "iter: 31  cost_fn: -15.526939856539105\n",
            "iter: 32  cost_fn: -15.527059318842277\n",
            "iter: 33  cost_fn: -15.527185855018233\n",
            "iter: 34  cost_fn: -15.527289049001412\n",
            "iter: 35  cost_fn: -15.527372230333052\n",
            "iter: 36  cost_fn: -15.527459264548241\n",
            "iter: 37  cost_fn: -15.52755534827534\n",
            "iter: 38  cost_fn: -15.52762581386273\n",
            "iter: 39  cost_fn: -15.52763198260934\n",
            "iter: 40  cost_fn: -15.527578200077942\n",
            "iter: 41  cost_fn: -15.527505215688333\n",
            "iter: 42  cost_fn: -15.527452364833746\n",
            "iter: 43  cost_fn: -15.527444215835084\n",
            "iter: 44  cost_fn: -15.52749219348059\n",
            "iter: 45  cost_fn: -15.527583629588307\n",
            "iter: 46  cost_fn: -15.527678643760503\n",
            "iter: 47  cost_fn: -15.52773459142982\n",
            "iter: 48  cost_fn: -15.527734709436675\n",
            "iter: 49  cost_fn: -15.527695546139181\n",
            "iter: 50  cost_fn: -15.52765286269408\n",
            "iter: 51  cost_fn: -15.527635492290038\n",
            "iter: 52  cost_fn: -15.52764731388145\n",
            "iter: 53  cost_fn: -15.52767478767302\n",
            "iter: 54  cost_fn: -15.527703191519068\n",
            "iter: 55  cost_fn: -15.527721523063715\n",
            "iter: 56  cost_fn: -15.527725703812298\n",
            "iter: 57  cost_fn: -15.527722051491212\n",
            "iter: 58  cost_fn: -15.527719855204024\n",
            "iter: 59  cost_fn: -15.527722111086195\n",
            "iter: 60  cost_fn: -15.5277260923027\n",
            "iter: 61  cost_fn: -15.527728308532335\n",
            "iter: 62  cost_fn: -15.527728466179449\n",
            "iter: 63  cost_fn: -15.527729952819698\n",
            "iter: 64  cost_fn: -15.527735087804057\n",
            "iter: 65  cost_fn: -15.527742210597086\n",
            "iter: 66  cost_fn: -15.527748214427836\n",
            "iter: 67  cost_fn: -15.527750345932736\n",
            "iter: 68  cost_fn: -15.527747787070432\n",
            "iter: 69  cost_fn: -15.5277439179157\n",
            "iter: 70  cost_fn: -15.52774290327514\n",
            "iter: 71  cost_fn: -15.527744857557463\n",
            "iter: 72  cost_fn: -15.52774777041131\n",
            "iter: 73  cost_fn: -15.5277506899315\n",
            "iter: 74  cost_fn: -15.527753279942036\n",
            "iter: 75  cost_fn: -15.52775508339077\n",
            "iter: 76  cost_fn: -15.527755799198886\n",
            "iter: 77  cost_fn: -15.527755635753087\n",
            "iter: 78  cost_fn: -15.527754852233294\n",
            "iter: 79  cost_fn: -15.52775342739473\n",
            "iter: 80  cost_fn: -15.52775233631825\n",
            "iter: 81  cost_fn: -15.527753191379789\n",
            "iter: 82  cost_fn: -15.527755762885292\n",
            "iter: 83  cost_fn: -15.527758252082194\n",
            "iter: 84  cost_fn: -15.527759314253586\n",
            "iter: 85  cost_fn: -15.527758658941693\n",
            "iter: 86  cost_fn: -15.527757154668882\n",
            "iter: 87  cost_fn: -15.5277562215659\n",
            "iter: 88  cost_fn: -15.527756550368471\n",
            "iter: 89  cost_fn: -15.527757692749722\n",
            "iter: 90  cost_fn: -15.52775865160842\n",
            "iter: 91  cost_fn: -15.527758913664213\n",
            "iter: 92  cost_fn: -15.527758728837943\n",
            "iter: 93  cost_fn: -15.527758535278926\n",
            "iter: 94  cost_fn: -15.527758604770245\n",
            "iter: 95  cost_fn: -15.527758810386592\n",
            "iter: 96  cost_fn: -15.52775886288109\n",
            "iter: 97  cost_fn: -15.52775883821171\n",
            "iter: 98  cost_fn: -15.527758958848203\n",
            "iter: 99  cost_fn: -15.527759222284105\n"
          ]
        }
      ],
      "source": [
        "Lowest_E, iter, cost_val, best_weights=run_VQE(r)"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Adaptive circuit\n",
        "\n",
        "Inspired by the idea in paper [3], pennylane people develop an method to reduce number of gates to train before running vqe. The method in Pennylane document [4] is to get rid of the gate with small gradient before executing vqe. The number of gates and parameters are significantly reduced. This way is useful for hardware to get rid of redundant gates since NISQ device have a lot of gate noise and short lifetime of qubits.\n",
        "\n",
        "[3] https://www.nature.com/articles/s41467-019-10988-2 \n",
        "\n",
        "[4] https://pennylane.ai/qml/demos/tutorial_adaptive_circuits.html"
      ],
      "metadata": {
        "id": "n_wdaKeCLCkO"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "vgYFj6MpWsa8"
      },
      "outputs": [],
      "source": [
        "def run_adaptive_cir_VQE(r):\n",
        "    \"\"\"Performs a VQE routine for the given hydrogen molecule.\n",
        "\n",
        "    Args:\n",
        "        coordinates (list(float)): Cartesian coordinates of each hydrogen molecule.\n",
        "        charge (int): The electric charge given to the hydrogen molecule.:\n",
        "\n",
        "    Returns:\n",
        "        (float): The expectation value of the hydrogen Hamiltonian.\n",
        "    \"\"\"\n",
        "\n",
        "    doubles_select, singles_select, num_qubits, hf_state, hamiltonian=BeH2_data(r,1e-5, selection=True)\n",
        "\n",
        "    print(\"The adaptive circuit + vqe uses \",len(doubles_select)+ len(singles_select),\" parameters\")\n",
        "    #print(num_qubits,len(singles),len( doubles))\n",
        "    print(\"The adaptive circuit + vqe uses \",len(doubles_select)*13 + len(singles_select)*2,\" cnot gates\")\n",
        "    print(\"The adaptive circuit + vqe uses \",len(doubles_select)*34 + len(singles_select)*10,\" gates\")\n",
        "\n",
        "    dev = qml.device(\"default.qubit\", wires=num_qubits)\n",
        "\n",
        "    @qml.qnode(dev)\n",
        "    def cost(weights):\n",
        "        \"\"\"A circuit with tunable parameters/weights that measures the expectation value of the hydrogen Hamiltonian.\n",
        "        \n",
        "        Args:\n",
        "            weights (numpy.array): An array of tunable parameters.\n",
        "\n",
        "        Returns:\n",
        "            (float): The expectation value of the hydrogen Hamiltonian.\n",
        "            \n",
        "        \"\"\"\n",
        "        #state=np.array([0]*(num_qubits//2)+[1]*(num_qubits//2))\n",
        "        qml.BasisState(hf_state, wires=list(range(num_qubits)))\n",
        "\n",
        "        for i in range(len(doubles_select)):\n",
        "            qml.DoubleExcitation(weights[i], wires=doubles_select[i])\n",
        "\n",
        "        for i in range(len(singles_select)):\n",
        "            #for j in range(E_len):\n",
        "            qml.SingleExcitation(weights[i+len(doubles_select)], wires=singles_select[i])\n",
        "        \n",
        "        return qml.expval(hamiltonian)\n",
        "    \n",
        "    #np.random.seed = 1234\n",
        "    weights =np.zeros(len(doubles_select + singles_select), requires_grad=True)\n",
        "    opt = qml.AdamOptimizer(stepsize=0.05)\n",
        "\n",
        "    i=0\n",
        "    iter=[]\n",
        "    cost_val=[]\n",
        "    Lowest_E=0\n",
        "    best_weights=0\n",
        "    for _ in range(100):\n",
        "        weights = opt.step(cost, weights)\n",
        "        cost_fn=cost(weights)\n",
        "        if Lowest_E>cost_fn:\n",
        "            Lowest_E=cost_fn\n",
        "            best_weights=weights\n",
        "        cost_val.append(cost_fn)\n",
        "        iter.append(i)\n",
        "        #cost_val.append(cost(weights))\n",
        "        print('iter:',i,' cost_fn:',cost_fn)\n",
        "        i=i+1\n",
        "\n",
        "    return Lowest_E, iter, cost_val, best_weights"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "Lowest_E_a, iter, cost_val_a, best_weights_a=run_adaptive_cir_VQE(r)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "C42Qow2kO4Ml",
        "outputId": "2447a8cb-2355-4c6b-8942-e2c5b7b9e381"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The adaptive circuit + vqe uses  14  parameters\n",
            "The adaptive circuit + vqe uses  182  cnot gates\n",
            "The adaptive circuit + vqe uses  476  gates\n",
            "iter: 0  cost_fn: -15.500364616985351\n",
            "iter: 1  cost_fn: -15.516528830940857\n",
            "iter: 2  cost_fn: -15.523253169035174\n",
            "iter: 3  cost_fn: -15.523265892353956\n",
            "iter: 4  cost_fn: -15.52038230076996\n",
            "iter: 5  cost_fn: -15.517814739079009\n",
            "iter: 6  cost_fn: -15.517128793691477\n",
            "iter: 7  cost_fn: -15.518107922004761\n",
            "iter: 8  cost_fn: -15.519788415206406\n",
            "iter: 9  cost_fn: -15.521340642493776\n",
            "iter: 10  cost_fn: -15.52242627265364\n",
            "iter: 11  cost_fn: -15.523148619966483\n",
            "iter: 12  cost_fn: -15.523748211717114\n",
            "iter: 13  cost_fn: -15.524344900425739\n",
            "iter: 14  cost_fn: -15.524904998649738\n",
            "iter: 15  cost_fn: -15.525338147329403\n",
            "iter: 16  cost_fn: -15.525577037075795\n",
            "iter: 17  cost_fn: -15.525604060556548\n",
            "iter: 18  cost_fn: -15.525461787201438\n",
            "iter: 19  cost_fn: -15.525259686759593\n",
            "iter: 20  cost_fn: -15.525154114084838\n",
            "iter: 21  cost_fn: -15.525275849968596\n",
            "iter: 22  cost_fn: -15.525643583644202\n",
            "iter: 23  cost_fn: -15.526147979787035\n",
            "iter: 24  cost_fn: -15.526620265201762\n",
            "iter: 25  cost_fn: -15.526925970720066\n",
            "iter: 26  cost_fn: -15.527020090120947\n",
            "iter: 27  cost_fn: -15.526943341444857\n",
            "iter: 28  cost_fn: -15.526788297162902\n",
            "iter: 29  cost_fn: -15.526658383311311\n",
            "iter: 30  cost_fn: -15.526624698139216\n",
            "iter: 31  cost_fn: -15.526696719350166\n",
            "iter: 32  cost_fn: -15.526827805568987\n",
            "iter: 33  cost_fn: -15.526957331418572\n",
            "iter: 34  cost_fn: -15.52705708431642\n",
            "iter: 35  cost_fn: -15.527139665951177\n",
            "iter: 36  cost_fn: -15.527224888988345\n",
            "iter: 37  cost_fn: -15.527305477011565\n",
            "iter: 38  cost_fn: -15.527350850799406\n",
            "iter: 39  cost_fn: -15.527340311362783\n",
            "iter: 40  cost_fn: -15.527285231533442\n",
            "iter: 41  cost_fn: -15.527220119124632\n",
            "iter: 42  cost_fn: -15.527180297309894\n",
            "iter: 43  cost_fn: -15.527188481715813\n",
            "iter: 44  cost_fn: -15.527249580357141\n",
            "iter: 45  cost_fn: -15.527345206777142\n",
            "iter: 46  cost_fn: -15.527435620305571\n",
            "iter: 47  cost_fn: -15.527481089715497\n",
            "iter: 48  cost_fn: -15.527470030618705\n",
            "iter: 49  cost_fn: -15.527425577082262\n",
            "iter: 50  cost_fn: -15.527383878714641\n",
            "iter: 51  cost_fn: -15.527367975245854\n",
            "iter: 52  cost_fn: -15.52737985051426\n",
            "iter: 53  cost_fn: -15.527408178827969\n",
            "iter: 54  cost_fn: -15.527437898853664\n",
            "iter: 55  cost_fn: -15.527457080416177\n",
            "iter: 56  cost_fn: -15.527462582807663\n",
            "iter: 57  cost_fn: -15.527460726919378\n",
            "iter: 58  cost_fn: -15.527459886100392\n",
            "iter: 59  cost_fn: -15.527462367828115\n",
            "iter: 60  cost_fn: -15.527464794040922\n",
            "iter: 61  cost_fn: -15.527464560248701\n",
            "iter: 62  cost_fn: -15.527463176808885\n",
            "iter: 63  cost_fn: -15.52746398071651\n",
            "iter: 64  cost_fn: -15.527468755455443\n",
            "iter: 65  cost_fn: -15.527476296436564\n",
            "iter: 66  cost_fn: -15.527482992461524\n",
            "iter: 67  cost_fn: -15.527485496822539\n",
            "iter: 68  cost_fn: -15.527483834037056\n",
            "iter: 69  cost_fn: -15.527481177908609\n",
            "iter: 70  cost_fn: -15.52748027072262\n",
            "iter: 71  cost_fn: -15.527481322265691\n",
            "iter: 72  cost_fn: -15.527483429818966\n",
            "iter: 73  cost_fn: -15.52748606921195\n",
            "iter: 74  cost_fn: -15.52748865842862\n",
            "iter: 75  cost_fn: -15.527490355756026\n",
            "iter: 76  cost_fn: -15.527490888762838\n",
            "iter: 77  cost_fn: -15.527490552063265\n",
            "iter: 78  cost_fn: -15.527489534368367\n",
            "iter: 79  cost_fn: -15.527488313504206\n",
            "iter: 80  cost_fn: -15.527488133346637\n",
            "iter: 81  cost_fn: -15.527489796675505\n",
            "iter: 82  cost_fn: -15.52749240334083\n",
            "iter: 83  cost_fn: -15.527494223643476\n",
            "iter: 84  cost_fn: -15.52749439043276\n",
            "iter: 85  cost_fn: -15.527493284236419\n",
            "iter: 86  cost_fn: -15.527491951689143\n",
            "iter: 87  cost_fn: -15.527491457787344\n",
            "iter: 88  cost_fn: -15.527492149161896\n",
            "iter: 89  cost_fn: -15.527493354609804\n",
            "iter: 90  cost_fn: -15.52749416047179\n",
            "iter: 91  cost_fn: -15.527494328056994\n",
            "iter: 92  cost_fn: -15.527494212819505\n",
            "iter: 93  cost_fn: -15.527494144989587\n",
            "iter: 94  cost_fn: -15.527494160796165\n",
            "iter: 95  cost_fn: -15.527494133570315\n",
            "iter: 96  cost_fn: -15.527494035536101\n",
            "iter: 97  cost_fn: -15.527494050396035\n",
            "iter: 98  cost_fn: -15.527494321382129\n",
            "iter: 99  cost_fn: -15.527494694632441\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# TETRIS adaptive circuit\n",
        "In 2022 September, Virginia Tech's group published the improved version of ADAPT-VQE [5]. It is called TETRIS-ADAPT-VQE. In order to stack the gate well without many idle qubits, they not only add the gate with the latgest gradient but also add the gates with second or third largest gradient but not overlaped with others in the apdative itersion. Inspired by this paper and Katharine's talk of the qhack, we also modified adaptive circuit in Pennylane document. First of all, we also delete some gates with small gradient. Then, we sorted the remaining gate according to their gradient. We try to put them on the circuits without too many overlape among the gates. We call this method as TETRIS adaptive circuit.\n",
        "\n",
        "[5] https://arxiv.org/abs/2209.10562"
      ],
      "metadata": {
        "id": "szZhYlkcltn_"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def run_TETRIS_adaptive_cir_VQE(r):\n",
        "    \n",
        "    doubles_select, singles_select, num_qubits, hf_state, hamiltonian=BeH2_data(r,1e-5, selection=True)\n",
        "    doubles, singles, _, _, _=BeH2_data(r,1e-5, selection=False)\n",
        "\n",
        "    print(\"The TETRIS adaptive VQE use \",len(doubles_select) + len(singles_select),\" parameters\")\n",
        "    print(\"The TETRIS adaptive circuit use \",len(doubles_select)*13 + len(singles_select)*2,\" cnot gates\")\n",
        "    print(\"The TETRIS adaptive circuit use \",len(doubles_select)*34 + len(singles_select)*10,\" gates\")\n",
        "\n",
        "    dev = qml.device(\"default.qubit\", wires=num_qubits)\n",
        "    @qml.qnode(dev)\n",
        "    def circuit_1(params, excitations):\n",
        "        qml.BasisState(hf_state, wires=range(num_qubits))\n",
        "\n",
        "        for i, excitation in enumerate(excitations):\n",
        "            if len(excitation) == 4:\n",
        "                qml.DoubleExcitation(params[i], wires=excitation)\n",
        "            else:\n",
        "                qml.SingleExcitation(params[i], wires=excitation)\n",
        "        return qml.expval(hamiltonian )\n",
        "\n",
        "    params = [0.0] * len(doubles+singles)\n",
        "    params=np.array(params )\n",
        "\n",
        "    dev = qml.device(\"default.qubit\", wires=num_qubits)\n",
        "    cost_fn = qml.QNode(circuit_1, dev)\n",
        "    circuit_gradient = qml.grad(cost_fn, argnum=0)\n",
        "    grads = circuit_gradient(params, excitations=doubles+singles)\n",
        "\n",
        "    op_pool=doubles+singles\n",
        "    new_op_pool=[op_pool[idx] for idx in range(len(grads)) if abs(grads[idx]) >1e-5]\n",
        "    new_grads=[grads[idx] for idx in range(len(grads)) if abs(grads[idx]) > 1e-5]\n",
        "              \n",
        "\n",
        "    Stacked_op_pool=[]\n",
        "\n",
        "    indx=np.argsort(np.abs(new_grads))\n",
        "    sorted_op_pool=[new_op_pool[idx] for idx in indx]\n",
        "\n",
        "    Stacked_op_pool=[]\n",
        "    q_layer=[]     \n",
        "    ind_left=[]       \n",
        "    while len(sorted_op_pool) !=0:\n",
        "        for idx, op in enumerate(sorted_op_pool):\n",
        "            if Stacked_op_pool==[]:\n",
        "                Stacked_op_pool.append(op)\n",
        "                q_layer=q_layer+op\n",
        "            else:\n",
        "                Stack=True\n",
        "                for k in op:\n",
        "                    if k in q_layer:\n",
        "                        Stack=False\n",
        "                        ind_left.append(idx)\n",
        "                        break\n",
        "                if Stack:\n",
        "                    Stacked_op_pool.append(op)\n",
        "                    q_layer=q_layer+op\n",
        "        sorted_op_pool=[sorted_op_pool[idx] for idx in ind_left]\n",
        "        q_layer=[]\n",
        "        ind_left=[]\n",
        "    dev = qml.device(\"default.qubit\", wires=num_qubits)\n",
        "    @qml.qnode(dev)\n",
        "    def cost(params, excitations):\n",
        "        qml.BasisState(hf_state, wires=range(num_qubits))\n",
        "\n",
        "        for i, excitation in enumerate(excitations):\n",
        "            if len(excitation) == 4:\n",
        "                qml.DoubleExcitation(params[i], wires=excitation)\n",
        "            else:\n",
        "                qml.SingleExcitation(params[i], wires=excitation)\n",
        "        return qml.expval(hamiltonian)\n",
        "\n",
        "    weights =np.zeros(len(Stacked_op_pool), requires_grad=True)\n",
        "    opt = qml.AdamOptimizer(stepsize=0.05)\n",
        "\n",
        "    i=0\n",
        "    iter=[]\n",
        "    cost_val=[]\n",
        "    Lowest_E=0\n",
        "    best_weights=0\n",
        "    for _ in range(100):\n",
        "        weights,_ = opt.step(cost, weights, Stacked_op_pool)\n",
        "        cost_fn=cost(weights,Stacked_op_pool)\n",
        "        if Lowest_E>cost_fn:\n",
        "            Lowest_E=cost_fn\n",
        "            best_weights=weights\n",
        "        cost_val.append(cost_fn)\n",
        "        iter.append(i)\n",
        "        #cost_val.append(cost(weights))\n",
        "        print('iter:',i,' cost_fn:',cost_fn)\n",
        "        i=i+1\n",
        "\n",
        "    return Lowest_E, iter, cost_val, best_weights"
      ],
      "metadata": {
        "id": "RhJ30vskl9Y6"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "Lowest_E_t, iter, cost_val_t, best_weights_t=run_TETRIS_adaptive_cir_VQE(r)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mo_6mMTVD8Fc",
        "outputId": "137a845e-322b-4a15-9eca-794935f5e84c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The TETRIS adaptive VQE use  14  parameters\n",
            "The TETRIS adaptive circuit use  182  cnot gates\n",
            "The TETRIS adaptive circuit use  476  gates\n",
            "iter: 0  cost_fn: -15.500330773341302\n",
            "iter: 1  cost_fn: -15.516475074877548\n",
            "iter: 2  cost_fn: -15.523277342963096\n",
            "iter: 3  cost_fn: -15.523461521401991\n",
            "iter: 4  cost_fn: -15.520723258000213\n",
            "iter: 5  cost_fn: -15.518186356678722\n",
            "iter: 6  cost_fn: -15.517374490434019\n",
            "iter: 7  cost_fn: -15.51807898291169\n",
            "iter: 8  cost_fn: -15.519451726670003\n",
            "iter: 9  cost_fn: -15.52081845189387\n",
            "iter: 10  cost_fn: -15.521940204138433\n",
            "iter: 11  cost_fn: -15.522883335195232\n",
            "iter: 12  cost_fn: -15.523738307233483\n",
            "iter: 13  cost_fn: -15.524491288464247\n",
            "iter: 14  cost_fn: -15.52508498884163\n",
            "iter: 15  cost_fn: -15.525492959074853\n",
            "iter: 16  cost_fn: -15.52570728929993\n",
            "iter: 17  cost_fn: -15.525715678230771\n",
            "iter: 18  cost_fn: -15.525538461685354\n",
            "iter: 19  cost_fn: -15.525279575370014\n",
            "iter: 20  cost_fn: -15.52511466311896\n",
            "iter: 21  cost_fn: -15.525191028987294\n",
            "iter: 22  cost_fn: -15.525529405470712\n",
            "iter: 23  cost_fn: -15.526022627766636\n",
            "iter: 24  cost_fn: -15.526512326951059\n",
            "iter: 25  cost_fn: -15.526871653963198\n",
            "iter: 26  cost_fn: -15.527041597628044\n",
            "iter: 27  cost_fn: -15.527028620919433\n",
            "iter: 28  cost_fn: -15.526896995093116\n",
            "iter: 29  cost_fn: -15.526747529996223\n",
            "iter: 30  cost_fn: -15.526669337486043\n",
            "iter: 31  cost_fn: -15.52669193335434\n",
            "iter: 32  cost_fn: -15.526781590853327\n",
            "iter: 33  cost_fn: -15.52688823459782\n",
            "iter: 34  cost_fn: -15.526992705059987\n",
            "iter: 35  cost_fn: -15.527105311062867\n",
            "iter: 36  cost_fn: -15.527227408847939\n",
            "iter: 37  cost_fn: -15.52733041732101\n",
            "iter: 38  cost_fn: -15.527377926922714\n",
            "iter: 39  cost_fn: -15.52735966509863\n",
            "iter: 40  cost_fn: -15.52729760962049\n",
            "iter: 41  cost_fn: -15.527226629785728\n",
            "iter: 42  cost_fn: -15.527178644234132\n",
            "iter: 43  cost_fn: -15.527178464949197\n",
            "iter: 44  cost_fn: -15.527236096965419\n",
            "iter: 45  cost_fn: -15.527333264206101\n",
            "iter: 46  cost_fn: -15.527426172133762\n",
            "iter: 47  cost_fn: -15.52747427780463\n",
            "iter: 48  cost_fn: -15.527469037434214\n",
            "iter: 49  cost_fn: -15.527433279651483\n",
            "iter: 50  cost_fn: -15.527397020709381\n",
            "iter: 51  cost_fn: -15.527378438299232\n",
            "iter: 52  cost_fn: -15.527381899065677\n",
            "iter: 53  cost_fn: -15.527402269807489\n",
            "iter: 54  cost_fn: -15.527427988111071\n",
            "iter: 55  cost_fn: -15.527447200578163\n",
            "iter: 56  cost_fn: -15.527456201940911\n",
            "iter: 57  cost_fn: -15.527460156013854\n",
            "iter: 58  cost_fn: -15.527464460602271\n",
            "iter: 59  cost_fn: -15.52746850526554\n",
            "iter: 60  cost_fn: -15.527468889537198\n",
            "iter: 61  cost_fn: -15.52746522644469\n",
            "iter: 62  cost_fn: -15.527460817220382\n",
            "iter: 63  cost_fn: -15.527459710285424\n",
            "iter: 64  cost_fn: -15.527464152979222\n",
            "iter: 65  cost_fn: -15.527472972156856\n",
            "iter: 66  cost_fn: -15.527481733839606\n",
            "iter: 67  cost_fn: -15.527486174814854\n",
            "iter: 68  cost_fn: -15.527485843556423\n",
            "iter: 69  cost_fn: -15.527483352300786\n",
            "iter: 70  cost_fn: -15.527481000393848\n",
            "iter: 71  cost_fn: -15.527479801859933\n",
            "iter: 72  cost_fn: -15.527480525518152\n",
            "iter: 73  cost_fn: -15.527483337527304\n",
            "iter: 74  cost_fn: -15.527486966273067\n",
            "iter: 75  cost_fn: -15.527489805424706\n",
            "iter: 76  cost_fn: -15.527491226449067\n",
            "iter: 77  cost_fn: -15.527491105569919\n",
            "iter: 78  cost_fn: -15.527489495696956\n",
            "iter: 79  cost_fn: -15.527487528637643\n",
            "iter: 80  cost_fn: -15.527487076590669\n",
            "iter: 81  cost_fn: -15.52748872956226\n",
            "iter: 82  cost_fn: -15.527491218775463\n",
            "iter: 83  cost_fn: -15.52749301877728\n",
            "iter: 84  cost_fn: -15.527493503364393\n",
            "iter: 85  cost_fn: -15.527492827760677\n",
            "iter: 86  cost_fn: -15.52749175116951\n",
            "iter: 87  cost_fn: -15.527491264121748\n",
            "iter: 88  cost_fn: -15.527491669128965\n",
            "iter: 89  cost_fn: -15.527492385102146\n",
            "iter: 90  cost_fn: -15.5274928741003\n",
            "iter: 91  cost_fn: -15.527493173112552\n",
            "iter: 92  cost_fn: -15.527493471088096\n",
            "iter: 93  cost_fn: -15.527493738754506\n",
            "iter: 94  cost_fn: -15.527493813761987\n",
            "iter: 95  cost_fn: -15.52749360130206\n",
            "iter: 96  cost_fn: -15.527493260470642\n",
            "iter: 97  cost_fn: -15.527493148896307\n",
            "iter: 98  cost_fn: -15.527493426065703\n",
            "iter: 99  cost_fn: -15.527493876501415\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Method's Comparison\n",
        "We show how single qubit excitation and double qubit excitation manifested on quantum computer using the single rotation gates and cnot gates [5]. We can convert the single qubit excitation and double qubit excitation gates on the circuit to the native quantum gates. Thus, we can calculate the each method's circuit depth on the real quantum computer.\n",
        "\n",
        "[5] https://arxiv.org/abs/2209.10562"
      ],
      "metadata": {
        "id": "c3IBdQgzxGT1"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "def Doublexcitation(param,wires):\n",
        "    qml.CNOT(wires=[wires[0],wires[1]])\n",
        "    qml.CNOT(wires=[wires[2],wires[3]])\n",
        "    qml.PauliX(wires=wires[1])\n",
        "    qml.PauliX(wires=wires[3])\n",
        "    qml.CNOT(wires=[wires[0],wires[2]])\n",
        "    qml.RY(param/8,wires=[wires[0]])\n",
        "    qml.Hadamard(wires=wires[1])\n",
        "    qml.CNOT(wires=[wires[0],wires[1]])\n",
        "    qml.RY(-param/8,wires=[wires[0]])\n",
        "    qml.Hadamard(wires=wires[3])\n",
        "    qml.CNOT(wires=[wires[0],wires[3]])\n",
        "    qml.RY(param/8,wires=[wires[0]])\n",
        "    qml.CNOT(wires=[wires[0],wires[1]])\n",
        "    qml.RY(-param/8,wires=[wires[0]])\n",
        "    qml.Hadamard(wires=wires[2])\n",
        "    qml.CNOT(wires=[wires[0],wires[2]])\n",
        "    qml.RY(param/8,wires=[wires[0]])\n",
        "    qml.CNOT(wires=[wires[0],wires[1]])\n",
        "    qml.RY(-param/8,wires=[wires[0]])\n",
        "    qml.CNOT(wires=[wires[0],wires[3]])\n",
        "    qml.RY(param/8,wires=[wires[0]])\n",
        "    qml.Hadamard(wires=wires[3])\n",
        "    qml.CNOT(wires=[wires[0],wires[1]])\n",
        "    qml.RY(-param/8,wires=[wires[0]])\n",
        "    qml.Hadamard(wires=wires[1])\n",
        "    qml.RZ(-np.pi/2,wires=[wires[2]])\n",
        "    qml.CNOT(wires=[wires[0],wires[2]])\n",
        "    qml.RZ(np.pi/2,wires=[wires[0]])\n",
        "    qml.RZ(-np.pi/2,wires=[wires[2]])\n",
        "    qml.PauliX(wires=wires[1])\n",
        "    qml.RY(-np.pi/2,wires=[wires[2]])\n",
        "    qml.PauliX(wires=wires[3])\n",
        "    qml.CNOT(wires=[wires[0],wires[1]])\n",
        "    qml.CNOT(wires=[wires[2],wires[3]])\n",
        "\n",
        "def SingleExcitation(param,wires):\n",
        "    qml.RZ(np.pi/2,wires=[wires[0]])\n",
        "    qml.RX(np.pi/2,wires=[wires[0]])\n",
        "    qml.RX(np.pi/2,wires=[wires[1]])\n",
        "    qml.CNOT(wires=[wires[0],wires[1]])\n",
        "    qml.RX(param,wires=[wires[0]])\n",
        "    qml.RZ(param,wires=[wires[1]])\n",
        "    qml.CNOT(wires=[wires[0],wires[1]])\n",
        "    qml.RX(-np.pi/2,wires=[wires[0]])\n",
        "    qml.RX(-np.pi/2,wires=[wires[1]])\n",
        "    qml.RZ(-np.pi/2,wires=[wires[0]])"
      ],
      "metadata": {
        "id": "9T-kf4yYqJzN"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "a0=0.529177210903\n",
        "coordinates=[ 0.0, 0.0, -r/a0, 0.0, 0.0, 0.0, 0.0, 0.0, r/a0] # in atomic unit\n",
        "charge=0\n",
        "hamiltonian = hydrogen_hamiltonian(np.array(coordinates), charge)\n",
        "\n",
        "electrons = num_electrons(charge)\n",
        "num_qubits = len(hamiltonian.wires)\n",
        "\n",
        "hf_state = hf(electrons, num_qubits)\n",
        "# singles and doubles are used to make the AllSinglesDoubles template\n",
        "singles, doubles = qml.qchem.excitations(electrons, num_qubits)\n",
        "\n",
        "dev = qml.device(\"default.qubit\", wires=num_qubits)\n",
        "@qml.qnode(dev)\n",
        "def circuit_1(params, excitations):\n",
        "    #qml.BasisState(hf_state, wires=range(num_qubits))\n",
        "    for i in range(4):\n",
        "        qml.PauliX(wires=i)\n",
        "\n",
        "    for i, excitation in enumerate(excitations):\n",
        "        if len(excitation) == 4:\n",
        "            #qml.DoubleExcitation(params[i], wires=excitation)\n",
        "            Doublexcitation(params[i],excitation)\n",
        "        else:\n",
        "            SingleExcitation(params[i],excitation)\n",
        "            #qml.SingleExcitation(params[i], wires=excitation)\n",
        "    return qml.expval(hamiltonian )\n",
        "\n",
        "\n",
        "def circuit_2(params, excitations):\n",
        "    qml.BasisState(hf_state, wires=range(num_qubits))\n",
        "\n",
        "    for i, excitation in enumerate(excitations):\n",
        "        if len(excitation) == 4:\n",
        "            qml.DoubleExcitation(params[i], wires=excitation)\n",
        "            #Doublexcitation(params[i],excitation)\n",
        "        else:\n",
        "            #SingleExcitation(params[i],excitation)\n",
        "            qml.SingleExcitation(params[i], wires=excitation)\n",
        "    return qml.expval(hamiltonian )\n",
        "\n",
        "specs_func = qml.specs(circuit_1)\n",
        "print(\"Orginal VQE circuit's depth:\",specs_func(best_weights, doubles+singles)['depth'])\n",
        "\n",
        "params = [0.0] * len(doubles)\n",
        "params=np.array(params )\n",
        "\n",
        "dev = qml.device(\"default.qubit\", wires=num_qubits)\n",
        "cost_fn = qml.QNode(circuit_2, dev)\n",
        "circuit_gradient = qml.grad(cost_fn, argnum=0)\n",
        "grads_d = circuit_gradient(params, excitations=doubles)\n",
        "\n",
        "\n",
        "params = [0.0] * len(singles)\n",
        "params=np.array(params )\n",
        "dev = qml.device(\"default.qubit\", wires=num_qubits)\n",
        "cost_fn = qml.QNode(circuit_2, dev)\n",
        "circuit_gradient = qml.grad(cost_fn, argnum=0)\n",
        "grads_s = circuit_gradient(params, excitations=singles)\n",
        "\n",
        "\n",
        "doubles_select = [(doubles*2)[i] for i in range(len(doubles)) if abs(grads_d[i]) > 1e-5]\n",
        "singles_select = [(singles*2)[i] for i in range(len(singles)) if abs(grads_s[i]) > 1e-5]\n",
        "dev = qml.device(\"default.qubit\", wires=num_qubits)\n",
        "\n",
        "specs_func = qml.specs(circuit_1)\n",
        "print(\"Adaptive circuit's depth:\",specs_func(best_weights_a, doubles_select+singles_select)['depth'])\n",
        "      \n",
        "params = [0.0] * len(doubles+singles)\n",
        "params=np.array(params )\n",
        "\n",
        "dev = qml.device(\"default.qubit\", wires=num_qubits)\n",
        "cost_fn = qml.QNode(circuit_2, dev)\n",
        "circuit_gradient = qml.grad(cost_fn, argnum=0)\n",
        "grads = circuit_gradient(params, excitations=doubles+singles)\n",
        "\n",
        "op_pool=doubles+singles\n",
        "new_op_pool=[op_pool[idx] for idx in range(len(grads)) if abs(grads[idx]) >1e-5]\n",
        "new_grads=[grads[idx] for idx in range(len(grads)) if abs(grads[idx]) > 1e-5]\n",
        "          \n",
        "\n",
        "Stacked_op_pool=[]\n",
        "\n",
        "indx=np.argsort(np.abs(new_grads))\n",
        "sorted_op_pool=[new_op_pool[idx] for idx in indx]\n",
        "\n",
        "Stacked_op_pool=[]\n",
        "q_layer=[]     \n",
        "ind_left=[]       \n",
        "while len(sorted_op_pool) !=0:\n",
        "    for idx, op in enumerate(sorted_op_pool):\n",
        "        if Stacked_op_pool==[]:\n",
        "            Stacked_op_pool.append(op)\n",
        "            q_layer=q_layer+op\n",
        "        else:\n",
        "            Stack=True\n",
        "            for k in op:\n",
        "                if k in q_layer:\n",
        "                    Stack=False\n",
        "                    ind_left.append(idx)\n",
        "                    break\n",
        "            if Stack:\n",
        "                Stacked_op_pool.append(op)\n",
        "                q_layer=q_layer+op\n",
        "    sorted_op_pool=[sorted_op_pool[idx] for idx in ind_left]\n",
        "    q_layer=[]\n",
        "    ind_left=[]\n",
        "dev = qml.device(\"default.qubit\", wires=num_qubits)\n",
        "specs_func = qml.specs(circuit_1)\n",
        "print(\"TETRIS Adaptive circuit's depth:\",specs_func(best_weights_a, Stacked_op_pool)['depth'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NKYIP9QsUbDs",
        "outputId": "f9eeb6a0-eb6e-4f6f-b2eb-33c35c72e975"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Orginal VQE circuit's depth: 1535\n",
            "Adaptive circuit's depth: 266\n",
            "TETRIS Adaptive circuit's depth: 147\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(8, 6), dpi=150)\n",
        "plt.plot(iter, cost_val, label='Normal VQE')\n",
        "plt.plot(iter, cost_val_a, label='Adaptive circuit + VQE')\n",
        "plt.plot(iter, cost_val_t, label='TETRIS Adaptive circuit + VQE')\n",
        "plt.axhline(real_solution_t)\n",
        "plt.xticks(fontsize=16)\n",
        "plt.yticks(fontsize=16)\n",
        "plt.ylabel('$E_{result}$', fontsize=18)\n",
        "plt.xlabel('Iteration', fontsize=18)\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 779
        },
        "id": "IkdOb7N_EjKl",
        "outputId": "c765009b-8561-4d69-a6a6-c6643f44e000"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x900 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Using the adaptive circuit and TETRIS adaptive circuit, the number of parameters is signficantly reduced by nearly 84.8%. The both circuit depth are reduced about 82.7% and 90.4 % respectively. One may argue that the accuracy of two method is less than the basic vqe. Due to insufficient parameters in the adaptive circuit and TETRIS adaptive circuit, the result cannot be improved as iteration increases. Thus, we need to add the necessary gates to the adaptive circuit so that the result could be improved a lot during the training process.Here, we use the adaptive vqe or TETRIS adaptive vqe to add the necessary gates during training process. "
      ],
      "metadata": {
        "id": "oeQgB2cF0JN2"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Adaptive VQE + adaptive circuit\n",
        "\n",
        "Here, we reproduce paper's work [3]. Every each iteration, one can add the largest gradient gate to the circuits, and set up the new parameter of the gate zero. Here are following steps [5] how adpative vqe works\n",
        "\n",
        "1. On the classical device, compute one- and two-body\n",
        "integrals and transform the fermionic Hamiltonian\n",
        "to a qubit Hamiltonian using a suitable mapping,\n",
        "e.g., Jordan-Wigner.\n",
        "2. On the quantum device, prepare the current ansatz\n",
        "and measure the energy gradient $∂E/\n",
        "∂θi\n",
        "|θi=0$ for every\n",
        "candidate pool operator $P_i$ with respect to its variational parameter $θ_i$\n",
        ".\n",
        "3. If the pool gradient norm is smaller than a predetermined threshold, ADAPT-VQE has converged,\n",
        "and the algorithm terminates.\n",
        "4. Add the operator with the largest gradient norm\n",
        "from step 2 to the ansatz, with its variational parameter set to zero.\n",
        "5. Perform a VQE subroutine to update all parameters in the current ansatz.\n",
        "6. Repeat steps 2 – 5 until convergence.\n",
        "\n",
        "Here, we use the adaptive circuit as the initial ansatz, and train the circuit with adaptive VQE.\n",
        "\n",
        "[3] https://www.nature.com/articles/s41467-019-10988-2 \n",
        "\n",
        "[5] https://arxiv.org/abs/2209.10562"
      ],
      "metadata": {
        "id": "Ss_z9abrVtAp"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def adapt_vqe_run(r,sol=-15.527759639229922,active_orb=6, num_e=4):\n",
        "    a0=0.529177210903 #bohr to angstrom\n",
        "    coordinates=[0.0, 0.0, -r/a0, 0.0, 0.0, 0.0, 0.0,0.0, r/a0]\n",
        "    charge=0\n",
        "    hamiltonian = hydrogen_hamiltonian(np.array(coordinates), charge, active_orb=active_orb, num_e=num_e)# step.1\n",
        "\n",
        "    electrons = num_e\n",
        "    num_qubits = len(hamiltonian.wires)\n",
        "\n",
        "    singles, doubles = qml.qchem.excitations(electrons, num_qubits) \n",
        "    pool_operators=singles+doubles\n",
        "\n",
        "    num_qubits = len(hamiltonian.wires)\n",
        "    hf_state = hf(electrons, num_qubits)\n",
        "    def circuit_1(params, excitations):\n",
        "        qml.BasisState(hf_state, wires=range(num_qubits))\n",
        "\n",
        "        for i, excitation in enumerate(excitations):\n",
        "            if len(excitation) == 4:\n",
        "                qml.DoubleExcitation(params[i], wires=excitation)\n",
        "            else:\n",
        "                qml.SingleExcitation(params[i], wires=excitation)\n",
        "        return qml.expval(hamiltonian)\n",
        "\n",
        "    params = [0.0] * len(doubles)\n",
        "    params=np.array(params )\n",
        "\n",
        "    dev = qml.device(\"default.qubit\", wires=num_qubits)\n",
        "    cost_fn = qml.QNode(circuit_1, dev)\n",
        "    circuit_gradient = qml.grad(cost_fn, argnum=0)\n",
        "    grads_d = circuit_gradient(params, excitations=doubles)\n",
        "\n",
        "    params = [0.0] * len(singles)\n",
        "    params=np.array(params )\n",
        "\n",
        "    dev = qml.device(\"default.qubit\", wires=num_qubits)\n",
        "    cost_fn = qml.QNode(circuit_1, dev)\n",
        "    circuit_gradient = qml.grad(cost_fn, argnum=0)\n",
        "    grads_s = circuit_gradient(params, excitations=singles)\n",
        "\n",
        "    threshold=1e-5\n",
        "    doubles_select = [(doubles)[i] for i in range(len(doubles)) if abs(grads_d[i]) > threshold] # remove the gate with samller gradient\n",
        "    singles_select = [(singles)[i] for i in range(len(singles)) if abs(grads_s[i]) > threshold] # remove the gate with samller gradient\n",
        "\n",
        "\n",
        "    epochs=25\n",
        "    conv_tol=1e-5\n",
        "    energy=[]\n",
        "\n",
        "    operator_circuits=doubles_select+singles_select\n",
        "    params = [0.0] * len(operator_circuits)\n",
        "    params=np.array(params )\n",
        "\n",
        "    opt = qml.GradientDescentOptimizer(stepsize=0.5) \n",
        "\n",
        "    dev = qml.device(\"default.qubit\", wires=num_qubits)\n",
        "    cost_fn = qml.QNode(circuit_1, dev, expansion_strategy=\"device\")\n",
        "    circuit_gradient = qml.grad(cost_fn, argnum=0)#step.2\n",
        "\n",
        "    energy = [cost_fn(params, operator_circuits)] # step.2\n",
        "    print(f\"Epoch = 0,  Energy = {energy[-1]:.8f} Ha\")\n",
        "    print(\"Number of gates = {}\\n\".format(len(operator_circuits)))\n",
        "    configs=qml.specs(cost_fn)(params, excitations=operator_circuits)\n",
        "    print(\"Circuit Depth = {}\\n\".format(configs['depth'],))\n",
        "\n",
        "    for n in range(epochs):\n",
        "          \n",
        "        Test_set=operator_circuits+pool_operators\n",
        "        Test_params=list(params)+[0.0]*len(pool_operators)\n",
        "        Test_params=np.array(Test_params)\n",
        "\n",
        "        test_grads = circuit_gradient(Test_params,Test_set)\n",
        "        test_grads = test_grads[len(params):]\n",
        "        maxpos = np.argmax(np.abs(test_grads))\n",
        "        \n",
        "        max_op=pool_operators[maxpos]   # step.4 for adaptive one\n",
        "        operator_circuits.append(max_op)# step.4 \n",
        "        params=np.append(params, 0.0)   # step.4\n",
        "        \n",
        "        params,_ = opt.step(cost_fn, params, operator_circuits) # Step 5.\n",
        "        E_val=cost_fn(params, operator_circuits)\n",
        "        energy.append(E_val)\n",
        "        conv = np.abs(sol - E_val) # Step 3. \n",
        "        #t2 = time.time()\n",
        "        print(f\"Epoch = {n+1}, Energy = {energy[-1]:.8f} Ha\")\n",
        "        print(\"Number of gates = {}\\n\".format(len(operator_circuits)))\n",
        "        configs=qml.specs(cost_fn)(params, excitations=operator_circuits)\n",
        "        print(\"Circuit Depth = {}\\n\".format(configs['depth'],))\n",
        "        if conv <= conv_tol:\n",
        "            break\n",
        "\n",
        "    return n+1, energy, params, operator_circuits"
      ],
      "metadata": {
        "id": "IOed1OC5cv02"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "r=1.7\n",
        "iteration_needed_aa, energy_aa, params_aa, operator_circuits_aa=adapt_vqe_run(r)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TAA1zeDoeJ2O",
        "outputId": "b4414cff-db2d-41fc-8fa8-3da19b0c040f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch = 0,  Energy = -15.47060578 Ha\n",
            "Number of gates = 14\n",
            "\n",
            "Circuit Depth = 14\n",
            "\n",
            "Epoch = 1, Energy = -15.49742667 Ha\n",
            "Number of gates = 15\n",
            "\n",
            "Circuit Depth = 15\n",
            "\n",
            "Epoch = 2, Energy = -15.51269841 Ha\n",
            "Number of gates = 16\n",
            "\n",
            "Circuit Depth = 16\n",
            "\n",
            "Epoch = 3, Energy = -15.52057484 Ha\n",
            "Number of gates = 17\n",
            "\n",
            "Circuit Depth = 16\n",
            "\n",
            "Epoch = 4, Energy = -15.52440330 Ha\n",
            "Number of gates = 18\n",
            "\n",
            "Circuit Depth = 17\n",
            "\n",
            "Epoch = 5, Energy = -15.52609793 Ha\n",
            "Number of gates = 19\n",
            "\n",
            "Circuit Depth = 18\n",
            "\n",
            "Epoch = 6, Energy = -15.52687692 Ha\n",
            "Number of gates = 20\n",
            "\n",
            "Circuit Depth = 18\n",
            "\n",
            "Epoch = 7, Energy = -15.52720951 Ha\n",
            "Number of gates = 21\n",
            "\n",
            "Circuit Depth = 19\n",
            "\n",
            "Epoch = 8, Energy = -15.52735670 Ha\n",
            "Number of gates = 22\n",
            "\n",
            "Circuit Depth = 19\n",
            "\n",
            "Epoch = 9, Energy = -15.52743774 Ha\n",
            "Number of gates = 23\n",
            "\n",
            "Circuit Depth = 20\n",
            "\n",
            "Epoch = 10, Energy = -15.52750378 Ha\n",
            "Number of gates = 24\n",
            "\n",
            "Circuit Depth = 20\n",
            "\n",
            "Epoch = 11, Energy = -15.52756492 Ha\n",
            "Number of gates = 25\n",
            "\n",
            "Circuit Depth = 21\n",
            "\n",
            "Epoch = 12, Energy = -15.52761644 Ha\n",
            "Number of gates = 26\n",
            "\n",
            "Circuit Depth = 21\n",
            "\n",
            "Epoch = 13, Energy = -15.52765619 Ha\n",
            "Number of gates = 27\n",
            "\n",
            "Circuit Depth = 22\n",
            "\n",
            "Epoch = 14, Energy = -15.52768523 Ha\n",
            "Number of gates = 28\n",
            "\n",
            "Circuit Depth = 22\n",
            "\n",
            "Epoch = 15, Energy = -15.52770817 Ha\n",
            "Number of gates = 29\n",
            "\n",
            "Circuit Depth = 22\n",
            "\n",
            "Epoch = 16, Energy = -15.52772592 Ha\n",
            "Number of gates = 30\n",
            "\n",
            "Circuit Depth = 22\n",
            "\n",
            "Epoch = 17, Energy = -15.52773828 Ha\n",
            "Number of gates = 31\n",
            "\n",
            "Circuit Depth = 22\n",
            "\n",
            "Epoch = 18, Energy = -15.52774671 Ha\n",
            "Number of gates = 32\n",
            "\n",
            "Circuit Depth = 23\n",
            "\n",
            "Epoch = 19, Energy = -15.52775209 Ha\n",
            "Number of gates = 33\n",
            "\n",
            "Circuit Depth = 24\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Adaptive VQE + TETRIS adaptive circuit\n",
        "\n",
        "Here, we use the TETRIS adaptive circuit as the initial ansatz, and train the circuit with adaptive VQE.\n",
        "\n",
        "[5] https://arxiv.org/abs/2209.10562\n",
        "\n"
      ],
      "metadata": {
        "id": "HT5uVUvfmaj0"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def adapt_vqe_run2(r,sol=-15.527759639229922,active_orb=6,num_e=4):\n",
        "    a0=0.529177210903\n",
        "    coordinates=[0.0, 0.0, -r/a0, 0.0, 0.0, 0.0, 0.0, 0.0, r/a0]\n",
        "    charge=0\n",
        "    hamiltonian = hydrogen_hamiltonian(np.array(coordinates), charge,active_orb=active_orb, num_e=num_e) #step.1\n",
        "\n",
        "    electrons = num_e\n",
        "    num_qubits = len(hamiltonian.wires)\n",
        "\n",
        "    hf_state = hf(electrons, num_qubits)\n",
        "\n",
        "    singles, doubles = qml.qchem.excitations(electrons, num_qubits) #generate UCCSD operators\n",
        "    pool_operators=singles+doubles\n",
        "\n",
        "    def circuit_1(params, excitations):\n",
        "        qml.BasisState(hf_state, wires=range(num_qubits))\n",
        "\n",
        "        for i, excitation in enumerate(excitations):\n",
        "            if len(excitation) == 4:\n",
        "                qml.DoubleExcitation(params[i], wires=excitation)\n",
        "            else:\n",
        "                qml.SingleExcitation(params[i], wires=excitation)\n",
        "        return qml.expval(hamiltonian )\n",
        "\n",
        "\n",
        "\n",
        "    params = [0.0] * len(doubles+singles)\n",
        "    params=np.array(params )\n",
        "\n",
        "    dev = qml.device(\"default.qubit\", wires=num_qubits)\n",
        "    cost_fn = qml.QNode(circuit_1, dev)\n",
        "    circuit_gradient = qml.grad(cost_fn, argnum=0)\n",
        "    grads = circuit_gradient(params, excitations=doubles+singles)\n",
        "\n",
        "    op_pool=doubles+singles\n",
        "    new_op_pool=[op_pool[idx] for idx in range(len(grads)) if abs(grads[idx]) >1e-5]\n",
        "    new_grads=[grads[idx] for idx in range(len(grads)) if abs(grads[idx]) > 1e-5]\n",
        "\n",
        "    dev = qml.device(\"default.qubit\", wires=num_qubits)\n",
        "    cost_fn = qml.QNode(circuit_1, dev)\n",
        "    circuit_gradient = qml.grad(cost_fn, argnum=0)\n",
        "    grads_d = circuit_gradient(params, excitations=doubles)\n",
        "\n",
        "    params = [0.0] * len(singles)\n",
        "    params=np.array(params )\n",
        "\n",
        "    dev = qml.device(\"default.qubit\", wires=num_qubits)\n",
        "    cost_fn = qml.QNode(circuit_1, dev)\n",
        "    circuit_gradient = qml.grad(cost_fn, argnum=0)\n",
        "    grads_s = circuit_gradient(params, excitations=singles)\n",
        "\n",
        "    threshold=1e-5\n",
        "    doubles_select = [(doubles)[i] for i in range(len(doubles)) if abs(grads_d[i]) > threshold] # remove the gate with samller gradient\n",
        "    singles_select = [(singles)[i] for i in range(len(singles)) if abs(grads_s[i]) > threshold] #    \n",
        "\n",
        "\n",
        "    # create the ansatz with large gradient gates, and stack them well.\n",
        "    indx=np.argsort(np.abs(new_grads))\n",
        "    sorted_op_pool=[new_op_pool[idx] for idx in indx]\n",
        "\n",
        "    operator_circuits=[]     \n",
        "    while len(sorted_op_pool) !=0:\n",
        "        q_layer=[]     \n",
        "        ind_left=[]  \n",
        "        for idx, op in enumerate(sorted_op_pool):\n",
        "            if operator_circuits==[]:\n",
        "                operator_circuits.append(op)\n",
        "                q_layer=q_layer+op\n",
        "            else:\n",
        "                Stack=True\n",
        "                for k in op:\n",
        "                    if k in q_layer:\n",
        "                        Stack=False\n",
        "                        ind_left.append(idx)\n",
        "                        break\n",
        "                if Stack:\n",
        "                    operator_circuits.append(op)\n",
        "                    q_layer=q_layer+op\n",
        "        sorted_op_pool=[sorted_op_pool[idx] for idx in ind_left]\n",
        "\n",
        "    params = [0.0] * len(doubles_select+singles_select)\n",
        "    params=np.array(params )\n",
        "\n",
        "    q_layer=[]\n",
        "    epochs=25\n",
        "    conv_tol=1e-5\n",
        "    energy=[]\n",
        "\n",
        "    opt = qml.GradientDescentOptimizer(stepsize=0.5) \n",
        "    dev = qml.device(\"default.qubit\", wires=num_qubits)\n",
        "\n",
        "    dev = qml.device(\"default.qubit\", wires=num_qubits)\n",
        "    cost_fn = qml.QNode(circuit_1, dev, expansion_strategy=\"device\")\n",
        "    circuit_gradient = qml.grad(cost_fn, argnum=0) #step.2\n",
        "\n",
        "    energy = [cost_fn(params, operator_circuits)]  #step.2\n",
        "    print(f\"Epoch = 0,  Energy = {energy[-1]:.8f} Ha\")\n",
        "    print(\"Number of gates = {}\\n\".format(len(operator_circuits)))\n",
        "    configs=qml.specs(cost_fn)(params, excitations=operator_circuits)\n",
        "    print(\"Circuit Depth = {}\\n\".format(configs['depth'],))\n",
        "\n",
        "    for n in range(epochs):\n",
        "\n",
        "        Test_set=operator_circuits+pool_operators\n",
        "        Test_params=list(params)+[0.0]*len(pool_operators)\n",
        "        Test_params=np.array(Test_params)\n",
        "        \n",
        "        test_grads = circuit_gradient(Test_params,Test_set)\n",
        "        test_grads = test_grads[len(params):]\n",
        "\n",
        "        test_sorted_indx=np.argsort(np.abs(test_grads))[::-1] #get sorted gradient in descending order.\n",
        "\n",
        "        operator_circuits.append(pool_operators[test_sorted_indx[0]]) #step.4 for adaptive one\n",
        "        params=np.append(params, 0.0)                                 #step.4\n",
        "\n",
        "        params,_ = opt.step(cost_fn, params, operator_circuits) # Step 5.\n",
        "        E_val=cost_fn(params, operator_circuits)\n",
        "        energy.append(E_val)\n",
        "        conv = np.abs(sol - E_val) # Step 3. ##-15.594361071247999 6 orbital #-15.56723276 4 orbital\n",
        "        #t2 = time.time()\n",
        "        print(f\"Epoch = {n+1}, Energy = {energy[-1]:.8f} Ha\")\n",
        "        print(\"Number of gates = {}\\n\".format(len(operator_circuits)))\n",
        "        configs=qml.specs(cost_fn)(params, excitations=operator_circuits)\n",
        "        print(\"Circuit Depth = {}\\n\".format(configs['depth'],))\n",
        "        if conv <= conv_tol:\n",
        "            break\n",
        "    return n+1, energy, params, operator_circuits"
      ],
      "metadata": {
        "id": "CiWiamwemOup"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "iteration_needed_at, energy_at, params_at, operator_circuits_at=adapt_vqe_run2(r)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FKlt1a_UniAi",
        "outputId": "408601ed-a41b-41bd-b4d9-fc43e88ceac9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch = 0,  Energy = -15.47060578 Ha\n",
            "Number of gates = 14\n",
            "\n",
            "Circuit Depth = 8\n",
            "\n",
            "Epoch = 1, Energy = -15.49742131 Ha\n",
            "Number of gates = 15\n",
            "\n",
            "Circuit Depth = 9\n",
            "\n",
            "Epoch = 2, Energy = -15.51267349 Ha\n",
            "Number of gates = 16\n",
            "\n",
            "Circuit Depth = 10\n",
            "\n",
            "Epoch = 3, Energy = -15.52054134 Ha\n",
            "Number of gates = 17\n",
            "\n",
            "Circuit Depth = 10\n",
            "\n",
            "Epoch = 4, Energy = -15.52437679 Ha\n",
            "Number of gates = 18\n",
            "\n",
            "Circuit Depth = 11\n",
            "\n",
            "Epoch = 5, Energy = -15.52608151 Ha\n",
            "Number of gates = 19\n",
            "\n",
            "Circuit Depth = 12\n",
            "\n",
            "Epoch = 6, Energy = -15.52686867 Ha\n",
            "Number of gates = 20\n",
            "\n",
            "Circuit Depth = 12\n",
            "\n",
            "Epoch = 7, Energy = -15.52720787 Ha\n",
            "Number of gates = 21\n",
            "\n",
            "Circuit Depth = 13\n",
            "\n",
            "Epoch = 8, Energy = -15.52735709 Ha\n",
            "Number of gates = 22\n",
            "\n",
            "Circuit Depth = 13\n",
            "\n",
            "Epoch = 9, Energy = -15.52743873 Ha\n",
            "Number of gates = 23\n",
            "\n",
            "Circuit Depth = 14\n",
            "\n",
            "Epoch = 10, Energy = -15.52750492 Ha\n",
            "Number of gates = 24\n",
            "\n",
            "Circuit Depth = 14\n",
            "\n",
            "Epoch = 11, Energy = -15.52756601 Ha\n",
            "Number of gates = 25\n",
            "\n",
            "Circuit Depth = 15\n",
            "\n",
            "Epoch = 12, Energy = -15.52761739 Ha\n",
            "Number of gates = 26\n",
            "\n",
            "Circuit Depth = 15\n",
            "\n",
            "Epoch = 13, Energy = -15.52765696 Ha\n",
            "Number of gates = 27\n",
            "\n",
            "Circuit Depth = 16\n",
            "\n",
            "Epoch = 14, Energy = -15.52768584 Ha\n",
            "Number of gates = 28\n",
            "\n",
            "Circuit Depth = 16\n",
            "\n",
            "Epoch = 15, Energy = -15.52770863 Ha\n",
            "Number of gates = 29\n",
            "\n",
            "Circuit Depth = 16\n",
            "\n",
            "Epoch = 16, Energy = -15.52772625 Ha\n",
            "Number of gates = 30\n",
            "\n",
            "Circuit Depth = 16\n",
            "\n",
            "Epoch = 17, Energy = -15.52773850 Ha\n",
            "Number of gates = 31\n",
            "\n",
            "Circuit Depth = 16\n",
            "\n",
            "Epoch = 18, Energy = -15.52774683 Ha\n",
            "Number of gates = 32\n",
            "\n",
            "Circuit Depth = 17\n",
            "\n",
            "Epoch = 19, Energy = -15.52775213 Ha\n",
            "Number of gates = 33\n",
            "\n",
            "Circuit Depth = 18\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# TETRIS adaptive VQE + TETRIS adaptive circuit\n",
        "\n",
        "Here we reproduce the paper's work [5]. TETRIS Adaptive VQE is improved version of adaptive vqe. In order to stack the gate well without too many idle qubits (like the plots below), TETRIS adaptive VQE change step 4. of adaptive vqe. Here are new sub steps 4. in TETRIS adaptive VQE\n",
        "\n",
        "(a) Sort pool operators according to the norms of\n",
        "their gradients, in descending order.\n",
        "\n",
        "(b) Identify the operator with the highest gradient norm acting on qubits not acted on by\n",
        "any previously added operator in the current\n",
        "ADAPT iteration. Add the operator to the\n",
        "ansatz, with its variational parameter set to\n",
        "zero.\n",
        "\n",
        "(c) If operators that act non-trivially on all qubits\n",
        "have been added to the ansatz in the current\n",
        "ADAPT iteration, proceed to step 5.\n",
        "\n",
        "(d) If the list of operators with nonzero gradients\n",
        "and disjoint support with operators already\n",
        "added in the current ADAPT iteration has\n",
        "been exhausted, proceed to step 5.\n",
        "\n",
        "(e) Go to sub-step (b).\n",
        "\n",
        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)\n",
        "\n",
        "[5] https://arxiv.org/abs/2209.10562"
      ],
      "metadata": {
        "id": "8LUTOkoWpe_0"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def TETRIS_adapt_vqe_run(r,cutoff=0.0,paper_ver=True,sol=-15.527759639229922,active_orb=6,num_e=4):\n",
        "    a0=0.529177210903\n",
        "    coordinates=[0.0, 0.0, -r/a0, 0.0, 0.0, 0.0, 0.0, 0.0, r/a0]\n",
        "    charge=0\n",
        "    hamiltonian = hydrogen_hamiltonian(np.array(coordinates),charge,active_orb=active_orb, num_e=num_e) # step 1\n",
        "\n",
        "    electrons = num_e\n",
        "    num_qubits = len(hamiltonian.wires)\n",
        "\n",
        "    hf_state = hf(electrons, num_qubits)\n",
        "\n",
        "    singles, doubles = qml.qchem.excitations(electrons, num_qubits) #generate single qubit excitations and double qubit excitations operators.\n",
        "    pool_operators=singles+doubles\n",
        "\n",
        "    def circuit_1(params, excitations):\n",
        "        qml.BasisState(hf_state, wires=range(num_qubits))\n",
        "\n",
        "        for i, excitation in enumerate(excitations):\n",
        "            if len(excitation) == 4:\n",
        "                qml.DoubleExcitation(params[i], wires=excitation)\n",
        "            else:\n",
        "                qml.SingleExcitation(params[i], wires=excitation)\n",
        "        return qml.expval(hamiltonian )\n",
        "\n",
        "\n",
        "    # create the ansatz with large gradient gates, and stack them well.\n",
        "    params = [0.0] * len(doubles+singles)\n",
        "    params=np.array(params )\n",
        "\n",
        "    dev = qml.device(\"default.qubit\", wires=num_qubits)\n",
        "    cost_fn = qml.QNode(circuit_1, dev)\n",
        "    circuit_gradient = qml.grad(cost_fn, argnum=0)\n",
        "    grads = circuit_gradient(params, excitations=doubles+singles)\n",
        "\n",
        "    op_pool=doubles+singles\n",
        "    new_op_pool=[op_pool[idx] for idx in range(len(grads)) if abs(grads[idx]) >1e-5]\n",
        "    new_grads=[grads[idx] for idx in range(len(grads)) if abs(grads[idx]) > 1e-5]\n",
        "\n",
        "    dev = qml.device(\"default.qubit\", wires=num_qubits)\n",
        "    cost_fn = qml.QNode(circuit_1, dev)\n",
        "    circuit_gradient = qml.grad(cost_fn, argnum=0)\n",
        "    grads_d = circuit_gradient(params, excitations=doubles)\n",
        "\n",
        "    params = [0.0] * len(singles)\n",
        "    params=np.array(params )\n",
        "\n",
        "    dev = qml.device(\"default.qubit\", wires=num_qubits)\n",
        "    cost_fn = qml.QNode(circuit_1, dev)\n",
        "    circuit_gradient = qml.grad(cost_fn, argnum=0)\n",
        "    grads_s = circuit_gradient(params, excitations=singles)\n",
        "\n",
        "    threshold=1e-5\n",
        "    doubles_select = [(doubles)[i] for i in range(len(doubles)) if abs(grads_d[i]) > threshold] # remove the gate with samller gradient\n",
        "    singles_select = [(singles)[i] for i in range(len(singles)) if abs(grads_s[i]) > threshold] #    \n",
        "\n",
        "    indx=np.argsort(np.abs(new_grads))\n",
        "    sorted_op_pool=[new_op_pool[idx] for idx in indx]\n",
        "\n",
        "    operator_circuits=[]   # the operator here has shallower circuit depth than the adaptive circuit.  \n",
        "    while len(sorted_op_pool) !=0:\n",
        "        q_layer=[]     \n",
        "        ind_left=[]  \n",
        "        for idx, op in enumerate(sorted_op_pool):\n",
        "            if operator_circuits==[]:\n",
        "                operator_circuits.append(op)\n",
        "                q_layer=q_layer+op\n",
        "            else:\n",
        "                Stack=True\n",
        "                for k in op:\n",
        "                    if k in q_layer:\n",
        "                        Stack=False\n",
        "                        ind_left.append(idx)\n",
        "                        break\n",
        "                if Stack:\n",
        "                    operator_circuits.append(op)\n",
        "                    q_layer=q_layer+op\n",
        "        sorted_op_pool=[sorted_op_pool[idx] for idx in ind_left]\n",
        "\n",
        "    params = [0.0] * len(doubles_select+singles_select)\n",
        "    params=np.array(params )\n",
        "\n",
        "    q_layer=[]\n",
        "    epochs=25\n",
        "    conv_tol=1e-5\n",
        "    energy=[]\n",
        "\n",
        "    opt = qml.GradientDescentOptimizer(stepsize=0.5) \n",
        "    dev = qml.device(\"default.qubit\", wires=num_qubits)\n",
        "\n",
        "    dev = qml.device(\"default.qubit\", wires=num_qubits)\n",
        "    cost_fn = qml.QNode(circuit_1, dev, expansion_strategy=\"device\")\n",
        "    circuit_gradient = qml.grad(cost_fn, argnum=0) # step 2.\n",
        "\n",
        "    energy = [cost_fn(params, operator_circuits)] # step 2.\n",
        "    print(f\"Epoch = 0,  Energy = {energy[-1]:.8f} Ha\")\n",
        "    print(\"Number of gates = {}\\n\".format(len(operator_circuits)))\n",
        "    configs=qml.specs(cost_fn)(params, excitations=operator_circuits)\n",
        "    print(\"Circuit Depth = {}\\n\".format(configs['depth'],))\n",
        "\n",
        "    for n in range(epochs):\n",
        "\n",
        "        Test_set=operator_circuits+pool_operators\n",
        "        Test_params=list(params)+[0.0]*len(pool_operators)\n",
        "        Test_params=np.array(Test_params)\n",
        "        \n",
        "        test_grads = circuit_gradient(Test_params,Test_set)\n",
        "        test_grads = test_grads[len(params):]\n",
        "\n",
        "        test_sorted_indx=np.argsort(np.abs(test_grads))[::-1] #get sorted gradient in descending order. # step 4.(a)\n",
        "        maxpos=test_sorted_indx[0]\n",
        "        Larger_sorted_indx=[]\n",
        "        for ind in test_sorted_indx:\n",
        "            if np.abs(test_grads[ind]) < np.abs(test_grads[maxpos])*cutoff: \n",
        "                break\n",
        "            else:\n",
        "                Larger_sorted_indx.append(ind)\n",
        "        # q_layer here is to make sure that selecting operators acting on most of qubits so that there are less idle qubits\n",
        "\n",
        "        for k,ind in enumerate(Larger_sorted_indx): # Step 4 (b)(c)(d)(e)\n",
        "            #if k != 0:\n",
        "            #    break\n",
        "            op_set=set(pool_operators[ind])\n",
        "            q_layer_set=set(q_layer)\n",
        "            if not bool(op_set & q_layer_set):\n",
        "                q_layer=q_layer+pool_operators[ind]\n",
        "                operator_circuits.append(pool_operators[ind])\n",
        "                params=np.append(params, 0.0)\n",
        "            if (len(q_layer)>=num_qubits-4) and paper_ver: # It is hard to find the operators with non-zero gradient fill all qubit layers.\n",
        "                q_layer=[]                                 # We leave at maximum 4 idle qubits\n",
        "                break\n",
        "\n",
        "        if not  paper_ver: # Since fill the qubit layers is hard, we also try to update q_layer each iteration if one don't want to use paper's step.4 (c)(d)\n",
        "            q_layer=[]\n",
        "\n",
        "        params,_ = opt.step(cost_fn, params, operator_circuits) # Step 5.\n",
        "        E_val=cost_fn(params, operator_circuits)\n",
        "        energy.append(E_val)\n",
        "        conv = np.abs(sol - E_val) #step 3.\n",
        "\n",
        "        print(f\"Epoch = {n+1}, Energy = {energy[-1]:.8f} Ha\")\n",
        "        print(\"Number of gates = {}\\n\".format(len(operator_circuits)))\n",
        "        configs=qml.specs(cost_fn)(params, excitations=operator_circuits)\n",
        "        print(\"Circuit Depth = {}\\n\".format(configs['depth'],))\n",
        "        if conv <= conv_tol:\n",
        "            break\n",
        "    return n+1, energy, params, operator_circuits"
      ],
      "metadata": {
        "id": "YYvonG4tTkCh"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "iteration_needed_tt, energy_tt, params_tt, operator_circuits_tt=TETRIS_adapt_vqe_run(r)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "s0rzAw0GVssY",
        "outputId": "c830a10b-47b3-4849-8160-d1e7c3110478"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch = 0,  Energy = -15.47060578 Ha\n",
            "Number of gates = 14\n",
            "\n",
            "Circuit Depth = 8\n",
            "\n",
            "Epoch = 1, Energy = -15.49828954 Ha\n",
            "Number of gates = 16\n",
            "\n",
            "Circuit Depth = 9\n",
            "\n",
            "Epoch = 2, Energy = -15.51475620 Ha\n",
            "Number of gates = 18\n",
            "\n",
            "Circuit Depth = 10\n",
            "\n",
            "Epoch = 3, Energy = -15.52242107 Ha\n",
            "Number of gates = 20\n",
            "\n",
            "Circuit Depth = 11\n",
            "\n",
            "Epoch = 4, Energy = -15.52565156 Ha\n",
            "Number of gates = 22\n",
            "\n",
            "Circuit Depth = 12\n",
            "\n",
            "Epoch = 5, Energy = -15.52677924 Ha\n",
            "Number of gates = 24\n",
            "\n",
            "Circuit Depth = 13\n",
            "\n",
            "Epoch = 6, Energy = -15.52719755 Ha\n",
            "Number of gates = 26\n",
            "\n",
            "Circuit Depth = 14\n",
            "\n",
            "Epoch = 7, Energy = -15.52736752 Ha\n",
            "Number of gates = 28\n",
            "\n",
            "Circuit Depth = 15\n",
            "\n",
            "Epoch = 8, Energy = -15.52747918 Ha\n",
            "Number of gates = 32\n",
            "\n",
            "Circuit Depth = 16\n",
            "\n",
            "Epoch = 9, Energy = -15.52758265 Ha\n",
            "Number of gates = 36\n",
            "\n",
            "Circuit Depth = 17\n",
            "\n",
            "Epoch = 10, Energy = -15.52766213 Ha\n",
            "Number of gates = 39\n",
            "\n",
            "Circuit Depth = 18\n",
            "\n",
            "Epoch = 11, Energy = -15.52771247 Ha\n",
            "Number of gates = 42\n",
            "\n",
            "Circuit Depth = 19\n",
            "\n",
            "Epoch = 12, Energy = -15.52773842 Ha\n",
            "Number of gates = 45\n",
            "\n",
            "Circuit Depth = 20\n",
            "\n",
            "Epoch = 13, Energy = -15.52775015 Ha\n",
            "Number of gates = 47\n",
            "\n",
            "Circuit Depth = 21\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def run_VQE_again(r,conv_tol=1e-04,active_orb=6,num_e=4):\n",
        "    \"\"\"Performs a VQE routine for the given hydrogen molecule.\n",
        "\n",
        "    Args:\n",
        "        coordinates (list(float)): Cartesian coordinates of each hydrogen molecule.\n",
        "        charge (int): The electric charge given to the hydrogen molecule.:\n",
        "\n",
        "    Returns:\n",
        "        (float): The expectation value of the hydrogen Hamiltonian.\n",
        "    \"\"\"\n",
        "    a0=0.529177210903\n",
        "    coordinates=[0.0, 0.0, -r/a0, 0.0, 0.0, 0.0, 0.0, 0.0, r/a0]\n",
        "    charge=0\n",
        "    hamiltonian = hydrogen_hamiltonian(np.array(coordinates), charge, active_orb=active_orb)\n",
        "\n",
        "    electrons = num_e\n",
        "    num_qubits = len(hamiltonian.wires)\n",
        "\n",
        "    hf_state = hf(electrons, num_qubits)\n",
        "    # singles and doubles are used to make the AllSinglesDoubles template\n",
        "    singles, doubles = qml.qchem.excitations(electrons, num_qubits)\n",
        "    #print(\"Hamiltonian Length:\", len(singles)+len(doubles))\n",
        "    #print(num_qubits,len(singles))\n",
        "\n",
        "    dev = qml.device(\"default.qubit\", wires=num_qubits)\n",
        "\n",
        "    @qml.qnode(dev)\n",
        "    def cost(weights):\n",
        "        \"\"\"A circuit with tunable parameters/weights that measures the expectation value of the hydrogen Hamiltonian.\n",
        "        \n",
        "        Args:\n",
        "            weights (numpy.array): An array of tunable parameters.\n",
        "\n",
        "        Returns:\n",
        "            (float): The expectation value of the hydrogen Hamiltonian.\n",
        "            \n",
        "        \"\"\"\n",
        "        #state=np.array([0]*(num_qubits//2)+[1]*(num_qubits//2))\n",
        "        qml.BasisState(hf_state, wires=list(range(num_qubits)))\n",
        "        \n",
        "        for i in range(len(singles)):\n",
        "            #for j in range(E_len):\n",
        "            qml.SingleExcitation(weights[i], wires=singles[i])\n",
        "\n",
        "        for i in range(len(doubles)):\n",
        "            qml.DoubleExcitation(weights[i+len(singles)], wires=doubles[i])\n",
        "        \n",
        "        return qml.expval(hamiltonian)\n",
        "    \n",
        "    weights =np.zeros(len(singles) + len(doubles), requires_grad=True)\n",
        "    opt = qml.AdamOptimizer(0.5)\n",
        "\n",
        "    iter=[]\n",
        "    cost_val=[]\n",
        "    for i in range(100):\n",
        "        weights = opt.step(cost, weights)\n",
        "        iter.append(i)\n",
        "        e=cost(weights)\n",
        "        conv = np.abs(np.real(real_solution_t) - e)\n",
        "\n",
        "        configs=qml.specs(cost)(weights)\n",
        "        print(i, e)\n",
        "        cost_val.append(e)\n",
        "        if conv <= conv_tol:\n",
        "            break\n",
        "\n",
        "\n",
        "    return i+1, cost_val, weights, singles+doubles"
      ],
      "metadata": {
        "id": "eVWCRR32q3k3"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "iteration_needed, energy, params, operator_circuits=run_VQE_again(r,conv_tol=1e-05)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "o4PIhnKJu_o_",
        "outputId": "62658b5d-cd5c-42bd-af08-e2523e60cc0a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0 -15.053129346930517\n",
            "1 -15.371235288723657\n",
            "2 -15.460717891578755\n",
            "3 -15.302615484550373\n",
            "4 -15.350726026517744\n",
            "5 -15.470791651068968\n",
            "6 -15.480743494040993\n",
            "7 -15.42829394014491\n",
            "8 -15.416458304973094\n",
            "9 -15.460085692203398\n",
            "10 -15.500164788202367\n",
            "11 -15.489725194745764\n",
            "12 -15.466180355878173\n",
            "13 -15.471020025209443\n",
            "14 -15.49695269423783\n",
            "15 -15.509529452089643\n",
            "16 -15.498686681511817\n",
            "17 -15.489433790260499\n",
            "18 -15.497418976955599\n",
            "19 -15.51162489153853\n",
            "20 -15.515814815842893\n",
            "21 -15.509454378604143\n",
            "22 -15.504632367062658\n",
            "23 -15.509213251779698\n",
            "24 -15.51827016635917\n",
            "25 -15.521336864489657\n",
            "26 -15.516336556811382\n",
            "27 -15.512365989114674\n",
            "28 -15.516592961933538\n",
            "29 -15.523506874810682\n",
            "30 -15.523722015406582\n",
            "31 -15.519182093782023\n",
            "32 -15.518394890206665\n",
            "33 -15.522479643787262\n",
            "34 -15.525289815317917\n",
            "35 -15.523791962186944\n",
            "36 -15.522005032694667\n",
            "37 -15.52333351822816\n",
            "38 -15.525377128727618\n",
            "39 -15.525270671511498\n",
            "40 -15.524339015053636\n",
            "41 -15.524902419645347\n",
            "42 -15.526013791972922\n",
            "43 -15.525807760755132\n",
            "44 -15.525409648398487\n",
            "45 -15.526092073206499\n",
            "46 -15.52660562440469\n",
            "47 -15.526334499444975\n",
            "48 -15.526193753648895\n",
            "49 -15.52655203553858\n",
            "50 -15.527000736163245\n",
            "51 -15.52695903423787\n",
            "52 -15.526618335359663\n",
            "53 -15.526831151925997\n",
            "54 -15.527350646860382\n",
            "55 -15.52725724496818\n",
            "56 -15.526918389699874\n",
            "57 -15.527136697855878\n",
            "58 -15.527533532682726\n",
            "59 -15.527413413446412\n",
            "60 -15.527135780496069\n",
            "61 -15.52736589267842\n",
            "62 -15.527671635261047\n",
            "63 -15.52746414681888\n",
            "64 -15.527305833912754\n",
            "65 -15.527574126595553\n",
            "66 -15.527689842157905\n",
            "67 -15.52749941036348\n",
            "68 -15.527493788176397\n",
            "69 -15.527661289316288\n",
            "70 -15.527676311936675\n",
            "71 -15.527578661754818\n",
            "72 -15.527604061330422\n",
            "73 -15.52770189884957\n",
            "74 -15.527678262428259\n",
            "75 -15.527634802360476\n",
            "76 -15.527683265288353\n",
            "77 -15.527713536557433\n",
            "78 -15.52768221189884\n",
            "79 -15.52768921997896\n",
            "80 -15.527727559527444\n",
            "81 -15.527705569054687\n",
            "82 -15.527701593872855\n",
            "83 -15.527738860094797\n",
            "84 -15.52772561935605\n",
            "85 -15.527709713495616\n",
            "86 -15.52773991191133\n",
            "87 -15.527744738298217\n",
            "88 -15.527721873286636\n",
            "89 -15.527736337395606\n",
            "90 -15.527752677033003\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Comparison"
      ],
      "metadata": {
        "id": "s0GliVRFy413"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def count_num_gates(excitations):\n",
        "    cnot_num=0\n",
        "    num_gates=0+4 #preparing the Hatree fock state initially\n",
        "\n",
        "    for i, excitation in enumerate(excitations):\n",
        "        if len(excitation) == 4:\n",
        "            cnot_num=cnot_num+10\n",
        "            num_gates=num_gates+34\n",
        "        else:\n",
        "            cnot_num=cnot_num+2\n",
        "            num_gates=num_gates+13\n",
        "    return cnot_num, num_gates\n",
        "\n",
        "num_qubits=12\n",
        "print(\"In order to get result with error within 1e-5\")\n",
        "print(\"Adaptive vqe + adaptive circuit needs \",iteration_needed_aa, \" iterations\")\n",
        "print(\"Adaptive vqe + TETRIS adaptive circuit needs \",iteration_needed_at, \" iterations\")\n",
        "print(\"TETRIS adaptive vqe + TETRIS adaptive circuit needs \",iteration_needed_tt, \" iterations\")\n",
        "print(\"Orginal vqe needs \", iteration_needed, \"iterations\")\n",
        "\n",
        "dev = qml.device(\"default.qubit\", wires=num_qubits)\n",
        "specs_func = qml.specs(circuit_1)\n",
        "print(\" \")\n",
        "print(\"Adaptive vqe + adaptive circuit depth:\",specs_func(params_aa, operator_circuits_aa)['depth'])\n",
        "print(\"Adaptive vqe + TETRIS adaptive circuit depth:\",specs_func(params_at, operator_circuits_at)['depth'])\n",
        "print(\"TETRIS adaptive vqe + TETRIS adaptive circuit depth:\",specs_func(params_tt, operator_circuits_tt)['depth'])\n",
        "print(\"VQE circuit depth:\",specs_func(params, operator_circuits)['depth'])\n",
        "print(\" \")\n",
        "\n",
        "cnot_num_aa, num_gates_aa=count_num_gates(operator_circuits_aa)\n",
        "cnot_num_at, num_gates_at=count_num_gates(operator_circuits_at)\n",
        "cnot_num_tt, num_gates_tt=count_num_gates(operator_circuits_tt)\n",
        "cnot_num, num_gates=count_num_gates(operator_circuits)\n",
        "print(\"Adaptive vqe + adaptive needs \",cnot_num_aa,' cnot gates and',num_gates_aa,'gates')\n",
        "print(\"Adaptive vqe + TETRIS adaptive circuit needs \",cnot_num_at,' cnot gates and',num_gates_at,'gates')\n",
        "print(\"TETRIS adaptive vqe + TETRIS adaptive circuit needs \",cnot_num_tt,' cnot gates and',num_gates_tt,'gates')\n",
        "print(\"Basic vqe needs \",cnot_num,' cnot gates and',num_gates,'gates')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HIZ9IhnmyKo4",
        "outputId": "99ad967d-0e5c-493e-cb7c-86566c88b804"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "In order to get result with error within 1e-5\n",
            "Adaptive vqe + adaptive circuit needs  19  iterations\n",
            "Adaptive vqe + TETRIS adaptive circuit needs  19  iterations\n",
            "TETRIS adaptive vqe + TETRIS adaptive circuit needs  13  iterations\n",
            "Orginal vqe needs  91 iterations\n",
            " \n",
            "Adaptive vqe + adaptive circuit depth: 431\n",
            "Adaptive vqe + TETRIS adaptive circuit depth: 313\n",
            "TETRIS adaptive vqe + TETRIS adaptive circuit depth: 389\n",
            "VQE circuit depth: 1536\n",
            " \n",
            "Adaptive vqe + adaptive needs  258  cnot gates and 937 gates\n",
            "Adaptive vqe + TETRIS adaptive circuit needs  258  cnot gates and 937 gates\n",
            "TETRIS adaptive vqe + TETRIS adaptive circuit needs  358  cnot gates and 1308 gates\n",
            "Basic vqe needs  792  cnot gates and 2796 gates\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(8, 6), dpi=150)\n",
        "iteration_needed_tt_lst=[i+1 for i in range(iteration_needed_tt+1)]\n",
        "iteration_needed_at_lst=[i+1 for i in range(iteration_needed_at+1)]\n",
        "iteration_needed_aa_lst=[i+1 for i in range(iteration_needed_aa+1)]\n",
        "iteration_needed_lst=[i+1 for i in range(iteration_needed)]\n",
        "plt.plot(iteration_needed_aa_lst, np.array(energy_aa)-real_solution_t, 'g-', label='Adaptive vqe + adaptive circuit')\n",
        "plt.plot(iteration_needed_at_lst, np.array(energy_at)-real_solution_t, 'b--', label='Adaptive vqe + TETRIS adaptive circuit')\n",
        "plt.plot(iteration_needed_tt_lst, np.array(energy_tt)-real_solution_t, 'r', label='TETRIS adaptive vqe + TETRIS adaptive circuit')\n",
        "plt.plot(iteration_needed_lst, np.array(energy)-real_solution_t, 'k', label='Orginal vqe using adam optimizer')\n",
        "plt.yscale('log')\n",
        "plt.ylabel('$|E_{result}-E_{exact}|$', fontsize=18)\n",
        "plt.xlabel('Iteration', fontsize=18)\n",
        "plt.xticks(fontsize=16)\n",
        "plt.yticks(fontsize=16)\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 818
        },
        "id": "7_VfA6hn2DWu",
        "outputId": "e6cc5868-489b-4a22-f0ee-ffa575c35934"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x900 with 1 Axes>"
            ],
            "image/png": 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          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Using the adaptive vqe + adaptive circuit, we just used 937 gates and 258 cnot gates to get the result with error less than 1e-5. But its circuit depth is 431. Adaptive vqe + TETRIS adaptive circuit also needs 937 gates and 258 cnot gates to get the result with error less 1e-5. Its circuit depth is just 313. Although TETRIS adaptive vqe + TETRIS adaptive circuit require more gates, it just spend 13 iterations to obtain the result with error less than 1e-5. Adaptive vqe + adaptive circuit and Adaptive vqe + TETRIS adaptive circuit take 19 iteration to get the error below 1e-5. Compared with the result basic vqe, all of three methods significantly reduce the number of gates, circuit depth, and have better converge. "
      ],
      "metadata": {
        "id": "7eArZIvj4iAC"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# The bond length vs ground state energy\n",
        "(12 qubits require amount of time for qiskit to get exact solution)"
      ],
      "metadata": {
        "id": "H84aNfzGebM7"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "mTqQrIrFWkSw"
      },
      "outputs": [],
      "source": [
        "from qiskit.algorithms import NumPyMinimumEigensolver\n",
        "def get_sol_from_qiskit(r,active_orb=6,num_electrons=4,parity=False):\n",
        "    hydrogen_t = [[\"H\", [-r, 0.0, 0.0]], \n",
        "                  [\"Be\", [0.0, 0.0, 0.0]], \n",
        "                  [\"H\", [r, 0.0, 0.0]]]\n",
        "                      \n",
        "    h3p = Molecule(\n",
        "        geometry=  hydrogen_t,\n",
        "        multiplicity=1, \n",
        "        charge=0, \n",
        "    )\n",
        "\n",
        "    driver = ElectronicStructureMoleculeDriver(h3p, basis=\"sto-3g\", driver_type=ElectronicStructureDriverType.PYSCF) \n",
        "\n",
        "    properties = driver.run()\n",
        "    PN_property = properties.get_property(\"ParticleNumber\")\n",
        "\n",
        "    # Define the active space around the Fermi level \n",
        "    # (selected automatically around the HOMO and LUMO, ordered by energy)\n",
        "    transformer = ActiveSpaceTransformer(\n",
        "        num_electrons=num_electrons, #how many electrons we have in our active space\n",
        "        num_molecular_orbitals=active_orb, #how many orbitals we have in our active space\n",
        "    )\n",
        "\n",
        "    # Now you can get the reduced electronic structure problem\n",
        "    problem_reduced = ElectronicStructureProblem(driver, transformers=[transformer]) \n",
        "\n",
        "    # The second quantized Hamiltonian of the reduce problem\n",
        "    second_q_ops_reduced = problem_reduced.second_q_ops()\n",
        "    if parity !=True:\n",
        "        mapper = JordanWignerMapper()\n",
        "\n",
        "        converter = QubitConverter(mapper)\n",
        "\n",
        "        qubit_op = converter.convert(second_q_ops_reduced[\"ElectronicEnergy\"])\n",
        "\n",
        "    else:\n",
        "        parity_mapper = ParityMapper()\n",
        "        # Set the qubit converter with two qubit reduction to reduce the computational cost \n",
        "        parity_converter = QubitConverter(parity_mapper, two_qubit_reduction=True)    \n",
        "\n",
        "        # Compute the Hamitonian in qubit form\n",
        "        qubit_op_parity = parity_converter.convert(second_q_ops_reduced.get('ElectronicEnergy'), num_particles=problem_reduced.num_particles)\n",
        "\n",
        "    \n",
        "    vqe_factory = VQEUCCFactory( # This is an example of UCC\"SD\" ansatz\n",
        "    quantum_instance=Aer.get_backend(\"aer_simulator_statevector\"),\n",
        "    optimizer=SLSQP(),\n",
        "    ansatz=UCC(excitations='sd')\n",
        "    ) \n",
        "\n",
        "    numpy_solver = NumPyMinimumEigensolver()\n",
        "    if parity==True:\n",
        "        solver = GroundStateEigensolver(parity_converter, vqe_factory)  \n",
        "        real_solution_t = solver.solve(problem_reduced).total_energies[0]\n",
        "    else:\n",
        "        solver = GroundStateEigensolver(converter, vqe_factory)  \n",
        "        real_solution_t = solver.solve(problem_reduced).total_energies[0]\n",
        "    return  real_solution_t"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Takes 25 mins"
      ],
      "metadata": {
        "id": "3y-nIOlvLrQv"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#r_lst=[i*0.2+0.8 for i in range(9)]\n",
        "#TT_result=[]\n",
        "#for r_s in r_lst:  \n",
        "#    _, energy_tt, _, _=TETRIS_adapt_vqe_run(r_s,cutoff=0.9,paper_ver=False)\n",
        "#    TT_result.append(energy_tt[-1])"
      ],
      "metadata": {
        "id": "ueeLvieZ3A55"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Takes 26 mins"
      ],
      "metadata": {
        "id": "jMyPCtD52GBL"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#AA_result=[]\n",
        "#for r_s in r_lst:\n",
        "#    _, energy_aa, _, _=adapt_vqe_run(r_s)\n",
        "#    AA_result.append(energy_aa[-1])"
      ],
      "metadata": {
        "id": "eiBS6YarDjUA"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Takes 26 mins"
      ],
      "metadata": {
        "id": "9vJhSFib2qNv"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#AT_result=[]\n",
        "#for r_s in r_lst:\n",
        "#    _, energy_at, _, _=adapt_vqe_run2(r_s)\n",
        "#    AT_result.append(energy_at[-1])"
      ],
      "metadata": {
        "id": "1vdF5lZ1LFQu"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "36 mins to run"
      ],
      "metadata": {
        "id": "fyEppjNAuDuf"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#result=[]\n",
        "#for r_s in r_lst:\n",
        "#    _, energy, _, _=run_VQE_again(r_s)\n",
        "#    result.append(energy[-1])\n"
      ],
      "metadata": {
        "id": "0P7iVMmzhMQi"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "1 hours to run"
      ],
      "metadata": {
        "id": "WK5uFg-jfh17"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#exact=[]\n",
        "#for r_s in r_lst:\n",
        "#    exact.append(get_sol_from_qiskit(r_s))"
      ],
      "metadata": {
        "id": "xxmWwp9fFImN"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "TT_result=[-15.17215239,-15.48116611,-15.58318749,-15.58996389,-15.55353054,-15.49979572,-15.44297443,\n",
        "          -15.39187758-15.35297833]\n",
        "\n",
        "exact=[-15.172167498683134,-15.481168435226788,\n",
        " -15.583189689928377,-15.589966052930187,\n",
        " 15.553531685130919,-15.499789631936995,\n",
        " -15.442935856112399,-15.39177943788372,\n",
        " -15.353093171812835]\n",
        "\n",
        "result=[-15.1721145,-15.48114261,-15.58316874,-15.58996337,-15.55352461,-15.49978208,-15.44292505,-15.39175577,-15.35304005]\n",
        "\n",
        "AA_result=[-15.17215245,-15.48116611,-15.5831875,-15.5899639,-15.55353048,-15.49979478,\n",
        " -15.44296086,-15.3918006,-15.35254853]\n",
        "\n",
        "AT_result=[-15.17215243,-15.48116611,-15.58318749,-15.58996387,-15.55353038,-15.49979443,\n",
        "          -15.44296093,-15.39180942,-15.35260423]"
      ],
      "metadata": {
        "id": "9gp6myW8Lw43"
      },
      "execution_count": 52,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "r_lst=[i*0.2+0.8 for i in range(9)]\n",
        "plt.figure(figsize=(8, 6), dpi=150)\n",
        "plt.plot(r_lst,exact,'b', label='Exact')\n",
        "plt.plot(r_lst,AA_result,'r--', label='Adaptive-VQE + Adaptive circuit')\n",
        "plt.plot(r_lst,AT_result,'k--', label='Adaptive-VQE + TETRIS Adaptive circuit')\n",
        "plt.plot(r_lst,TT_result,'g--', label='TETRIS Adaptive-VQE + TETRIS Adaptive circuit')\n",
        "plt.plot(r_lst,result,'c--', label='VQE')\n",
        "plt.legend()\n",
        "plt.ylabel('E', fontsize=18)\n",
        "plt.xlabel('distance', fontsize=18)\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "rwGsjfgCiPqc",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 805
        },
        "outputId": "2403c70c-4593-4b30-e261-77bcd3a390b9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x900 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#plt.plot(r_lst,exact,'b', label='Exact')\n",
        "plt.figure(figsize=(8, 6), dpi=150)\n",
        "plt.plot(r_lst,np.abs(np.array(exact)-np.array(AA_result)),'r--', label='Adaptive-VQE + Adaptive circuit + 25 epochs')\n",
        "plt.plot(r_lst,np.abs(np.array(exact)-np.array(AT_result)),'k--', label='Adaptive-VQE + TETRIS Adaptive circuit + 25 epochs')\n",
        "plt.plot(r_lst,np.abs(np.array(exact)-np.array(TT_result)),'g--', label='TETRIS Adaptive-VQE + TETRIS Adaptive circuit + 25 epochs')\n",
        "plt.plot(r_lst,np.abs(np.array(exact)-np.array(result)),'c--', label='VQE +Adam+ 100 epochs')\n",
        "plt.legend()\n",
        "plt.yscale('log')\n",
        "plt.ylabel('E', fontsize=18)\n",
        "plt.xlabel('distance', fontsize=18)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 805
        },
        "id": "MBCA2Rvf13LW",
        "outputId": "1c54d515-7a91-4070-ad5a-323dce4a773f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x900 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "When bond length is between 0.8 and 1.8 angstrom, with only 25 iterations, both of ADAPT vqe and TETRIS-ADAPT vqe work well and reach the result with error within 1e-5.However, when bond length is larger than 1.8 angstrom. The accuracy of the result from ADAPT vqe and TETRIS-ADAPT vqe becomes worse. There are two solution could make result better. The stepsize of optimizer could be tuned larger. One could also run more iteration and see whether the result is improve or not."
      ],
      "metadata": {
        "id": "zPO0BlGbY46R"
      }
    }
  ],
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}