Source code for pennylane.templates.subroutines.kupccgsd

# Copyright 2018-2021 Xanadu Quantum Technologies Inc.

# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at

#     http://www.apache.org/licenses/LICENSE-2.0

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r"""
Contains the k-UpCCGSD template.
"""
# pylint: disable-msg=too-many-branches,too-many-arguments,protected-access
import numpy as np
import pennylane as qml
from pennylane.operation import Operation, AnyWires


def generalized_singles(wires, delta_sz):
    r"""Return generalized single excitation terms

    .. math::
        \hat{T_1} = \sum_{pq} t_{p}^{q} \hat{c}^{\dagger}_{q} \hat{c}_{p}

    """
    sz = np.array(
        [0.5 if (i % 2 == 0) else -0.5 for i in range(len(wires))]
    )  # alpha-beta electrons
    gen_singles_wires = []
    for r in range(len(wires)):
        for p in range(len(wires)):
            if sz[p] - sz[r] == delta_sz and p != r:
                if r < p:
                    gen_singles_wires.append(wires[r : p + 1])
                else:
                    gen_singles_wires.append(wires[p : r + 1][::-1])
    return gen_singles_wires


def generalized_pair_doubles(wires):
    r"""Return pair coupled-cluster double excitations

    .. math::
        \hat{T_2} = \sum_{pq} t_{p_\alpha p_\beta}^{q_\alpha, q_\beta}
               \hat{c}^{\dagger}_{q_\alpha} \hat{c}^{\dagger}_{q_\beta} \hat{c}_{p_\beta} \hat{c}_{p_\alpha}

    """
    pair_gen_doubles_wires = [
        [
            wires[r : r + 2],
            wires[p : p + 2],
        ]  # wires for [wires[r], wires[r+1], wires[p], wires[p+1]] terms
        for r in range(0, len(wires) - 1, 2)
        for p in range(0, len(wires) - 1, 2)
        if p != r  # remove redundant terms
    ]
    return pair_gen_doubles_wires


[docs]class kUpCCGSD(Operation): r"""Implements the k-Unitary Pair Coupled-Cluster Generalized Singles and Doubles (k-UpCCGSD) ansatz. The k-UpCCGSD ansatz calls the :func:`~.FermionicSingleExcitation` and :func:`~.FermionicDoubleExcitation` templates to exponentiate the product of :math:`k` generalized singles and pair coupled-cluster doubles excitation operators. Here, "generalized" means that the single and double excitation terms do not distinguish between occupied and unoccupied orbitals. Additionally, the term "pair coupled-cluster" refers to the fact that the double excitations contain only those two-body excitations that move a pair of electrons from one spatial orbital to another. This k-UpCCGSD belongs to the family of Unitary Coupled Cluster (UCC) based ansätze, commonly used to solve quantum chemistry problems on quantum computers. The k-UpCCGSD unitary, within the first-order Trotter approximation for a given integer :math:`k`, is given by: .. math:: \hat{U}(\vec{\theta}) = \prod_{l=1}^{k} \bigg(\prod_{p,r}\exp{\Big\{ \theta_{r}^{p}(\hat{c}^{\dagger}_p\hat{c}_r - \text{H.c.})\Big\}} \ \prod_{i,j} \Big\{\exp{\theta_{j_\alpha j_\beta}^{i_\alpha i_\beta} (\hat{c}^{\dagger}_{i_\alpha}\hat{c}^{\dagger}_{i_\beta} \hat{c}_{j_\alpha}\hat{c}_{j_\beta} - \text{H.c.}) \Big\}}\bigg) where :math:`\hat{c}` and :math:`\hat{c}^{\dagger}` are the fermionic annihilation and creation operators. The indices :math:`p, q` run over the spin orbitals and :math:`i, j` run over the spatial orbitals. The singles and paired doubles amplitudes :math:`\theta_{r}^{p}` and :math:`\theta_{j_\alpha j_\beta}^{i_\alpha i_\beta}` represent the set of variational parameters. Args: weights (tensor_like): Tensor containing the parameters :math:`\theta_{pr}` and :math:`\theta_{pqrs}` entering the Z rotation in :func:`~.FermionicSingleExcitation` and :func:`~.FermionicDoubleExcitation`. These parameters are the coupled-cluster amplitudes that need to be optimized for each generalized single and pair double excitation terms. wires (Iterable): wires that the template acts on k (int): Number of times UpCCGSD unitary is repeated. delta_sz (int): Specifies the selection rule ``sz[p] - sz[r] = delta_sz`` for the spin-projection ``sz`` of the orbitals involved in the generalized single excitations. ``delta_sz`` can take the values :math:`0` and :math:`\pm 1`. init_state (array[int]): Length ``len(wires)`` occupation-number vector representing the HF state. ``init_state`` is used to initialize the wires. .. details:: :title: Usage Details #. The number of wires has to be equal to the number of spin-orbitals included in the active space, and should be even. #. The number of trainable parameters scales linearly with the number of layers as :math:`2 k n`, where :math:`n` is the total number of generalized singles and paired doubles excitation terms. An example of how to use this template is shown below: .. code-block:: python import numpy as np import pennylane as qml # Build the electronic Hamiltonian symbols = ["H", "H"] coordinates = np.array([0.0, 0.0, -0.6614, 0.0, 0.0, 0.6614]) H, qubits = qml.qchem.molecular_hamiltonian(symbols, coordinates) # Define the Hartree-Fock state electrons = 2 ref_state = qml.qchem.hf_state(electrons, qubits) # Define the device dev = qml.device('default.qubit', wires=qubits) # Define the ansatz @qml.qnode(dev) def ansatz(weights): qml.kUpCCGSD(weights, wires=[0, 1, 2, 3], k=1, delta_sz=0, init_state=ref_state) return qml.expval(H) # Get the shape of the weights for this template layers = 1 shape = qml.kUpCCGSD.shape(k=layers, n_wires=qubits, delta_sz=0) # Initialize the weight tensors np.random.seed(24) weights = np.random.random(size=shape) # Define the optimizer opt = qml.GradientDescentOptimizer(stepsize=0.4) # Store the values of the cost function energy = [ansatz(weights)] # Store the values of the circuit weights angle = [weights] max_iterations = 100 conv_tol = 1e-06 for n in range(max_iterations): weights, prev_energy = opt.step_and_cost(ansatz, weights) energy.append(ansatz(weights)) angle.append(weights) conv = np.abs(energy[-1] - prev_energy) if n % 4 == 0: print(f"Step = {n}, Energy = {energy[-1]:.8f} Ha") if conv <= conv_tol: break print("\n" f"Final value of the ground-state energy = {energy[-1]:.8f} Ha") print("\n" f"Optimal value of the circuit parameters = {angle[-1]}") .. code-block:: none Step = 0, Energy = 0.18046117 Ha Step = 4, Energy = -1.06545723 Ha Step = 8, Energy = -1.13028734 Ha Step = 12, Energy = -1.13528393 Ha Step = 16, Energy = -1.13604954 Ha Step = 20, Energy = -1.13616762 Ha Step = 24, Energy = -1.13618584 Ha Final value of the ground-state energy = -1.13618786 Ha Optimal value of the circuit parameters = [[ 0.97882258 0.46090942 0.98106201 0.45866993 -0.91548184 2.01637919]] **Parameter shape** The shape of the weights argument can be computed by the static method :meth:`~.kUpCCGSD.shape` and used when creating randomly initialised weight tensors: .. code-block:: python shape = qml.kUpCCGSD.shape(n_layers=2, n_wires=4) weights = np.random.random(size=shape) >>> weights.shape (2, 6) """ num_wires = AnyWires def __init__(self, weights, wires, k=1, delta_sz=0, init_state=None, do_queue=True, id=None): if len(wires) < 4: raise ValueError(f"Requires at least four wires; got {len(wires)} wires.") if len(wires) % 2: raise ValueError(f"Requires even number of wires; got {len(wires)} wires.") if k < 1: raise ValueError(f"Requires k to be at least 1; got {k}.") if delta_sz not in [-1, 0, 1]: raise ValueError(f"Requires delta_sz to be one of ±1 or 0; got {delta_sz}.") s_wires = generalized_singles(list(wires), delta_sz) d_wires = generalized_pair_doubles(list(wires)) shape = qml.math.shape(weights) if shape != ( k, len(s_wires) + len(d_wires), ): raise ValueError( f"Weights tensor must be of shape {(k, len(s_wires) + len(d_wires),)}; got {shape}." ) init_state = qml.math.toarray(init_state) if init_state.dtype != np.dtype("int"): raise ValueError(f"Elements of 'init_state' must be integers; got {init_state.dtype}") self._hyperparameters = { "init_state": init_state, "s_wires": s_wires, "d_wires": d_wires, "k": k, } super().__init__(weights, wires=wires, do_queue=do_queue, id=id) @property def num_params(self): return 1
[docs] @staticmethod def compute_decomposition( weights, wires, s_wires, d_wires, k, init_state ): # pylint: disable=arguments-differ r"""Representation of the operator as a product of other operators. .. math:: O = O_1 O_2 \dots O_n. .. seealso:: :meth:`~.kUpCCGSD.decomposition`. Args: weights (tensor_like): tensor containing the parameters entering the Z rotation wires (Any or Iterable[Any]): wires that the operator acts on k (int): number of times UpCCGSD unitary is repeated s_wires (Iterable[Any]): single excitation wires d_wires (Iterable[Any]): double excitation wires init_state (array[int]): Length ``len(wires)`` occupation-number vector representing the HF state. Returns: list[.Operator]: decomposition of the operator """ init_state_flipped = np.flip(init_state) op_list = [] op_list.append(qml.BasisEmbedding(init_state_flipped, wires=wires)) for layer in range(k): for i, (w1, w2) in enumerate(d_wires): op_list.append( qml.FermionicDoubleExcitation( weights[layer][len(s_wires) + i], wires1=w1, wires2=w2 ) ) for j, s_wires_ in enumerate(s_wires): op_list.append(qml.FermionicSingleExcitation(weights[layer][j], wires=s_wires_)) return op_list
[docs] @staticmethod def shape(k, n_wires, delta_sz): r"""Returns the shape of the weight tensor required for this template. Args: k (int): Number of layers n_wires (int): Number of qubits delta_sz (int): Specifies the selection rules ``sz[p] - sz[r] = delta_sz`` for the spin-projection ``sz`` of the orbitals involved in the single excitations. ``delta_sz`` can take the values :math:`0` and :math:`\pm 1`. Returns: tuple[int]: shape """ if n_wires < 4: raise ValueError( f"This template requires the number of qubits to be greater than four; got 'n_wires' = {n_wires}" ) if n_wires % 2: raise ValueError( f"This template requires an even number of qubits; got 'n_wires' = {n_wires}" ) s_wires = generalized_singles(range(n_wires), delta_sz) d_wires = generalized_pair_doubles(range(n_wires)) return k, len(s_wires) + len(d_wires)