Source code for pennylane.transforms.adjoint_metric_tensor

# 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


# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# See the License for the specific language governing permissions and
# limitations under the License.
Contains the adjoint_metric_tensor.
import warnings
from itertools import chain

import pennylane as qml
from pennylane import numpy as np

# pylint: disable=too-many-statements
from pennylane.transforms.metric_tensor import _contract_metric_tensor_with_cjac

def _reshape_real_imag(state, dim):
    state = qml.math.reshape(state, (dim,))
    return qml.math.real(state), qml.math.imag(state)

def _group_operations(tape):
    """Divide all operations of a tape into trainable operations and blocks
    of untrainable operations after each trainable one."""

    # Extract tape operations list
    ops = tape.operations
    # Find the indices of trainable operations in the tape operations list
    trainables = np.where([qml.operation.is_trainable(op) for op in ops])[0]
    # Add the indices incremented by one to the trainable indices
    split_ids = list(chain.from_iterable([idx, idx + 1] for idx in trainables))

    # Split at trainable and incremented indices to get groups after trainable
    # operations and single trainable operations (in alternating order)
    all_groups = np.split(ops, split_ids)

    # Collect trainable operations and groups after trainable operations
    # the first set of non-trainable ops are the ops "after the -1st" trainable op
    group_after_trainable_op = dict(enumerate(all_groups[::2], start=-1))
    trainable_operations = list(chain.from_iterable(all_groups[1::2]))

    return trainable_operations, group_after_trainable_op

[docs]def adjoint_metric_tensor(circuit, device=None, hybrid=True): r"""Implements the adjoint method outlined in `Jones <>`__ to compute the metric tensor. A forward pass followed by intermediate partial backwards passes are used to evaluate the metric tensor in :math:`\mathcal{O}(p^2)` operations, where :math:`p` is the number of trainable operations, using 4 state vectors. .. note:: The adjoint metric tensor method has the following restrictions: * Currently only ``"default.qubit"`` with ``shots=None`` is supported. * We assume the circuit to be composed of unitary gates only and rely on the ``generator`` property of the gates to be implemented. Note also that this makes the metric tensor strictly real-valued. Args: circuit (.QuantumTape or .QNode): Circuit to compute the metric tensor of device (.Device): Device to use for the adjoint method hybrid (bool): Whether to take classical preprocessing into account. Ignored if ``circuit`` is a tape. Returns: array: the metric tensor of the tape with respect to its trainable parameters. Dimensions are ``(tape.num_params, tape.num_params)``. .. seealso:: :func:`~.metric_tensor` for hardware-compatible metric tensor computations. **Example** Consider the following QNode: .. code-block:: python dev = qml.device("default.qubit", wires=3) @qml.qnode(dev, interface="autograd") def circuit(weights): qml.RX(weights[0], wires=0) qml.RY(weights[1], wires=0) qml.CNOT(wires=[0, 1]) qml.RZ(weights[2], wires=1) qml.RZ(weights[3], wires=0) return qml.expval(qml.PauliZ(0) @ qml.PauliZ(1)), qml.expval(qml.PauliY(1)) We can use the ``adjoint_metric_tensor`` transform to generate a new function that returns the metric tensor of this QNode: >>> mt_fn = qml.adjoint_metric_tensor(circuit) >>> weights = np.array([0.1, 0.2, 0.4, 0.5], requires_grad=True) >>> mt_fn(weights) tensor([[ 0.25 , 0. , -0.0497, -0.0497], [ 0. , 0.2475, 0.0243, 0.0243], [-0.0497, 0.0243, 0.0123, 0.0123], [-0.0497, 0.0243, 0.0123, 0.0123]], requires_grad=True) This approach has the benefit of being significantly faster than the hardware-ready ``metric_tensor`` function: >>> import time >>> start_time = time.process_time() >>> mt = mt_fn(weights) >>> time.process_time() - start_time 0.019 >>> mt_fn_2 = qml.metric_tensor(circuit) >>> start_time = time.process_time() >>> mt = mt_fn_2(weights) >>> time.process_time() - start_time 0.025 This speedup becomes more drastic for larger circuits. The drawback of the adjoint method is that it is only available on simulators and without shot simulations. """ if isinstance(circuit, qml.tape.QuantumScript): return _adjoint_metric_tensor_tape(circuit) if isinstance(circuit, (qml.QNode, qml.ExpvalCost)): return _adjoint_metric_tensor_qnode(circuit, device, hybrid) raise qml.QuantumFunctionError("The passed object is not a QuantumTape or QNode.")
def _adjoint_metric_tensor_tape(tape): """Computes the metric tensor of a tape using the adjoint method and a given device.""" # pylint: disable=protected-access if tape.shots: raise ValueError( "The adjoint method for the metric tensor is only implemented for shots=None" ) if set(tape.wires) != set(range(tape.num_wires)): wire_map = {w: i for i, w in enumerate(tape.wires)} tape = qml.map_wires(tape, wire_map) tape = qml.transforms.expand_trainable_multipar(tape) # Divide all operations of a tape into trainable operations and blocks # of untrainable operations after each trainable one. trainable_operations, group_after_trainable_op = _group_operations(tape) dim = 2**tape.num_wires # generate and extract initial state prep = tape[0] if len(tape) > 0 and isinstance(tape[0], qml.operation.StatePrep) else None interface = qml.math.get_interface(*tape.get_parameters(trainable_only=False)) psi = qml.devices.qubit.create_initial_state(tape.wires, prep, like=interface) # initialize metric tensor components (which all will be real-valued) like_real = qml.math.real(psi[0]) L = qml.math.convert_like(qml.math.zeros((tape.num_params, tape.num_params)), like_real) T = qml.math.convert_like(qml.math.zeros((tape.num_params,)), like_real) for op in group_after_trainable_op[-1]: psi = qml.devices.qubit.apply_operation(op, psi) for j, outer_op in enumerate(trainable_operations): generator_1, prefactor_1 = qml.generator(outer_op) # the state vector phi is missing a factor of 1j * prefactor_1 phi = qml.devices.qubit.apply_operation(generator_1, psi) phi_real, phi_imag = _reshape_real_imag(phi, dim) diag_value = prefactor_1**2 * (, phi_real) +, phi_imag) ) L = qml.math.scatter_element_add(L, (j, j), diag_value) lam = psi * 1.0 lam_real, lam_imag = _reshape_real_imag(lam, dim) # this entry is missing a factor of 1j value = prefactor_1 * (, phi_real) +, phi_imag)) T = qml.math.scatter_element_add(T, (j,), value) for i in range(j - 1, -1, -1): # after first iteration of inner loop: apply U_{i+1}^\dagger if i < j - 1: phi = qml.devices.qubit.apply_operation( qml.adjoint(trainable_operations[i + 1], lazy=False), phi ) # apply V_{i}^\dagger for op in reversed(group_after_trainable_op[i]): adj_op = qml.adjoint(op, lazy=False) phi = qml.devices.qubit.apply_operation(adj_op, phi) lam = qml.devices.qubit.apply_operation(adj_op, lam) inner_op = trainable_operations[i] # extract and apply G_i generator_2, prefactor_2 = qml.generator(inner_op) # this state vector is missing a factor of 1j * prefactor_2 mu = qml.devices.qubit.apply_operation(generator_2, lam) phi_real, phi_imag = _reshape_real_imag(phi, dim) mu_real, mu_imag = _reshape_real_imag(mu, dim) # this entry is missing a factor of 1j * (-1j) = 1, i.e. none value = ( prefactor_1 * prefactor_2 * (, phi_real) +, phi_imag)) ) L = qml.math.scatter_element_add( L, [(i, j), (j, i)], value * qml.math.convert_like(qml.math.ones((2,)), value) ) # apply U_i^\dagger lam = qml.devices.qubit.apply_operation(qml.adjoint(inner_op, lazy=False), lam) # apply U_j and V_j psi = qml.devices.qubit.apply_operation(outer_op, psi) for op in group_after_trainable_op[j]: psi = qml.devices.qubit.apply_operation(op, psi) # postprocessing: combine L and T into the metric tensor. # We require outer(conj(T), T) here, but as we skipped the factor 1j above, # the stored T is real-valued. Thus we have -1j*1j*outer(T, T) = outer(T, T) metric_tensor = L - qml.math.tensordot(T, T, 0) return metric_tensor def _adjoint_metric_tensor_qnode(qnode, device, hybrid): """Computes the metric tensor of a qnode using the adjoint method and its device. For ``hybrid==True`` this wrapper accounts for classical preprocessing within the QNode. """ if device is None: if isinstance(qnode, qml.ExpvalCost): if qnode._multiple_devices: # pylint: disable=protected-access warnings.warn( "ExpvalCost was instantiated with multiple devices. Only the first device " "will be used to evaluate the metric tensor with the adjoint method.", UserWarning, ) qnode = qnode.qnodes[0] device = qnode.device def wrapper(*args, **kwargs): old_interface = qnode.interface if old_interface == "auto": qnode.interface = qml.math.get_interface(*args, *list(kwargs.values())) cjac_fn = qml.transforms.classical_jacobian( qnode, expand_fn=qml.transforms.expand_trainable_multipar ) qnode.construct(args, kwargs) batch, _, _ = qml.devices.qubit.preprocess((qnode.tape,)) mt = _adjoint_metric_tensor_tape(batch[0]) if old_interface == "auto": qnode.interface = "auto" if not hybrid: return mt cjac = cjac_fn(*args, **kwargs) return _contract_metric_tensor_with_cjac(mt, cjac) return wrapper