Source code for pennylane.gradients.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
# http://www.apache.org/licenses/LICENSE-2.0
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""
Contains the metric_tensor batch_transform which wraps multiple
methods of computing the metric tensor.
"""
from typing import Sequence, Callable
import functools
from functools import partial
import warnings
import numpy as np
import pennylane as qml
from pennylane.circuit_graph import LayerData
from pennylane.queuing import WrappedObj
from pennylane.transforms import transform
def _contract_metric_tensor_with_cjac(mt, cjac, tape): # pylint: disable=unused-argument
"""Execute the contraction of pre-computed classical Jacobian(s)
and the metric tensor of a tape in order to obtain the hybrid
metric tensor of a QNode.
Args:
mt (array): Metric tensor of a tape (2-dimensional)
cjac (array or tuple[array]): The classical Jacobian of a QNode
Returns:
array or tuple[array]: Hybrid metric tensor(s) of the QNode.
The number of metric tensors depends on the number of QNode arguments
for which the classical Jacobian was computed, the tensor shape(s)
depend on the shape of these QNode arguments.
"""
if isinstance(mt, tuple) and len(mt) == 1:
mt = mt[0]
if isinstance(cjac, tuple):
# Classical processing of multiple arguments is present. Return cjac.T @ mt @ cjac
# as a tuple of contractions.
metric_tensors = tuple(
qml.math.tensordot(c, qml.math.tensordot(mt, c, axes=[[-1], [0]]), axes=[[0], [0]])
for c in cjac
if c is not None
)
return metric_tensors[0] if len(metric_tensors) == 1 else metric_tensors
is_square = cjac.shape == (1,) or (cjac.ndim == 2 and cjac.shape[0] == cjac.shape[1])
if is_square and qml.math.allclose(cjac, qml.numpy.eye(cjac.shape[0])):
# Classical Jacobian is the identity. No classical processing
# is present inside the QNode.
return mt
mt_cjac = qml.math.tensordot(mt, cjac, axes=[[-1], [0]])
mt = qml.math.tensordot(cjac, mt_cjac, axes=[[0], [0]])
return mt
def _expand_metric_tensor(
tape: qml.tape.QuantumTape,
argnum=None,
approx=None,
allow_nonunitary=True,
aux_wire=None,
device_wires=None,
) -> (Sequence[qml.tape.QuantumTape], Callable): # pylint: disable=too-many-arguments
"""Set the metric tensor based on whether non-unitary gates are allowed."""
# pylint: disable=unused-argument,too-many-arguments
if not allow_nonunitary and approx is None:
return [qml.transforms.expand_nonunitary_gen(tape)], lambda x: x[0]
return [qml.transforms.expand_multipar(tape)], lambda x: x[0]
[docs]@partial(
transform,
expand_transform=_expand_metric_tensor,
classical_cotransform=_contract_metric_tensor_with_cjac,
final_transform=True,
)
def metric_tensor( # pylint:disable=too-many-arguments
tape: qml.tape.QuantumTape,
argnum=None,
approx=None,
allow_nonunitary=True,
aux_wire=None,
device_wires=None,
) -> (Sequence[qml.tape.QuantumTape], Callable):
r"""Returns a function that computes the metric tensor of a given QNode or quantum tape.
The metric tensor convention we employ here has the following form:
.. math::
\text{metric_tensor}_{i, j} = \text{Re}\left[ \langle \partial_i \psi(\bm{\theta}) | \partial_j \psi(\bm{\theta}) \rangle
- \langle \partial_i \psi(\bm{\theta}) | \psi(\bm{\theta}) \rangle \langle \psi(\bm{\theta}) | \partial_j \psi(\bm{\theta}) \rangle \right]
with short notation :math:`| \partial_j \psi(\bm{\theta}) \rangle := \frac{\partial}{\partial \theta_j}| \psi(\bm{\theta}) \rangle`.
It is closely related to the quantum fisher information matrix, see :func:`~.pennylane.qinfo.transforms.quantum_fisher` and eq. (27) in `arxiv:2103.15191 <https://arxiv.org/abs/2103.15191>`_.
.. note::
Only gates that have a single parameter and define a ``generator`` are supported.
All other parametrized gates will be decomposed if possible.
The ``generator`` of all parametrized operations, with respect to which the
tensor is computed, are assumed to be Hermitian.
This is the case for unitary single-parameter operations.
Args:
tape (QNode or QuantumTape): quantum circuit to find the metric tensor of
argnum (int or Sequence[int] or None): Trainable tape-parameter indices with respect to which
the metric tensor is computed. If ``argnum=None``, the metric tensor with respect to all
trainable parameters is returned. Excluding tape-parameter indices from this list reduces
the computational cost and the corresponding metric-tensor elements will be set to 0.
approx (str): Which approximation of the metric tensor to compute.
- If ``None``, the full metric tensor is computed
- If ``"block-diag"``, the block-diagonal approximation is computed, reducing
the number of evaluated circuits significantly.
- If ``"diag"``, only the diagonal approximation is computed, slightly
reducing the classical overhead but not the quantum resources
(compared to ``"block-diag"``).
allow_nonunitary (bool): Whether non-unitary operations are allowed in circuits
created by the transform. Only relevant if ``approx`` is ``None``.
Should be set to ``True`` if possible to reduce cost.
aux_wire (None or int or str or Sequence or pennylane.wires.Wires): Auxiliary wire to
be used for Hadamard tests. If ``None`` (the default), a suitable wire is inferred
from the (number of) used wires in the original circuit and ``device_wires``,
if the latter are given.
device_wires (.wires.Wires): Wires of the device that is going to be used for the
metric tensor. Facilitates finding a default for ``aux_wire`` if ``aux_wire``
is ``None``.
hybrid (bool): Specifies whether classical processing inside a QNode
should be taken into account when transforming a QNode.
- If ``True``, and classical processing is detected, the Jacobian of the classical
processing will be computed and included. When evaluated, the
returned metric tensor will be with respect to the QNode arguments.
The output shape can vary widely.
- If ``False``, any internal QNode classical processing will be
**ignored**. When evaluated, the returned metric tensor will be with
respect to the **gate** arguments, and not the QNode arguments.
The output shape is a single two-dimensional tensor.
Returns:
qnode (QNode) or tuple[List[QuantumTape], function]:
The transformed circuit as described in :func:`qml.transform <pennylane.transform>`. Executing this circuit
will provide the metric tensor in the form of a tensor.
The block-diagonal part of the metric tensor always is computed using the
covariance-based approach. If no approximation is selected,
the off block-diagonal is computed using Hadamard tests.
.. warning::
Performing the Hadamard tests requires a device
that has an additional wire as compared to the wires on which the
original circuit was defined. This wire may be specified via ``aux_wire``.
The available wires on the device may be specified via ``device_wires``.
By default (that is, if ``device_wires=None`` ), contiguous wire
numbering and usage is assumed and the additional
wire is set to the next wire of the device after the circuit wires.
If the given or inferred ``aux_wire`` does not exist on the device,
a warning is raised and the block-diagonal approximation is computed instead.
It is significantly cheaper in this case to explicitly set ``approx="block-diag"`` .
The flag ``allow_nonunitary`` should be set to ``True`` whenever the device with
which the metric tensor is computed supports non-unitary operations.
This will avoid additional decompositions of gates, in turn avoiding a potentially
large number of additional Hadamard test circuits to be run.
State vector simulators, for example, often allow applying operations that are
not unitary.
On a real QPU, setting this flag to ``True`` may cause exceptions because the
computation of the metric tensor will request invalid operations on a quantum
device.
**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.Z(0) @ qml.Z(1)), qml.expval(qml.Y(1))
We can use the ``metric_tensor`` transform to generate a new function that returns the
metric tensor of this QNode:
>>> mt_fn = qml.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)
In order to save cost, one might want to compute only the block-diagonal part of
the metric tensor, which requires significantly fewer executions of quantum functions
and does not need an auxiliary wire on the device. This can be done using the
``approx`` keyword:
>>> mt_fn = qml.metric_tensor(circuit, approx="block-diag")
>>> weights = np.array([0.1, 0.2, 0.4, 0.5], requires_grad=True)
>>> mt_fn(weights)
tensor([[0.25 , 0. , 0. , 0. ],
[0. , 0.2475, 0. , 0. ],
[0. , 0. , 0.0123, 0.0123],
[0. , 0. , 0.0123, 0.0123]], requires_grad=True)
These blocks are given by parameter groups that belong to groups of commuting gates.
The tensor can be further restricted to the diagonal via ``approx="diag"``. However,
this will not save further quantum function evolutions but only classical postprocessing.
The returned metric tensor is also fully differentiable in all interfaces.
For example, we can compute the gradient of the Frobenius norm of the metric tensor
with respect to the QNode ``weights`` :
>>> norm_fn = lambda x: qml.math.linalg.norm(mt_fn(x), ord="fro")
>>> grad_fn = qml.grad(norm_fn)
>>> grad_fn(weights)
array([-0.0282246 , 0.01340413, 0. , 0. ])
.. details::
:title: Usage Details
This transform can also be applied to low-level
:class:`~.QuantumTape` objects. This will result in no implicit quantum
device evaluation. Instead, the processed tapes, and post-processing
function, which together define the metric tensor are directly returned:
>>> params = np.array([1.7, 1.0, 0.5], requires_grad=True)
>>> ops = [
... qml.RX(params[0], wires=0),
... qml.RY(params[1], wires=0),
... qml.CNOT(wires=(0,1)),
... qml.PhaseShift(params[2], wires=1),
... ]
>>> measurements = [qml.expval(qml.X(0))]
>>> tape = qml.tape.QuantumTape(ops, measurements)
>>> tapes, fn = qml.metric_tensor(tape)
>>> tapes
[<QuantumTape: wires=[0, 1], params=0>,
<QuantumTape: wires=[0, 1], params=1>,
<QuantumTape: wires=[0, 1], params=3>,
<QuantumTape: wires=[2, 0], params=1>,
<QuantumTape: wires=[2, 0, 1], params=2>,
<QuantumTape: wires=[2, 0, 1], params=2>]
This can be useful if the underlying circuits representing the metric tensor
computation need to be analyzed. We clearly can distinguish the first three
tapes used for the block-diagonal from the last three tapes that use the
auxiliary wire ``2`` , which was not used by the original tape.
The output tapes can then be evaluated and post-processed to retrieve
the metric tensor:
>>> dev = qml.device("default.qubit", wires=3)
>>> fn(qml.execute(tapes, dev, None))
tensor([[ 0.25 , 0. , 0.42073549],
[ 0. , 0.00415023, -0.26517488],
[ 0.42073549, -0.26517488, 0.24878844]], requires_grad=True)
The first term of the off block-diagonal entries of the full metric tensor are
computed with Hadamard tests. This first term reads
.. math ::
\mathfrak{Re}\left\{\langle \partial_i\psi|\partial_j\psi\rangle\right\}
and can be computed using an augmented circuit with an additional qubit.
See for example the appendix of `McArdle et al. (2019) <https://doi.org/10.1038/s41534-019-0187-2>`__
for details.
The block-diagonal of the tensor is computed using the covariance matrix approach.
In addition, we may extract the factors for the second terms
:math:`\langle \psi|\partial_j\psi\rangle`
of the *off block-diagonal* tensor from the quantum function output for the covariance matrix!
This means that in total only the tapes for the first terms of the off block-diagonal
are required in addition to the circuits for the block diagonal.
.. warning::
The ``argnum`` argument can be used to restrict the parameters which are taken into account
for computing the metric tensor.
When the metric tensor of a QNode is computed, the ordering of the parameters has to be
specified as they appear in the corresponding QuantumTape.
**Example**
Consider the following QNode in which parameters are used out of order:
.. code-block:: python
>>> dev = qml.device("default.qubit", wires=3)
>>> @qml.qnode(dev, interface="autograd")
>>> def circuit(weights): # , extra_weight):
... qml.RX(weights[1], wires=0)
... qml.RY(weights[0], wires=0)
... qml.CNOT(wires=[0, 1])
... qml.RZ(weights[2], wires=1)
... qml.RZ(weights[3], wires=0)
... return qml.expval(qml.Z(0))
>>> weights = np.array([0.1, 0.2, 0.4, 0.5], requires_grad=True)
>>> mt = qml.metric_tensor(circuit, argnum=(0, 2, 3))(weights)
>>> print(mt)
[[ 0. 0. 0. 0. ]
[ 0. 0.25 -0.02495835 -0.02495835]
[ 0. -0.02495835 0.01226071 0.01226071]
[ 0. -0.02495835 0.01226071 0.01226071]]
Because the 0-th element of ``weights`` appears second in the QNode and therefore in the
underlying tape, it is the 1st tape parameter.
By setting ``argnum = (0, 2, 3)`` we exclude the 0-th element of ``weights`` from the computation
of the metric tensor and not the 1st element, as one might expect.
"""
if not tape.trainable_params:
warnings.warn(
"Attempted to compute the metric tensor of a tape with no trainable parameters. "
"If this is unintended, please mark trainable parameters in accordance with the "
"chosen auto differentiation framework, or via the 'tape.trainable_params' property."
)
return [], lambda _: ()
if argnum is None:
argnum = tape.trainable_params
elif isinstance(argnum, int):
argnum = [argnum]
if any(i not in tape.trainable_params for i in argnum):
raise ValueError(
"Some parameters specified in argnum are not in the "
f"trainable parameters {tape.trainable_params} of the tape "
"and will be ignored. This may be caused by attempting to "
"differentiate with respect to parameters that are not marked "
"as trainable."
)
if approx in {"diag", "block-diag"}:
# Only require covariance matrix based transform
diag_approx = approx == "diag"
tapes, processing_fn = _metric_tensor_cov_matrix(tape, argnum, diag_approx)[:2]
return tapes, processing_fn
if approx is None:
tapes, processing_fn = _metric_tensor_hadamard(
tape, argnum, allow_nonunitary, aux_wire, device_wires
)
return tapes, processing_fn
raise ValueError(
f"Unknown value {approx} for keyword argument approx. "
"Valid values are 'diag', 'block-diag' and None."
)
@metric_tensor.custom_qnode_transform
def qnode_execution_wrapper(self, qnode, targs, tkwargs):
"""Here, we overwrite the QNode execution wrapper in order
to take into account that classical processing may be present
inside the QNode."""
tkwargs.setdefault("device_wires", qnode.device.wires)
mt_fn = self.default_qnode_transform(qnode, targs, tkwargs)
return mt_fn
def _metric_tensor_cov_matrix(tape, argnum, diag_approx): # pylint: disable=too-many-statements
r"""This is the metric tensor method for the block diagonal, using
the covariance matrix of the generators of each layer.
Args:
tape (pennylane.QNode or .QuantumTape): quantum tape or QNode to find the metric tensor of
argnum (list[int]): Trainable tape-parameter indices with respect to which the metric tensor
is computed.
diag_approx (bool): if True, use the diagonal approximation. If ``False`` , a
block-diagonal approximation of the metric tensor is computed.
Returns:
list[pennylane.tape.QuantumTape]: Transformed tapes that compute the probabilities
required for the covariance matrix
callable: Post-processing function that computes the covariance matrix from the
results of the tapes in the first return value
list[list[.Observable]]: Observables measured in each tape, one inner list
corresponding to one tape in the first return value
list[list[float]]: Coefficients to scale the results for each observable, one inner list
corresponding to one tape in the first return value
list[list[bool]]: Each inner list corresponds to one tape and therefore also one parametrized
layer and its elements determine whether a trainable parameter in that layer is
contained in ``argnum``.
list[None, int]: Id list representing the layer for each parameter.
list[None, int]: Id list representing the observables for each parameter.
This method assumes the ``generator`` of all parametrized operations with respect to
which the tensor is computed to be Hermitian. This is the case for unitary single-parameter
operations.
"""
# get the circuit graph
graph = tape.graph
metric_tensor_tapes = []
obs_list = []
coeffs_list = []
params_list = []
in_argnum_list = []
layers_ids = []
obs_ids = []
i = 0
for queue, curr_ops, param_idx, _ in graph.iterate_parametrized_layers():
params_list.append(param_idx)
in_argnum_list.append([p in argnum for p in param_idx])
if not any(in_argnum_list[-1]):
layers_ids.extend([None] * len(in_argnum_list[-1]))
obs_ids.extend([None] * len(in_argnum_list[-1]))
# no tape needs to be created for this block
continue
layer_coeffs, layer_obs = [], []
# for each operation in the layer, get the generator
j = 0
for p, op in zip(param_idx, curr_ops):
layers_ids.append(i)
if p in argnum:
obs, s = qml.generator(op)
layer_obs.append(obs)
layer_coeffs.append(s)
obs_ids.append(j)
j = j + 1
else:
obs_ids.append(None)
i = i + 1
coeffs_list.append(layer_coeffs)
obs_list.append(layer_obs)
# Create a quantum tape with all operations
# prior to the parametrized layer, and the rotations
# to measure in the basis of the parametrized layer generators.
with qml.queuing.AnnotatedQueue() as layer_q:
for op in queue:
# TODO: Maybe there are gates that do not affect the
# generators of interest and thus need not be applied.
qml.apply(op)
for o, param_in_argnum in zip(layer_obs, in_argnum_list[-1]):
if param_in_argnum:
o.diagonalizing_gates()
qml.probs(wires=tape.wires)
layer_tape = qml.tape.QuantumScript.from_queue(layer_q)
metric_tensor_tapes.append(layer_tape)
def processing_fn(probs):
gs = []
probs_idx = 0
for params_in_argnum in in_argnum_list:
if not any(params_in_argnum):
# there is no tape and no probs associated to this layer
dim = len(params_in_argnum)
gs.append(qml.math.zeros((dim, dim)))
continue
coeffs = coeffs_list[probs_idx]
obs = obs_list[probs_idx]
p = probs[probs_idx]
scale = qml.math.convert_like(qml.math.outer(coeffs, coeffs), p)
scale = qml.math.cast_like(scale, p)
g = scale * qml.math.cov_matrix(p, obs, wires=tape.wires, diag_approx=diag_approx)
for i, in_argnum in enumerate(params_in_argnum):
# fill in rows and columns of zeros where a parameter was not in argnum
if not in_argnum:
dim = g.shape[0]
g = qml.math.concatenate((g[:i], qml.math.zeros((1, dim)), g[i:]))
g = qml.math.concatenate(
(g[:, :i], qml.math.zeros((dim + 1, 1)), g[:, i:]), axis=1
)
gs.append(g)
probs_idx += 1
# create the block diagonal metric tensor
return qml.math.block_diag(gs)
return (
metric_tensor_tapes,
processing_fn,
obs_list,
coeffs_list,
in_argnum_list,
layers_ids,
obs_ids,
)
@functools.lru_cache()
def _get_gen_op(op, allow_nonunitary, aux_wire):
r"""Get the controlled-generator operation for a given operation.
Args:
op (WrappedObj[Operation]): Wrapped Operation from which to extract the generator. The
Operation needs to be wrapped for hashability in order to use the lru-cache.
allow_nonunitary (bool): Whether non-unitary gates are allowed in the circuit
aux_wire (int or pennylane.wires.Wires): Auxiliary wire on which to control the operation
Returns
qml.Operation: Controlled-generator operation of the generator of ``op``, controlled
on wire ``aux_wire``.
Raises
ValueError: If the generator of ``op`` is not known or it is non-unitary while
``allow_nonunitary=False``.
If ``allow_nonunitary=True``, a general :class:`~.pennylane.ControlledQubitUnitary` is returned,
otherwise only controlled Pauli operations are used. If the operation has a non-unitary
generator but ``allow_nonunitary=False``, the operation ``op`` should have been decomposed
before, leading to a ``ValueError``.
"""
op_to_cgen = {
qml.RX: qml.CNOT,
qml.RY: qml.CY,
qml.RZ: qml.CZ,
qml.PhaseShift: qml.CZ, # PhaseShift is the same as RZ up to a global phase
}
op = op.obj
try:
cgen = op_to_cgen[op.__class__]
return cgen(wires=[aux_wire, *op.wires])
except KeyError as e:
if allow_nonunitary:
mat = qml.matrix(qml.generator(op)[0])
return qml.ControlledQubitUnitary(mat, control_wires=aux_wire, wires=op.wires)
raise ValueError(
f"Generator for operation {op} not known and non-unitary operations "
"deactivated via allow_nonunitary=False."
) from e
def _get_first_term_tapes(layer_i, layer_j, allow_nonunitary, aux_wire):
r"""Obtain the tapes for the first term of all tensor entries
belonging to an off-diagonal block.
Args:
layer_i (list): The first layer of parametrized ops, of the format of
the layers generated by ``iterate_parametrized_layers``
layer_j (list): The second layer of parametrized ops
allow_nonunitary (bool): Whether non-unitary operations are allowed
in the circuit; passed to ``_get_gen_op``
aux_wire (object or pennylane.wires.Wires): Auxiliary wire on which to
control the controlled-generator operations
Returns:
list[pennylane.tape.QuantumTape]: Transformed tapes that compute the
first term of the metric tensor for the off-diagonal block belonging
to the input layers
list[tuple[int]]: 2-tuple indices assigning the tapes to metric tensor
entries
"""
tapes = []
ids = []
# Exclude the backwards cone of layer_i from the backwards cone of layer_j
ops_between_cgens = [
op1 for op1 in layer_j.pre_ops if not any(op1 is op2 for op2 in layer_i.pre_ops)
]
# Iterate over differentiated operation in first layer
for diffed_op_i, par_idx_i in zip(layer_i.ops, layer_i.param_inds):
gen_op_i = _get_gen_op(WrappedObj(diffed_op_i), allow_nonunitary, aux_wire)
# Iterate over differentiated operation in second layer
# There will be one tape per pair of differentiated operations
for diffed_op_j, par_idx_j in zip(layer_j.ops, layer_j.param_inds):
gen_op_j = _get_gen_op(WrappedObj(diffed_op_j), allow_nonunitary, aux_wire)
with qml.queuing.AnnotatedQueue() as q:
# Initialize auxiliary wire
qml.Hadamard(wires=aux_wire)
# Apply backward cone of first layer
for op in layer_i.pre_ops:
qml.apply(op)
# Controlled-generator operation of first diff'ed op
qml.apply(gen_op_i)
# Apply first layer and operations between layers
for op in ops_between_cgens:
qml.apply(op)
# Controlled-generator operation of second diff'ed op
qml.apply(gen_op_j)
# Measure X on auxiliary wire
qml.expval(qml.X(aux_wire))
tapes.append(qml.tape.QuantumScript.from_queue(q))
# Memorize to which metric entry this tape belongs
ids.append((par_idx_i, par_idx_j))
return tapes, ids
def _metric_tensor_hadamard(
tape, argnum, allow_nonunitary, aux_wire, device_wires
): # pylint: disable=too-many-statements
r"""Generate the quantum tapes that execute the Hadamard tests
to compute the first term of off block-diagonal metric entries
and combine them with the covariance matrix-based block-diagonal tapes.
Args:
tape (pennylane.QNode or .QuantumTape): quantum tape or QNode to find the metric tensor of
argnum (list[int]): Trainable tape-parameter indices with respect to which the metric tensor
is computed.
allow_nonunitary (bool): Whether non-unitary operations are allowed in circuits
created by the transform. Only relevant if ``approx`` is ``None``
Should be set to ``True`` if possible to reduce cost.
aux_wire (int or .wires.Wires): Auxiliary wire to be used for
Hadamard tests. By default, a suitable wire is inferred from the number
of used wires in the original circuit.
device_wires (.wires.Wires): Wires of the device that is going to be used for the
metric tensor. Facilitates finding a default for ``aux_wire`` if ``aux_wire``
is ``None`` .
Returns:
list[pennylane.tape.QuantumTape]: Tapes to evaluate the metric tensor
callable: processing function to obtain the metric tensor from the tape results
"""
# Get tapes and processing function for the block-diagonal metric tensor,
# as well as the generator observables and generator coefficients for each diff'ed operation
(
diag_tapes,
diag_proc_fn,
obs_list,
coeffs_list,
in_argnum_list,
layer_ids,
obs_ids,
) = _metric_tensor_cov_matrix(tape, argnum, diag_approx=False)
# Obtain layers of parametrized operations and account for the discrepancy between trainable
# and non-trainable parameter indices
graph = tape.graph
par_idx_to_trainable_idx = {idx: i for i, idx in enumerate(sorted(tape.trainable_params))}
layers = []
for layer, in_argnum in zip(graph.iterate_parametrized_layers(), in_argnum_list):
if not any(in_argnum):
# no tapes need to be constructed for this layer
continue
pre_ops, ops, param_idx, post_ops = layer
new_ops = []
new_param_idx = []
for o, idx, param_in_argnum in zip(ops, param_idx, in_argnum):
if param_in_argnum:
new_ops.append(o)
new_param_idx.append(par_idx_to_trainable_idx[idx])
layers.append(LayerData(pre_ops, new_ops, new_param_idx, post_ops))
if len(layers) <= 1:
return diag_tapes, diag_proc_fn
# Get default for aux_wire
aux_wire = _get_aux_wire(aux_wire, tape, device_wires)
# Get all tapes for the first term of the metric tensor and memorize which
# entry they belong to
first_term_tapes = []
ids = []
block_sizes = []
for idx_i, layer_i in enumerate(layers):
block_sizes.append(len(layer_i.param_inds))
for layer_j in layers[idx_i + 1 :]:
_tapes, _ids = _get_first_term_tapes(layer_i, layer_j, allow_nonunitary, aux_wire)
first_term_tapes.extend(_tapes)
ids.extend(_ids)
# Combine block-diagonal and off block-diagonal tapes
tapes = diag_tapes + first_term_tapes
# prepare off block-diagonal mask
blocks = []
for in_argnum in in_argnum_list:
d = len(in_argnum)
blocks.append(qml.math.ones((d, d)))
mask = 1 - qml.math.block_diag(blocks)
# Required for slicing in processing_fn
num_diag_tapes = len(diag_tapes)
def processing_fn(results):
"""Postprocessing function for the full metric tensor."""
nonlocal mask
# Split results
diag_res, off_diag_res = results[:num_diag_tapes], results[num_diag_tapes:]
# Get full block-diagonal tensor
diag_mt = diag_proc_fn(diag_res)
# the off diag tapes only have a single expval measurement
off_diag_res = [qml.math.expand_dims(res, 0) for res in off_diag_res]
# Prepare the mask to match the used interface
mask = qml.math.convert_like(mask, diag_mt)
# Initialize off block-diagonal tensor using the stored ids
first_term = qml.math.zeros_like(diag_mt)
if ids:
off_diag_res = qml.math.stack(off_diag_res, 1)[0]
inv_ids = [_id[::-1] for _id in ids]
first_term = qml.math.scatter_element_add(first_term, list(zip(*ids)), off_diag_res)
first_term = qml.math.scatter_element_add(first_term, list(zip(*inv_ids)), off_diag_res)
# Second terms of off block-diagonal metric tensor
expvals = qml.math.zeros_like(first_term[0])
for i, (layer_i, obs_i) in enumerate(zip(layer_ids, obs_ids)):
if layer_i is not None and obs_i is not None:
prob = diag_res[layer_i]
o = obs_list[layer_i][obs_i]
l = qml.math.cast(o.eigvals(), dtype=np.float64)
w = tape.wires.indices(o.wires)
p = qml.math.marginal_prob(prob, w)
expvals = qml.math.scatter_element_add(expvals, (i,), qml.math.dot(l, p))
# Construct <\partial_i\psi|\psi><\psi|\partial_j\psi> and mask it
second_term = qml.math.tensordot(expvals, expvals, axes=0) * mask
# Subtract second term from first term
off_diag_mt = first_term - second_term
# Rescale first and second term
coeffs_gen = (c for c in qml.math.hstack(coeffs_list))
# flattened coeffs_list but also with 0s where parameters are not in argnum
interface = qml.math.get_interface(*results)
extended_coeffs_list = qml.math.asarray(
[
next(coeffs_gen) if param_in_argnum else 0.0
for param_in_argnum in qml.math.hstack(in_argnum_list)
],
like=interface,
)
scale = qml.math.tensordot(extended_coeffs_list, extended_coeffs_list, axes=0)
off_diag_mt = scale * off_diag_mt
# Combine block-diagonal and off block-diagonal
mt = off_diag_mt + diag_mt
return mt
return tapes, processing_fn
def _get_aux_wire(aux_wire, tape, device_wires):
r"""Determine an unused wire to be used as auxiliary wire for Hadamard tests.
Args:
aux_wire (object): Input auxiliary wire. May be one of a variety of input formats:
If ``None``, try to infer a reasonable choice based on the number of wires used
in the ``tape``, and based on ``device_wires``, if they are not ``None``.
If an ``int``, a ``str`` or a ``Sequence``, convert the input to a ``Wires``
object and take the first entry of the result. This leads to consistent behaviour
between ``_get_aux_wire`` and the ``Wires`` class.
If a ``Wires`` instance already, the conversion to such an instance is performed
trivially as well (also see the source code of ``~.Wires``).
tape (pennylane.tape.QuantumTape): Tape to infer the wire for
device_wires (.wires.Wires): Wires of the device that is going to be used for the
metric tensor. Facilitates finding a default for ``aux_wire`` if ``aux_wire``
is ``None`` .
Returns:
object: The auxiliary wire to be used. Equals ``aux_wire`` if it was not ``None``\ ,
and an often reasonable choice else.
"""
if aux_wire is not None:
aux_wire = qml.wires.Wires(aux_wire)[0]
if aux_wire in tape.wires:
msg = "The requested auxiliary wire is already in use by the circuit."
raise qml.wires.WireError(msg)
if device_wires is None or aux_wire in device_wires:
return aux_wire
raise qml.wires.WireError("The requested auxiliary wire does not exist on the used device.")
if device_wires is not None:
if len(device_wires) == len(tape.wires):
raise qml.wires.WireError("The device has no free wire for the auxiliary wire.")
unused_wires = qml.wires.Wires(device_wires.toset().difference(tape.wires.toset()))
return unused_wires[0]
_wires = tape.wires
for _aux in range(tape.num_wires):
if _aux not in _wires:
return _aux
return tape.num_wires
_modules/pennylane/gradients/metric_tensor
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