Source code for pennylane.gradients.parameter_shift_hessian
# 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.
"""
This module contains functions for computing the parameter-shift hessian
of a qubit-based quantum tape.
"""
import itertools as it
import warnings
from functools import partial
from string import ascii_letters as ABC
import numpy as np
import pennylane as qml
from pennylane.measurements import ProbabilityMP, StateMP, VarianceMP
from pennylane.tape import QuantumScript, QuantumScriptBatch
from pennylane.transforms import transform
from pennylane.typing import PostprocessingFn
from .general_shift_rules import (
_combine_shift_rules,
generate_multishifted_tapes,
generate_shifted_tapes,
)
from .gradient_transform import find_and_validate_gradient_methods
from .parameter_shift import _get_operation_recipe
def _process_jacs(jac, qhess):
"""
Combine the classical and quantum jacobians
"""
# Check for a Jacobian equal to the identity matrix.
if not qml.math.is_abstract(jac):
shape = qml.math.shape(jac)
is_square = len(shape) == 2 and shape[0] == shape[1]
if is_square and qml.math.allclose(jac, qml.numpy.eye(shape[0])):
return qhess if len(qhess) > 1 else qhess[0]
hess = []
for qh in qhess:
if not isinstance(qh, tuple) or not isinstance(qh[0], tuple):
# single parameter case
qh = qml.math.expand_dims(qh, [0, 1])
else:
# multi parameter case
qh = qml.math.stack([qml.math.stack(row) for row in qh])
jac_ndim = len(qml.math.shape(jac))
# The classical jacobian has shape (num_params, num_qnode_args)
# The quantum Hessian has shape (num_params, num_params, output_shape)
# contracting the quantum Hessian with the classical jacobian twice gives
# a result with shape (num_qnode_args, num_qnode_args, output_shape)
qh_indices = "ab..."
# contract the first axis of the jacobian with the first and second axes of the Hessian
first_jac_indices = f"a{ABC[2:2 + jac_ndim - 1]}"
second_jac_indices = f"b{ABC[2 + jac_ndim - 1:2 + 2 * jac_ndim - 2]}"
result_indices = f"{ABC[2:2 + 2 * jac_ndim - 2]}..."
qh = qml.math.einsum(
f"{qh_indices},{first_jac_indices},{second_jac_indices}->{result_indices}",
qh,
jac,
jac,
)
hess.append(qh)
return tuple(hess) if len(hess) > 1 else hess[0]
def _process_argnum(argnum, tape):
"""Process the argnum keyword argument to ``param_shift_hessian`` from any of ``None``,
``int``, ``Sequence[int]``, ``array_like[bool]`` to an ``array_like[bool]``."""
_trainability_note = (
"This may be caused by attempting to differentiate with respect to parameters "
"that are not marked as trainable."
)
if argnum is None:
# All trainable tape parameters are considered
argnum = list(range(tape.num_params))
elif isinstance(argnum, int):
if argnum >= tape.num_params:
raise ValueError(
f"The index {argnum} exceeds the number of trainable tape parameters "
f"({tape.num_params}). " + _trainability_note
)
# Make single marked parameter an iterable
argnum = [argnum]
if len(qml.math.shape(argnum)) == 1:
# If the iterable is 1D, consider all combinations of all marked parameters
if not qml.math.array(argnum).dtype == bool:
# If the 1D iterable contains indices, make sure it contains valid indices...
if qml.math.max(argnum) >= tape.num_params:
raise ValueError(
f"The index {qml.math.max(argnum)} exceeds the number of "
f"trainable tape parameters ({tape.num_params})." + _trainability_note
)
# ...and translate it to Boolean 1D iterable
argnum = [i in argnum for i in range(tape.num_params)]
elif len(argnum) != tape.num_params:
# If the 1D iterable already is Boolean, check its length
raise ValueError(
"One-dimensional Boolean array argnum is expected to have as many entries as the "
f"tape has trainable parameters ({tape.num_params}), but got {len(argnum)}."
+ _trainability_note
)
# Finally mark all combinations using the outer product
argnum = qml.math.tensordot(argnum, argnum, axes=0)
elif not (
qml.math.shape(argnum) == (tape.num_params,) * 2
and qml.math.array(argnum).dtype == bool
and qml.math.allclose(qml.math.transpose(argnum), argnum)
):
# If the iterable is 2D, make sure it is Boolean, symmetric and of the correct size
raise ValueError(
f"Expected a symmetric 2D Boolean array with shape {(tape.num_params,) * 2} "
f"for argnum, but received {argnum}." + _trainability_note
)
return argnum
def _collect_recipes(tape, argnum, method_map, diagonal_shifts, off_diagonal_shifts):
r"""Extract second order recipes for the tape operations for the diagonal of the Hessian
as well as the first-order derivative recipes for the off-diagonal entries.
"""
diag_argnum = qml.math.diag(argnum)
offdiag_argnum = qml.math.any(argnum ^ qml.math.diag(qml.math.diag(argnum)), axis=0)
diag_recipes = []
partial_offdiag_recipes = []
diag_shifts_idx = offdiag_shifts_idx = 0
for i, (d, od) in enumerate(zip(diag_argnum, offdiag_argnum)):
if not d or method_map[i] == "0":
# hessian will be set to 0 for this row/column
diag_recipes.append(None)
else:
# Get the diagonal second-order derivative recipe
diag_shifts = None if diagonal_shifts is None else diagonal_shifts[diag_shifts_idx]
diag_recipes.append(_get_operation_recipe(tape, i, diag_shifts, order=2))
diag_shifts_idx += 1
if not od or method_map[i] == "0":
# hessian will be set to 0 for this row/column
partial_offdiag_recipes.append((None, None, None))
else:
# Create the first-order gradient recipes per parameter for off-diagonal entries
offdiag_shifts = (
None if off_diagonal_shifts is None else off_diagonal_shifts[offdiag_shifts_idx]
)
partial_offdiag_recipes.append(_get_operation_recipe(tape, i, offdiag_shifts, order=1))
offdiag_shifts_idx += 1
return diag_recipes, partial_offdiag_recipes
def _generate_offdiag_tapes(tape, idx, first_order_recipes, add_unshifted, tapes, coeffs):
r"""Combine two univariate first order recipes and create
multi-shifted tapes to compute the off-diagonal entry of the Hessian."""
# pylint: disable=too-many-arguments
recipe_i = first_order_recipes[idx[0]]
recipe_j = first_order_recipes[idx[1]]
# The columns of combined_rules contain the coefficients (1), the multipliers (2) and the
# shifts (2) in that order, with the number in brackets indicating the number of columns
combined_rules = _combine_shift_rules([recipe_i, recipe_j])
# If there are unmultiplied, unshifted tapes, the coefficient is memorized and the term
# removed from the list of tapes to create
if np.allclose(combined_rules[0, 1:3], 1.0) and np.allclose(combined_rules[0, 3:5], 0.0):
# Extract the unshifted coefficient, if the first shifts (multipliers) equal 0 (1).
if add_unshifted:
# Add the unshifted tape if it has not been added yet and is required
# because f0 was not provided (both captured by add_unshifted).
tapes.insert(0, tape)
add_unshifted = False
unshifted_coeff = combined_rules[0, 0]
combined_rules = combined_rules[1:]
else:
unshifted_coeff = None
s = combined_rules[:, 3:5]
m = combined_rules[:, 1:3]
new_tapes = generate_multishifted_tapes(tape, idx, s, m)
tapes.extend(new_tapes)
coeffs.append(combined_rules[:, 0])
return add_unshifted, unshifted_coeff
def _generate_diag_tapes(tape, idx, diag_recipes, add_unshifted, tapes, coeffs):
"""Create the required parameter-shifted tapes for a single diagonal entry of
the Hessian using precomputed second-order shift rules."""
# pylint: disable=too-many-arguments
# Obtain the recipe for the diagonal.
c, m, s = diag_recipes[idx].T
if s[0] == 0 and m[0] == 1.0:
# Extract the unshifted coefficient, if the first shift (multiplier) equals 0 (1).
if add_unshifted:
# Add the unshifted tape if it has not been added yet and is required
# because f0 was not provided (both captured by add_unshifted).
tapes.insert(0, tape)
add_unshifted = False
unshifted_coeff = c[0]
c, m, s = c[1:], m[1:], s[1:]
else:
unshifted_coeff = None
# Create the shifted tapes for the diagonal entry and store them along with coefficients
new_tapes = generate_shifted_tapes(tape, idx, s, m)
tapes.extend(new_tapes)
coeffs.append(c)
return add_unshifted, unshifted_coeff
_no_trainable_hessian_warning = (
"Attempted to compute the Hessian 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."
)
def _no_trainable_hessian(tape):
warnings.warn(_no_trainable_hessian_warning)
if len(tape.measurements) == 1:
return [], lambda _: qml.math.zeros((0,))
return [], lambda _: tuple(qml.math.zeros((0,)) for _ in tape.measurements)
def _all_zero_hessian(tape):
num_params = len(tape.trainable_params)
zeros_list = []
for m in tape.measurements:
shape = 2 ** len(m.wires) if isinstance(m, ProbabilityMP) else ()
zeros = tuple(
tuple(qml.math.zeros(shape) for _ in range(num_params)) for _ in range(num_params)
)
if num_params == 1:
zeros = zeros[0][0]
zeros_list.append(zeros)
if len(tape.measurements) == 1:
return [], lambda _: zeros_list[0]
return [], lambda _: tuple(zeros_list)
def expval_hessian_param_shift(tape, argnum, method_map, diagonal_shifts, off_diagonal_shifts, f0):
r"""Generate the Hessian tapes that are used in the computation of the second derivative of a
quantum tape, using analytical parameter-shift rules to do so exactly. Also define a
post-processing function to combine the results of evaluating the tapes into the Hessian.
Args:
tape (.QuantumTape): quantum tape to differentiate
argnum (array_like[bool]): Parameter indices to differentiate
with respect to, in form of a two-dimensional boolean ``array_like`` mask.
method_map (dict[int, string]): The differentiation method to use for each trainable
parameter. Can be "A" or "0", where "A" is the analytical parameter shift rule
and "0" indicates a 0 derivative (the parameter does not affect the tape's output).
diagonal_shifts (list[tuple[int or float]]): List containing tuples of shift values
for the Hessian diagonal.
If provided, one tuple of shifts should be given per trainable parameter
and the tuple length should match the number of frequencies for that parameter.
If unspecified, equidistant shifts are used.
off_diagonal_shifts (list[tuple[int or float]]): List containing tuples of shift
values for the off-diagonal entries of the Hessian.
If provided, one tuple of shifts should be given per trainable parameter
and the tuple should match the number of frequencies for that parameter.
The combination of shifts into bivariate shifts is performed automatically.
If unspecified, equidistant shifts are used.
f0 (tensor_like[float] or None): Output of the evaluated input tape. If provided,
and the Hessian tapes include the original input tape, the 'f0' value is used
instead of evaluating the input tape, reducing the number of device invocations.
Returns:
tuple[list[QuantumTape], function]: A tuple containing a
list of generated tapes, together with a post-processing
function to be applied to the results of the evaluated tapes
in order to obtain the Hessian matrix.
"""
# pylint: disable=too-many-arguments, too-many-statements
h_dim = tape.num_params
unshifted_coeffs = {}
# Marks whether we will need to add the unshifted tape to all Hessian tapes.
add_unshifted = f0 is None
# Assemble all univariate recipes for the diagonal and as partial components for the
# off-diagonal entries.
diag_recipes, partial_offdiag_recipes = _collect_recipes(
tape, argnum, method_map, diagonal_shifts, off_diagonal_shifts
)
hessian_tapes = []
hessian_coeffs = []
for i, j in it.combinations_with_replacement(range(h_dim), r=2):
if not argnum[i, j]:
# The (i, j) entry of the Hessian is not to be computed
hessian_coeffs.append(None)
continue
if i == j:
add_unshifted, unshifted_coeffs[(i, i)] = _generate_diag_tapes(
tape, i, diag_recipes, add_unshifted, hessian_tapes, hessian_coeffs
)
else:
# Create tapes and coefficients for the off-diagonal entry by combining
# the two univariate first-order derivative recipes.
add_unshifted, unshifted_coeffs[(i, j)] = _generate_offdiag_tapes(
tape, (i, j), partial_offdiag_recipes, add_unshifted, hessian_tapes, hessian_coeffs
)
unshifted_coeffs = {key: val for key, val in unshifted_coeffs.items() if val is not None}
def processing_fn(results):
num_measurements = len(tape.measurements)
if num_measurements == 1:
results = tuple((r,) for r in results)
# the hessian should have a nested tuple structure with shape
# (num_measurements, num_params, num_params, *output_dims)
# first accumulate all elements of the hessian into a list
hessians = []
# Keep track of tape results already consumed. Start with 1 if the unshifted tape was
# included in the tapes for the Hessian.
start = 1 if unshifted_coeffs and f0 is None else 0
# Results of the unshifted tape.
r0 = results[0] if start == 1 else f0
for i, j in it.product(range(h_dim), repeat=2):
if j < i:
hessians.append(hessians[j * h_dim + i])
continue
k = i * h_dim + j - i * (i + 1) // 2
coeffs = hessian_coeffs[k]
if coeffs is None or len(coeffs) == 0:
hessian = []
for m in range(num_measurements):
hessian.append(qml.math.zeros_like(results[0][m]))
hessians.append(tuple(hessian))
continue
res = results[start : start + len(coeffs)]
start = start + len(coeffs)
unshifted_coeff = unshifted_coeffs.get((i, j), None)
hessian = []
for m in range(num_measurements):
# the res array has shape (num_tapes, num_measurements, *output_dims)
# first collect all tape results for the individual measurements
measure_res = qml.math.stack([r[m] for r in res])
# then compute the hessian via parameter-shift
coeffs = qml.math.convert_like(coeffs, measure_res)
hess = qml.math.tensordot(measure_res, coeffs, [[0], [0]])
if unshifted_coeff is not None:
hess = hess + unshifted_coeff * r0[m]
hess = qml.math.array(hess, like=measure_res)
hessian.append(hess)
hessians.append(tuple(hessian))
# at this point, the hessian has shape (num_params ** 2, num_measurements, *output_dims)
# swap the first two axes, so that the hessian now has
# shape (num_measurements, num_params ** 2, *output_dims)
hessians = tuple(tuple(h[i] for h in hessians) for i in range(num_measurements))
# replace the axis of size num_params ** 2 with two axes of size num_params;
# that is, reshape the hessian to have shape (num_measurements, num_params, num_params, *output_dims)
hessians = tuple(
tuple(tuple(hess[i * h_dim + j] for j in range(h_dim)) for i in range(h_dim))
for hess in hessians
)
# squeeze every axis with size 1
if h_dim == 1:
hessians = tuple(hess[0][0] for hess in hessians)
if num_measurements == 1:
hessians = hessians[0]
return hessians
return hessian_tapes, processing_fn
# pylint: disable=too-many-return-statements,too-many-branches
def _contract_qjac_with_cjac(qhess, cjac, tape):
"""Contract a quantum Jacobian with a classical preprocessing Jacobian."""
if len(tape.measurements) > 1:
qhess = qhess[0]
has_single_arg = False
if not isinstance(cjac, tuple):
has_single_arg = True
cjac = (cjac,)
# The classical Jacobian for each argument has shape:
# (# gate_args, *qnode_arg_shape)
# The Jacobian needs to be contracted twice with the quantum Hessian of shape:
# (*qnode_output_shape, # gate_args, # gate_args)
# The result should then have the shape:
# (*qnode_output_shape, *qnode_arg_shape, *qnode_arg_shape)
hessians = []
for jac in cjac:
if jac is not None:
hess = _process_jacs(jac, qhess)
hessians.append(hess)
return hessians[0] if has_single_arg else tuple(hessians)
[docs]@partial(transform, classical_cotransform=_contract_qjac_with_cjac, final_transform=True)
def param_shift_hessian(
tape: QuantumScript,
argnum=None,
diagonal_shifts=None,
off_diagonal_shifts=None,
f0=None,
) -> tuple[QuantumScriptBatch, PostprocessingFn]:
r"""Transform a circuit to compute the parameter-shift Hessian with respect to its trainable
parameters. This is the Hessian transform to replace the old one in the new return types system
Use this transform to explicitly generate and explore parameter-shift circuits for computing
the Hessian of QNodes directly, without computing first derivatives.
For second-order derivatives of more complicated cost functions, please consider using your
chosen autodifferentiation framework directly, by chaining gradient computations:
>>> qml.jacobian(qml.grad(cost))(weights)
Args:
tape (QNode or QuantumTape): quantum circuit to differentiate
argnum (int or list[int] or array_like[bool] or None): Parameter indices to differentiate
with respect to. If not provided, the Hessian with respect to all
trainable indices is returned. Note that the indices refer to tape
parameters both if ``tape`` is a tape, and if it is a QNode. If an ``array_like``
is provided, it is expected to be a symmetric two-dimensional Boolean mask with
shape ``(n, n)`` where ``n`` is the number of trainable tape parameters.
diagonal_shifts (list[tuple[int or float]]): List containing tuples of shift values
for the Hessian diagonal. The shifts are understood as first-order derivative
shifts and are iterated to obtain the second-order derivative.
If provided, one tuple of shifts should be given per trainable parameter
and the tuple length should match the number of frequencies for that parameter.
If unspecified, equidistant shifts are used.
off_diagonal_shifts (list[tuple[int or float]]): List containing tuples of shift
values for the off-diagonal entries of the Hessian.
If provided, one tuple of shifts should be given per trainable parameter
and the tuple should match the number of frequencies for that parameter.
The combination of shifts into bivariate shifts is performed automatically.
If unspecified, equidistant shifts are used.
f0 (tensor_like[float] or None): Output of the evaluated input tape. If provided,
and the Hessian tapes include the original input tape, the 'f0' value is used
instead of evaluating the input tape, reducing the number of device invocations.
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 Hessian in the form of a tensor, a tuple, or a nested tuple depending upon the number
of trainable QNode arguments, the output shape(s) of the input QNode itself, and the usage of shot vectors
in the QNode execution.
Note: By default a QNode with the keyword ``hybrid=True`` computes derivates with respect to
QNode arguments, which can include classical computations on those arguments before they are
passed to quantum operations. The "purely quantum" Hessian can instead be obtained with
``hybrid=False``, which is then computed with respect to the gate arguments and produces a
result of shape ``(*QNode output dimensions, # gate arguments, # gate arguments)``.
**Example**
Applying the Hessian transform to a QNode computes its Hessian tensor.
This works best if no classical processing is applied within the
QNode to operation parameters.
>>> dev = qml.device("default.qubit")
>>> @qml.qnode(dev)
... def circuit(x):
... qml.RX(x[0], wires=0)
... qml.CRY(x[1], wires=[0, 1])
... return qml.expval(qml.Z(0) @ qml.Z(1))
>>> x = np.array([0.5, 0.2], requires_grad=True)
>>> qml.gradients.param_shift_hessian(circuit)(x)
((array(-0.86883595), array(0.04762358)),
(array(0.04762358), array(0.05998862)))
.. details::
:title: Usage Details
The Hessian transform can also be applied to a quantum tape instead of a QNode, producing
the parameter-shifted tapes and a post-processing function to combine the execution
results of these tapes into the Hessian:
>>> circuit(x) # generate the QuantumTape inside the QNode
>>> tape = circuit.qtape
>>> hessian_tapes, postproc_fn = qml.gradients.param_shift_hessian(tape)
>>> len(hessian_tapes)
13
>>> all(isinstance(tape, qml.tape.QuantumTape) for tape in hessian_tapes)
True
>>> postproc_fn(qml.execute(hessian_tapes, dev, None))
((array(-0.86883595), array(0.04762358)),
(array(0.04762358), array(0.05998862)))
The Hessian tapes can be inspected via their draw function, which reveals the different
gate arguments generated from parameter-shift rules (we only draw the first four out of
all 13 tapes here):
>>> for h_tape in hessian_tapes[0:4]:
... print(qml.drawer.tape_text(h_tape, decimals=1))
0: ──RX(0.5)─╭●───────┤ ╭<Z@Z>
1: ──────────╰RY(0.2)─┤ ╰<Z@Z>
0: ──RX(-2.6)─╭●───────┤ ╭<Z@Z>
1: ───────────╰RY(0.2)─┤ ╰<Z@Z>
0: ──RX(2.1)─╭●───────┤ ╭<Z@Z>
1: ──────────╰RY(1.8)─┤ ╰<Z@Z>
0: ──RX(2.1)─╭●────────┤ ╭<Z@Z>
1: ──────────╰RY(-1.4)─┤ ╰<Z@Z>
To enable more detailed control over the parameter shifts, shift values can be provided
per parameter, and separately for the diagonal and the off-diagonal terms.
Here we choose them based on the parameters ``x`` themselves, mostly yielding multiples of
the original parameters in the shifted tapes.
>>> diag_shifts = [(x[0] / 2,), (x[1] / 2, x[1])]
>>> offdiag_shifts = [(x[0],), (x[1], 2 * x[1])]
>>> hessian_tapes, postproc_fn = qml.gradients.param_shift_hessian(
... tape, diagonal_shifts=diag_shifts, off_diagonal_shifts=offdiag_shifts
... )
>>> for h_tape in hessian_tapes[0:4]:
... print(qml.drawer.tape_text(h_tape, decimals=1))
0: ──RX(0.5)─╭●───────┤ ╭<Z@Z>
1: ──────────╰RY(0.2)─┤ ╰<Z@Z>
0: ──RX(0.0)─╭●───────┤ ╭<Z@Z>
1: ──────────╰RY(0.2)─┤ ╰<Z@Z>
0: ──RX(1.0)─╭●───────┤ ╭<Z@Z>
1: ──────────╰RY(0.2)─┤ ╰<Z@Z>
0: ──RX(1.0)─╭●───────┤ ╭<Z@Z>
1: ──────────╰RY(0.4)─┤ ╰<Z@Z>
.. note::
Note that the ``diagonal_shifts`` are interpreted as *first-order* derivative
shift values. That means they are used to generate a first-order derivative
recipe, which then is iterated in order to obtain the second-order derivative
for the diagonal Hessian entry. Explicit control over the used second-order
shifts is not implemented.
Finally, the ``argnum`` argument can be used to compute the Hessian only for some of the
variational parameters. Note that this indexing refers to trainable tape parameters both
if ``tape`` is a ``QNode`` and if it is a ``QuantumTape``.
>>> hessian_tapes, postproc_fn = qml.gradients.param_shift_hessian(tape, argnum=(1,))
>>> postproc_fn(qml.execute(hessian_tapes, dev, None))
((tensor(0., requires_grad=True), tensor(0., requires_grad=True)),
(tensor(0., requires_grad=True), array(0.05998862)))
"""
# Perform input validation before generating tapes.
if any(isinstance(m, StateMP) for m in tape.measurements):
raise ValueError(
"Computing the Hessian of circuits that return the state is not supported."
)
# The parameter-shift Hessian implementation currently doesn't support variance measurements.
# TODO: Support variances similar to how param_shift does it
if any(isinstance(m, VarianceMP) for m in tape.measurements):
raise ValueError(
"Computing the Hessian of circuits that return variances is currently not supported."
)
if argnum is None and not tape.trainable_params:
return _no_trainable_hessian(tape)
bool_argnum = _process_argnum(argnum, tape)
compare_diag_to = qml.math.sum(qml.math.diag(bool_argnum))
offdiag = bool_argnum ^ qml.math.diag(qml.math.diag(bool_argnum))
compare_offdiag_to = qml.math.sum(qml.math.any(offdiag, axis=0))
if diagonal_shifts is not None and len(diagonal_shifts) != compare_diag_to:
raise ValueError(
"The number of provided sets of shift values for diagonal entries "
f"({len(diagonal_shifts)}) does not match the number of marked arguments "
f"to compute the diagonal for ({compare_diag_to})."
)
if off_diagonal_shifts is not None and len(off_diagonal_shifts) != compare_offdiag_to:
raise ValueError(
"The number of provided sets of shift values for off-diagonal entries "
f"({len(off_diagonal_shifts)}) does not match the number of marked arguments "
f"for which to compute at least one off-diagonal entry ({compare_offdiag_to})."
)
# If argnum is given, the grad_method_validation may allow parameters with
# finite-difference as method. If they are among the requested argnum, we catch this
# further below (as no fallback function in analogy to `param_shift` is used currently).
method = "analytic" if argnum is None else "best"
trainable_params = qml.math.where(qml.math.any(bool_argnum, axis=0))[0]
diff_methods = find_and_validate_gradient_methods(tape, method, list(trainable_params))
for i, g in diff_methods.items():
if g == "0":
bool_argnum[i] = bool_argnum[:, i] = False
if qml.math.all(~bool_argnum): # pylint: disable=invalid-unary-operand-type
return _all_zero_hessian(tape)
# If any of these argument indices correspond to a finite difference
# derivative (diff_methods[idx]="F"), raise an error.
unsupported_params = {i for i, m in diff_methods.items() if m == "F"}
if unsupported_params:
raise ValueError(
"The parameter-shift Hessian currently does not support the operations "
f"for parameter(s) {unsupported_params}."
)
return expval_hessian_param_shift(
tape, bool_argnum, diff_methods, diagonal_shifts, off_diagonal_shifts, f0
)
_modules/pennylane/gradients/parameter_shift_hessian
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