Source code for pennylane.transforms.hamiltonian_expand
# 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 hamiltonian expand tape transform
"""
# pylint: disable=protected-access
from typing import List, Sequence, Callable
import pennylane as qml
from pennylane.measurements import ExpectationMP, MeasurementProcess
from pennylane.ops import SProd, Sum
from pennylane.tape import QuantumScript, QuantumTape
from pennylane.transforms import transform
[docs]@transform
def hamiltonian_expand(tape: QuantumTape, group: bool = True) -> (Sequence[QuantumTape], Callable):
r"""
Splits a tape measuring a Hamiltonian expectation into mutliple tapes of Pauli expectations,
and provides a function to recombine the results.
Args:
tape (QNode or QuantumTape or Callable): the quantum circuit used when calculating the
expectation value of the Hamiltonian
group (bool): Whether to compute disjoint groups of commuting Pauli observables, leading to fewer tapes.
If grouping information can be found in the Hamiltonian, it will be used even if group=False.
Returns:
qnode (QNode) or tuple[List[QuantumTape], function]: The transformed circuit as described in :func:`qml.transform <pennylane.transform>`.
**Example**
Given a Hamiltonian,
.. code-block:: python3
H = qml.Y(2) @ qml.Z(1) + 0.5 * qml.Z(2) + qml.Z(1)
and a tape of the form,
.. code-block:: python3
ops = [qml.Hadamard(0), qml.CNOT((0,1)), qml.X(2)]
tape = qml.tape.QuantumTape(ops, [qml.expval(H)])
We can use the ``hamiltonian_expand`` transform to generate new tapes and a classical
post-processing function for computing the expectation value of the Hamiltonian.
>>> tapes, fn = qml.transforms.hamiltonian_expand(tape)
We can evaluate these tapes on a device:
>>> dev = qml.device("default.qubit", wires=3)
>>> res = dev.execute(tapes)
Applying the processing function results in the expectation value of the Hamiltonian:
>>> fn(res)
array(-0.5)
Fewer tapes can be constructed by grouping commuting observables. This can be achieved
by the ``group`` keyword argument:
.. code-block:: python3
H = qml.Hamiltonian([1., 2., 3.], [qml.Z(0), qml.X(1), qml.X(0)])
tape = qml.tape.QuantumTape(ops, [qml.expval(H)])
With grouping, the Hamiltonian gets split into two groups of observables (here ``[qml.Z(0)]`` and
``[qml.X(1), qml.X(0)]``):
>>> tapes, fn = qml.transforms.hamiltonian_expand(tape)
>>> len(tapes)
2
Without grouping it gets split into three groups (``[qml.Z(0)]``, ``[qml.X(1)]`` and ``[qml.X(0)]``):
>>> tapes, fn = qml.transforms.hamiltonian_expand(tape, group=False)
>>> len(tapes)
3
Alternatively, if the Hamiltonian has already computed groups, they are used even if ``group=False``:
.. code-block:: python3
obs = [qml.Z(0), qml.X(1), qml.X(0)]
coeffs = [1., 2., 3.]
H = qml.Hamiltonian(coeffs, obs, grouping_type='qwc')
# the initialisation already computes grouping information and stores it in the Hamiltonian
assert H.grouping_indices is not None
tape = qml.tape.QuantumTape(ops, [qml.expval(H)])
Grouping information has been used to reduce the number of tapes from 3 to 2:
>>> tapes, fn = qml.transforms.hamiltonian_expand(tape, group=False)
>>> len(tapes)
2
"""
if (
len(tape.measurements) != 1
or not isinstance(hamiltonian := tape.measurements[0].obs, qml.Hamiltonian)
or not isinstance(tape.measurements[0], ExpectationMP)
):
raise ValueError(
"Passed tape must end in `qml.expval(H)`, where H is of type `qml.Hamiltonian`"
)
if qml.math.shape(hamiltonian.coeffs) == (0,) and qml.math.shape(hamiltonian.ops) == (0,):
raise ValueError(
"The Hamiltonian in the tape has no terms defined - cannot perform the Hamiltonian expansion."
)
# note: for backward passes of some frameworks
# it is crucial to use the hamiltonian.data attribute,
# and not hamiltonian.coeffs when recombining the results
if group or hamiltonian.grouping_indices is not None:
if hamiltonian.grouping_indices is None:
# explicitly selected grouping, but indices not yet computed
hamiltonian.compute_grouping()
coeff_groupings = [
qml.math.stack([hamiltonian.data[i] for i in indices])
for indices in hamiltonian.grouping_indices
]
obs_groupings = [
[hamiltonian.ops[i] for i in indices] for indices in hamiltonian.grouping_indices
]
# make one tape per grouping, measuring the
# observables in that grouping
tapes = []
for obs in obs_groupings:
new_tape = tape.__class__(
tape.operations, (qml.expval(o) for o in obs), shots=tape.shots
)
new_tape = new_tape.expand(stop_at=lambda obj: True)
tapes.append(new_tape)
def processing_fn(res_groupings):
# pylint: disable=no-member
res_groupings = [
qml.math.stack(r) if isinstance(r, (tuple, qml.numpy.builtins.SequenceBox)) else r
for r in res_groupings
]
res_groupings = [
qml.math.reshape(r, (1,)) if r.shape == () else r for r in res_groupings
]
dot_products = []
for c_group, r_group in zip(coeff_groupings, res_groupings):
if tape.batch_size:
r_group = r_group.T
if len(c_group) == 1 and len(r_group) != 1:
dot_products.append(r_group * c_group)
else:
dot_products.append(qml.math.dot(r_group, c_group))
summed_dot_products = qml.math.sum(qml.math.stack(dot_products), axis=0)
return qml.math.convert_like(summed_dot_products, res_groupings[0])
return tapes, processing_fn
coeffs = hamiltonian.data
# make one tape per observable
tapes = []
for o in hamiltonian.ops:
# pylint: disable=protected-access
new_tape = tape.__class__(tape.operations, [qml.expval(o)], shots=tape.shots)
tapes.append(new_tape)
# pylint: disable=function-redefined
def processing_fn(res):
dot_products = []
for c, r in zip(coeffs, res):
if qml.math.ndim(c) == 0 and qml.math.size(r) != 1:
dot_products.append(qml.math.squeeze(r) * c)
else:
dot_products.append(qml.math.dot(qml.math.squeeze(r), c))
summed_dot_products = qml.math.sum(qml.math.stack(dot_products), axis=0)
return qml.math.convert_like(summed_dot_products, res[0])
return tapes, processing_fn
# pylint: disable=too-many-branches, too-many-statements
[docs]@transform
def sum_expand(tape: QuantumTape, group: bool = True) -> (Sequence[QuantumTape], Callable):
"""Splits a quantum tape measuring a Sum expectation into multiple tapes of summand
expectations, and provides a function to recombine the results.
Args:
tape (.QuantumTape): the quantum tape used when calculating the expectation value
of the Hamiltonian
group (bool): Whether to compute disjoint groups of Pauli observables acting on different
wires, leading to fewer tapes.
Returns:
tuple[Sequence[.QuantumTape], Callable]: Returns a tuple containing a list of
quantum tapes to be evaluated, and a function to be applied to these
tape executions to compute the expectation value.
**Example**
Given a Sum operator,
.. code-block:: python3
S = qml.sum(qml.prod(qml.Y(2), qml.Z(1)), qml.s_prod(0.5, qml.Z(2)), qml.Z(1))
and a tape of the form,
.. code-block:: python3
ops = [qml.Hadamard(0), qml.CNOT((0,1)), qml.X(2)]
measurements = [
qml.expval(S),
qml.expval(qml.Z(0)),
qml.expval(qml.X(1)),
qml.expval(qml.Z(2))
]
tape = qml.tape.QuantumTape(ops, measurements)
We can use the ``sum_expand`` transform to generate new tapes and a classical
post-processing function to speed-up the computation of the expectation value of the `Sum`.
>>> tapes, fn = qml.transforms.sum_expand(tape, group=False)
>>> for tape in tapes:
... print(tape.measurements)
[expval(Y(2) @ Z(1))]
[expval(Z(2))]
[expval(Z(1))]
[expval(Z(0))]
[expval(X(1))]
Five tapes are generated: the first three contain the summands of the `Sum` operator,
and the last two contain the remaining observables. Note that the scalars of the scalar products
have been removed. In the processing function, these values will be multiplied by the result obtained
from executing the tapes.
Additionally, the observable expval(Z(2)) occurs twice in the original tape, but only once
in the transformed tapes. When there are multipe identical measurements in the circuit, the measurement
is performed once and the outcome is copied when obtaining the final result. This will also be resolved
when the processing function is applied.
We can evaluate these tapes on a device:
>>> dev = qml.device("default.qubit", wires=3)
>>> res = dev.execute(tapes)
Applying the processing function results in the expectation value of the Hamiltonian:
>>> fn(res)
[-0.5, 0.0, 0.0, -0.9999999999999996]
Fewer tapes can be constructed by grouping observables acting on different wires. This can be achieved
by the ``group`` keyword argument:
.. code-block:: python3
S = qml.sum(qml.Z(0), qml.s_prod(2, qml.X(1)), qml.s_prod(3, qml.X(0)))
ops = [qml.Hadamard(0), qml.CNOT((0,1)), qml.X(2)]
tape = qml.tape.QuantumTape(ops, [qml.expval(S)])
With grouping, the Sum gets split into two groups of observables (here
``[qml.Z(0), qml.s_prod(2, qml.X(1))]`` and ``[qml.s_prod(3, qml.X(0))]``):
>>> tapes, fn = qml.transforms.sum_expand(tape, group=True)
>>> for tape in tapes:
... print(tape.measurements)
[expval(Z(0)), expval(X(1))]
[expval(X(0))]
"""
# Populate these 2 dictionaries with the unique measurement objects, the index of the
# initial measurement on the tape and the coefficient
# NOTE: expval(Sum) is expanded into the expectation of each summand
# NOTE: expval(SProd) is transformed into expval(SProd.base) and the coeff is updated
measurements_dict = {} # {m_hash: measurement}
idxs_coeffs_dict = {} # {m_hash: [(location_idx, coeff)]}
for idx, m in enumerate(tape.measurements):
obs = m.obs
if isinstance(obs, Sum) and isinstance(m, ExpectationMP):
for summand in obs.operands:
coeff = 1
if isinstance(summand, SProd):
coeff = summand.scalar
summand = summand.base
s_m = qml.expval(summand)
if s_m.hash not in measurements_dict:
measurements_dict[s_m.hash] = s_m
idxs_coeffs_dict[s_m.hash] = [(idx, coeff)]
else:
idxs_coeffs_dict[s_m.hash].append((idx, coeff))
continue
coeff = 1 if isinstance(m, ExpectationMP) else None
if isinstance(obs, SProd) and isinstance(m, ExpectationMP):
coeff = obs.scalar
m = qml.expval(obs.base)
if m.hash not in measurements_dict:
measurements_dict[m.hash] = m
idxs_coeffs_dict[m.hash] = [(idx, coeff)]
else:
idxs_coeffs_dict[m.hash].append((idx, coeff))
# Cast the dictionaries into lists (we don't need the hashed anymore)
measurements = list(measurements_dict.values())
idxs_coeffs = list(idxs_coeffs_dict.values())
# Create the tapes, group observables if group==True
# pylint: disable=too-many-nested-blocks
if group:
m_groups = _group_measurements(measurements)
# Update ``idxs_coeffs`` list such that it tracks the new ``m_groups`` list of lists
tmp_idxs = []
for m_group in m_groups:
if len(m_group) == 1:
# pylint: disable=undefined-loop-variable
for i, m in enumerate(measurements):
if m is m_group[0]:
break
tmp_idxs.append(idxs_coeffs[i])
else:
inds = []
for mp in m_group:
# pylint: disable=undefined-loop-variable
for i, m in enumerate(measurements):
if m is mp:
break
inds.append(idxs_coeffs[i])
tmp_idxs.append(inds)
idxs_coeffs = tmp_idxs
qscripts = [
QuantumScript(ops=tape.operations, measurements=m_group, shots=tape.shots)
for m_group in m_groups
]
else:
qscripts = [
QuantumScript(ops=tape.operations, measurements=[m], shots=tape.shots)
for m in measurements
]
def processing_fn(expanded_results):
results = [] # [(m_idx, result)]
for qscript_res, qscript_idxs in zip(expanded_results, idxs_coeffs):
if isinstance(qscript_idxs[0], tuple): # qscript_res contains only one result
for idx, coeff in qscript_idxs:
results.append((idx, qscript_res if coeff is None else coeff * qscript_res))
continue
# qscript_res contains multiple results
for res, idxs in zip(qscript_res, qscript_idxs):
for idx, coeff in idxs:
results.append((idx, res if coeff is None else coeff * res))
# sum results by idx
res_dict = {}
for idx, res in results:
if idx in res_dict:
res_dict[idx] += res
else:
res_dict[idx] = res
# sort results by idx
results = [res_dict[key] for key in sorted(res_dict)]
return results[0] if len(results) == 1 else results
return qscripts, processing_fn
def _group_measurements(measurements: List[MeasurementProcess]) -> List[List[MeasurementProcess]]:
"""Group observables of ``measurements`` into groups with non overlapping wires.
Args:
measurements (List[MeasurementProcess]): list of measurement processes
Returns:
List[List[MeasurementProcess]]: list of groups of observables with non overlapping wires
"""
qwc_groups = []
for m in measurements:
if len(m.wires) == 0: # measurement acts on all wires: e.g. qml.counts()
qwc_groups.append((m.wires, [m]))
continue
op_added = False
for idx, (wires, group) in enumerate(qwc_groups):
if len(wires) > 0 and all(wire not in m.wires for wire in wires):
qwc_groups[idx] = (wires + m.wires, group + [m])
op_added = True
break
if not op_added:
qwc_groups.append((m.wires, [m]))
return [group[1] for group in qwc_groups]
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