Source code for pennylane.tape.qscript
# Copyright 2018-2022 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 defines the QuantumScript object responsible for storing quantum operations and measurements to be
executed by a device.
"""
# pylint: disable=too-many-instance-attributes, protected-access, too-many-public-methods
import contextlib
import copy
from collections import Counter
from collections.abc import Callable, Hashable, Iterable, Iterator, Sequence
from functools import cached_property
from typing import Any, Optional, TypeVar, Union
import pennylane as qml
from pennylane.measurements import MeasurementProcess, ProbabilityMP, StateMP
from pennylane.measurements.shots import Shots, ShotsLike
from pennylane.operation import _UNSET_BATCH_SIZE, Observable, Operation, Operator
from pennylane.pytrees import register_pytree
from pennylane.queuing import AnnotatedQueue, process_queue
from pennylane.typing import TensorLike
from pennylane.wires import Wires, WiresLike
OPENQASM_GATES = {
"CNOT": "cx",
"CZ": "cz",
"U3": "u3",
"U2": "u2",
"U1": "u1",
"Identity": "id",
"PauliX": "x",
"PauliY": "y",
"PauliZ": "z",
"Hadamard": "h",
"S": "s",
"Adjoint(S)": "sdg",
"T": "t",
"Adjoint(T)": "tdg",
"RX": "rx",
"RY": "ry",
"RZ": "rz",
"CRX": "crx",
"CRY": "cry",
"CRZ": "crz",
"SWAP": "swap",
"Toffoli": "ccx",
"CSWAP": "cswap",
"PhaseShift": "u1",
}
"""
dict[str, str]: Maps PennyLane gate names to equivalent QASM gate names.
Note that QASM has two native gates:
- ``U`` (equivalent to :class:`~.U3`)
- ``CX`` (equivalent to :class:`~.CNOT`)
All other gates are defined in the file stdgates.inc:
https://github.com/Qiskit/openqasm/blob/master/examples/stdgates.inc
"""
QS = TypeVar("QS", bound="QuantumScript")
[docs]class QuantumScript:
r"""The operations and measurements that represent instructions for
execution on a quantum device.
Args:
ops (Iterable[Operator]): An iterable of the operations to be performed
measurements (Iterable[MeasurementProcess]): All the measurements to be performed
Keyword Args:
shots (None, int, Sequence[int], ~.Shots): Number and/or batches of shots for execution.
Note that this property is still experimental and under development.
trainable_params (None, Sequence[int]): the indices for which parameters are trainable
.. seealso:: :class:`pennylane.tape.QuantumTape`
**Example:**
.. code-block:: python
from pennylane.tape import QuantumScript
ops = [qml.BasisState(np.array([1,1]), wires=(0,"a")),
qml.RX(0.432, 0),
qml.RY(0.543, 0),
qml.CNOT((0,"a")),
qml.RX(0.133, "a")]
qscript = QuantumScript(ops, [qml.expval(qml.Z(0))])
>>> list(qscript)
[BasisState(array([1, 1]), wires=[0, "a"]),
RX(0.432, wires=[0]),
RY(0.543, wires=[0]),
CNOT(wires=[0, 'a']),
RX(0.133, wires=['a']),
expval(Z(0))]
>>> qscript.operations
[BasisState(array([1, 1]), wires=[0, "a"]),
RX(0.432, wires=[0]),
RY(0.543, wires=[0]),
CNOT(wires=[0, 'a']),
RX(0.133, wires=['a'])]
>>> qscript.measurements
[expval(Z(0))]
Iterating over the quantum script can be done by:
>>> for op in qscript:
... print(op)
BasisState(array([1, 1]), wires=[0, "a"])
RX(0.432, wires=[0])
RY(0.543, wires=[0])
CNOT(wires=[0, 'a'])
RX(0.133, wires=['a'])
expval(Z(0))'
Quantum scripts also support indexing and length determination:
>>> qscript[0]
BasisState(array([1, 1]), wires=[0, "a"])
>>> len(qscript)
6
Once constructed, the script can be executed directly on a quantum device
using the :func:`~.pennylane.execute` function:
>>> dev = qml.device('default.qubit', wires=(0,'a'))
>>> qml.execute([qscript], dev, gradient_fn=None)
[array([-0.77750694])]
Quantum scripts can also store information about the number and batches of
executions by setting the ``shots`` keyword argument. This information is internally
stored in a :class:`pennylane.measurements.Shots` object:
>>> s_vec = [1, 1, 2, 2, 2]
>>> qscript = QuantumScript([qml.Hadamard(0)], [qml.expval(qml.Z(0))], shots=s_vec)
>>> qscript.shots.shot_vector
(ShotCopies(1 shots x 2), ShotCopies(2 shots x 3))
``ops`` and ``measurements`` are converted to lists upon initialization,
so those arguments accept any iterable object:
>>> qscript = QuantumScript((qml.X(i) for i in range(3)))
>>> qscript.circuit
[X(0), X(1), X(2)]
"""
def _flatten(self):
return (self._ops, self.measurements), (self.shots, tuple(self.trainable_params))
@classmethod
def _unflatten(cls, data, metadata):
return cls(*data, shots=metadata[0], trainable_params=metadata[1])
def __init__(
self,
ops: Optional[Iterable[Operator]] = None,
measurements: Optional[Iterable[MeasurementProcess]] = None,
shots: Optional[ShotsLike] = None,
trainable_params: Optional[Sequence[int]] = None,
):
self._ops = [] if ops is None else list(ops)
self._measurements = [] if measurements is None else list(measurements)
self._shots = Shots(shots)
self._trainable_params = trainable_params
self._graph = None
self._specs = None
self._output_dim = None
self._batch_size = _UNSET_BATCH_SIZE
self._obs_sharing_wires = None
self._obs_sharing_wires_id = None
def __repr__(self) -> str:
return f"<{self.__class__.__name__}: wires={self.wires.tolist()}, params={self.num_params}>"
@cached_property
def hash(self) -> int:
"""int: returns an integer hash uniquely representing the quantum script"""
fingerprint = []
fingerprint.extend(op.hash for op in self.operations)
fingerprint.extend(m.hash for m in self.measurements)
fingerprint.extend(self.trainable_params)
fingerprint.extend(self.shots)
return hash(tuple(fingerprint))
def __iter__(self) -> Iterator[Union[Operator, MeasurementProcess]]:
"""Iterator[.Operator, .MeasurementProcess]: Return an iterator to the
underlying quantum circuit object."""
return iter(self.circuit)
def __getitem__(self, idx: int) -> Union[Operator, MeasurementProcess]:
"""Union[Operator, MeasurementProcess]: Return the indexed operator from underlying quantum
circuit object."""
return self.circuit[idx]
def __len__(self) -> int:
"""int: Return the number of operations and measurements in the
underlying quantum circuit object."""
return len(self.circuit)
# ========================================================
# QSCRIPT properties
# ========================================================
@property
def circuit(self) -> list[Union[Operator, MeasurementProcess]]:
"""Returns the underlying quantum circuit as a list of operations and measurements.
The circuit is created with the assumptions that:
* The ``operations`` attribute contains quantum operations and
mid-circuit measurements and
* The ``measurements`` attribute contains terminal measurements.
Note that the resulting list could contain MeasurementProcess objects
that some devices may not support.
Returns:
list[.Operator, .MeasurementProcess]: the quantum circuit
containing quantum operations and measurements
"""
return self.operations + self.measurements
@property
def operations(self) -> list[Operator]:
"""Returns the state preparations and operations on the quantum script.
Returns:
list[.Operator]: quantum operations
>>> ops = [qml.StatePrep([0, 1], 0), qml.RX(0.432, 0)]
>>> qscript = QuantumScript(ops, [qml.expval(qml.Z(0))])
>>> qscript.operations
[StatePrep([0, 1], wires=[0]), RX(0.432, wires=[0])]
"""
return self._ops
@property
def observables(self) -> list[Union[MeasurementProcess, Observable]]:
"""Returns the observables on the quantum script.
Returns:
list[.MeasurementProcess, .Observable]]: list of observables
**Example**
>>> ops = [qml.StatePrep([0, 1], 0), qml.RX(0.432, 0)]
>>> qscript = QuantumScript(ops, [qml.expval(qml.Z(0))])
>>> qscript.observables
[expval(Z(0))]
"""
# TODO: modify this property once devices
# have been refactored to accept and understand recieving
# measurement processes rather than specific observables.
obs = []
for m in self.measurements:
if m.obs is not None:
obs.append(m.obs)
else:
obs.append(m)
return obs
@property
def measurements(self) -> list[MeasurementProcess]:
"""Returns the measurements on the quantum script.
Returns:
list[.MeasurementProcess]: list of measurement processes
**Example**
>>> ops = [qml.StatePrep([0, 1], 0), qml.RX(0.432, 0)]
>>> qscript = QuantumScript(ops, [qml.expval(qml.Z(0))])
>>> qscript.measurements
[expval(Z(0))]
"""
return self._measurements
@property
def samples_computational_basis(self) -> bool:
"""Determines if any of the measurements are in the computational basis."""
return any(o.samples_computational_basis for o in self.measurements)
@property
def num_params(self) -> int:
"""Returns the number of trainable parameters on the quantum script."""
return len(self.trainable_params)
@property
def batch_size(self) -> Optional[int]:
r"""The batch size of the quantum script inferred from the batch sizes
of the used operations for parameter broadcasting.
.. seealso:: :attr:`~.Operator.batch_size` for details.
Returns:
int or None: The batch size of the quantum script if present, else ``None``.
"""
if self._batch_size is _UNSET_BATCH_SIZE:
self._update_batch_size()
return self._batch_size
@property
def output_dim(self) -> int:
"""The (inferred) output dimension of the quantum script."""
if self._output_dim is None:
self._update_output_dim() # this will set _batch_size if it isn't already
return self._output_dim
@property
def diagonalizing_gates(self) -> list[Operation]:
"""Returns the gates that diagonalize the measured wires such that they
are in the eigenbasis of the circuit observables.
Returns:
List[~.Operation]: the operations that diagonalize the observables
**Examples**
For a tape with a single observable, we get the diagonalizing gate of that observable:
>>> tape = qml.tape.QuantumScript([], [qml.expval(X(0))])
>>> tape.diagonalizing_gates
[Hadamard(wires=[0])]
If the tape includes multiple observables, they are each diagonalized individually:
>>> tape = qml.tape.QuantumScript([], [qml.expval(X(0)), qml.var(Y(1))])
>>> tape.diagonalizing_gates
[Hadamard(wires=[0]), Z(1), S(wires=[1]), Hadamard(wires=[1])]
.. warning::
If the tape contains multiple observables acting on the same wire,
then ``tape.diagonalizing_gates`` will include multiple conflicting
diagonalizations.
For example:
>>> tape = qml.tape.QuantumScript([], [qml.expval(X(0)), qml.var(Y(0))])
>>> tape.diagonalizing_gates
[Hadamard(wires=[0]), Z(0), S(wires=[0]), Hadamard(wires=[0])]
If it is relevant for your application, applying
:func:`~.pennylane.transforms.split_non_commuting` to a tape will split it into multiple
tapes with only qubit-wise commuting observables.
Generally, composite operators are handled by diagonalizing their component parts, for example:
>>> tape = qml.tape.QuantumScript([], [qml.expval(X(0)+Y(1))])
>>> tape.diagonalizing_gates
[Hadamard(wires=[0]), Z(1), S(wires=[1]), Hadamard(wires=[1])]
However, for operators that contain multiple terms on the same wire, a single diagonalizing
operator will be returned that diagonalizes the full operator as a unit:
>>> tape = qml.tape.QuantumScript([], [qml.expval(X(0)+Y(0))])
>>> tape.diagonalizing_gates
[QubitUnitary(array([[-0.70710678-0.j , 0.5 -0.5j],
[-0.70710678-0.j , -0.5 +0.5j]]), wires=[0])]
"""
rotation_gates = []
with qml.queuing.QueuingManager.stop_recording():
for observable in _get_base_obs(self.observables):
# some observables do not have diagonalizing gates,
# in which case we just don't append any
with contextlib.suppress(qml.operation.DiagGatesUndefinedError):
rotation_gates.extend(observable.diagonalizing_gates())
return rotation_gates
@property
def shots(self) -> Shots:
"""Returns a ``Shots`` object containing information about the number
and batches of shots
Returns:
~.Shots: Object with shot information
"""
return self._shots
@property
def num_preps(self) -> int:
"""Returns the index of the first operator that is not an StatePrepBase operator."""
idx = 0
num_ops = len(self.operations)
while idx < num_ops and isinstance(self.operations[idx], qml.operation.StatePrepBase):
idx += 1
return idx
@property
def op_wires(self) -> Wires:
"""Returns the wires that the tape operations act on."""
return Wires.all_wires(op.wires for op in self.operations)
##### Update METHODS ###############
def _update(self):
"""Update all internal metadata regarding processed operations and observables"""
self._graph = None
self._specs = None
self._trainable_params = None
try:
# Invalidate cached properties so they get recalculated
del self.wires
del self.par_info
del self.hash
except AttributeError:
pass
@cached_property
def wires(self) -> Wires:
"""Returns the wires used in the quantum script process
Returns:
~.Wires: wires in quantum script process
"""
return Wires.all_wires(dict.fromkeys(op.wires for op in self))
@property
def num_wires(self) -> int:
"""Returns the number of wires in the quantum script process
Returns:
int: number of wires in quantum script process
"""
return len(self.wires)
@cached_property
def par_info(self) -> list[dict[str, Union[int, Operator]]]:
"""Returns the parameter information of the operations and measurements in the quantum script.
Returns:
list[dict[str, Operator or int]]: list of dictionaries with parameter information.
**Example**
>>> ops = [qml.StatePrep([0, 1], 0), qml.RX(0.432, 0), qml.CNOT((0,1))]
>>> qscript = QuantumScript(ops, [qml.expval(qml.Z(0))])
>>> qscript.par_info
[{'op': StatePrep(array([0, 1]), wires=[0]), 'op_idx': 0, 'p_idx': 0},
{'op': RX(0.432, wires=[0]), 'op_idx': 1, 'p_idx': 0}]
Note that the operations and measurements included in this list are only the ones that have parameters
"""
par_info = []
for idx, op in enumerate(self.operations):
par_info.extend({"op": op, "op_idx": idx, "p_idx": i} for i, d in enumerate(op.data))
n_ops = len(self.operations)
for idx, m in enumerate(self.measurements):
if m.obs is not None:
par_info.extend(
{"op": m.obs, "op_idx": idx + n_ops, "p_idx": i}
for i, d in enumerate(m.obs.data)
)
return par_info
@property
def obs_sharing_wires(self) -> list[Operator]:
"""Returns the subset of the observables that share wires with another observable,
i.e., that do not have their own unique set of wires.
Returns:
list[~.Observable]: list of observables with shared wires.
"""
if self._obs_sharing_wires is None:
self._update_observables()
return self._obs_sharing_wires
@property
def obs_sharing_wires_id(self) -> list[int]:
"""Returns the indices subset of the observables that share wires with another observable,
i.e., that do not have their own unique set of wires.
Returns:
list[int]: list of indices from observables with shared wires.
"""
if self._obs_sharing_wires_id is None:
self._update_observables()
return self._obs_sharing_wires_id
def _update_observables(self):
"""Update information about observables, including the wires that are acted upon and
identifying any observables that share wires.
Sets:
_obs_sharing_wires (list[~.Observable]): Observables that share wires with
any other observable
_obs_sharing_wires_id (list[int]): Indices of the measurements that contain
the observables in _obs_sharing_wires
"""
obs_wires = [wire for m in self.measurements for wire in m.wires if m.obs is not None]
self._obs_sharing_wires = []
self._obs_sharing_wires_id = []
if len(obs_wires) != len(set(obs_wires)):
c = Counter(obs_wires)
repeated_wires = {w for w in obs_wires if c[w] > 1}
for i, m in enumerate(self.measurements):
if m.obs is not None and len(set(m.wires) & repeated_wires) > 0:
self._obs_sharing_wires.append(m.obs)
self._obs_sharing_wires_id.append(i)
def _update_batch_size(self):
"""Infer the batch_size of the quantum script from the batch sizes of its operations
and check the latter for consistency.
Sets:
_batch_size (int): The common batch size of the quantum script operations, if any has one
"""
candidate = None
for op in self.operations:
op_batch_size = getattr(op, "batch_size", None)
if op_batch_size is None:
continue
if candidate:
if op_batch_size != candidate:
raise ValueError(
"The batch sizes of the quantum script operations do not match, they include "
f"{candidate} and {op_batch_size}."
)
else:
candidate = op_batch_size
self._batch_size = candidate
def _update_output_dim(self):
"""Update the dimension of the output of the quantum script.
Sets:
self._output_dim (int): Size of the quantum script output (when flattened)
This method makes use of `self.batch_size`, so that `self._batch_size`
needs to be up to date when calling it.
Call `_update_batch_size` before `_update_output_dim`
"""
self._output_dim = 0
for m in self.measurements:
# attempt to infer the output dimension
if isinstance(m, ProbabilityMP):
# TODO: what if we had a CV device here? Having the base as
# 2 would have to be swapped to the cutoff value
self._output_dim += 2 ** len(m.wires)
elif not isinstance(m, StateMP):
self._output_dim += 1
if self.batch_size:
self._output_dim *= self.batch_size
# ========================================================
# Parameter handling
# ========================================================
@property
def data(self) -> list[TensorLike]:
"""Alias to :meth:`~.get_parameters` and :meth:`~.set_parameters`
for backwards compatibilities with operations."""
return self.get_parameters(trainable_only=False)
@property
def trainable_params(self) -> list[int]:
r"""Store or return a list containing the indices of parameters that support
differentiability. The indices provided match the order of appearance in the
quantum circuit.
Setting this property can help reduce the number of quantum evaluations needed
to compute the Jacobian; parameters not marked as trainable will be
automatically excluded from the Jacobian computation.
The number of trainable parameters determines the number of parameters passed to
:meth:`~.set_parameters`, and changes the default output size of method :meth:`~.get_parameters()`.
.. note::
For devices that support native backpropagation (such as
``default.qubit`` and ``default.mixed``), this
property contains no relevant information when using
backpropagation to compute gradients.
**Example**
>>> ops = [qml.RX(0.432, 0), qml.RY(0.543, 0),
... qml.CNOT((0,"a")), qml.RX(0.133, "a")]
>>> qscript = QuantumScript(ops, [qml.expval(qml.Z(0))])
>>> qscript.trainable_params
[0, 1, 2]
>>> qscript.trainable_params = [0] # set only the first parameter as trainable
>>> qscript.get_parameters()
[0.432]
"""
if self._trainable_params is None:
self._trainable_params = list(range(len(self.par_info)))
return self._trainable_params
@trainable_params.setter
def trainable_params(self, param_indices: list[int]):
"""Store the indices of parameters that support differentiability.
Args:
param_indices (list[int]): parameter indices
"""
if any(not isinstance(i, int) or i < 0 for i in param_indices):
raise ValueError("Argument indices must be non-negative integers.")
num_params = len(self.par_info)
if any(i > num_params for i in param_indices):
raise ValueError(f"Quantum Script only has {num_params} parameters.")
self._trainable_params = sorted(set(param_indices))
[docs] def get_operation(self, idx: int) -> tuple[Operator, int, int]:
"""Returns the trainable operation, the operation index and the corresponding operation argument
index, for a specified trainable parameter index.
Args:
idx (int): the trainable parameter index
Returns:
tuple[.Operation, int, int]: tuple containing the corresponding
operation, operation index and an integer representing the argument index,
for the provided trainable parameter.
"""
# get the index of the parameter in the script
t_idx = self.trainable_params[idx]
# get the info for the parameter
info = self.par_info[t_idx]
return info["op"], info["op_idx"], info["p_idx"]
[docs] def get_parameters(
self,
trainable_only: bool = True,
operations_only: bool = False,
**kwargs, # pylint:disable=unused-argument
) -> list[TensorLike]:
"""Return the parameters incident on the quantum script operations.
The returned parameters are provided in order of appearance
on the quantum script.
Args:
trainable_only (bool): if True, returns only trainable parameters
operations_only (bool): if True, returns only the parameters of the
operations excluding parameters to observables of measurements
**Example**
>>> ops = [qml.RX(0.432, 0), qml.RY(0.543, 0),
... qml.CNOT((0,"a")), qml.RX(0.133, "a")]
>>> qscript = QuantumScript(ops, [qml.expval(qml.Z(0))])
By default, all parameters are trainable and will be returned:
>>> qscript.get_parameters()
[0.432, 0.543, 0.133]
Setting the trainable parameter indices will result in only the specified
parameters being returned:
>>> qscript.trainable_params = [1] # set the second parameter as trainable
>>> qscript.get_parameters()
[0.543]
The ``trainable_only`` argument can be set to ``False`` to instead return
all parameters:
>>> qscript.get_parameters(trainable_only=False)
[0.432, 0.543, 0.133]
"""
if trainable_only:
params = []
for p_idx in self.trainable_params:
par_info = self.par_info[p_idx]
if operations_only and isinstance(self[par_info["op_idx"]], MeasurementProcess):
continue
op = par_info["op"]
op_idx = par_info["p_idx"]
params.append(op.data[op_idx])
return params
# If trainable_only=False, return all parameters
# This is faster than the above and should be used when indexing `par_info` is not needed
params = [d for op in self.operations for d in op.data]
if operations_only:
return params
for m in self.measurements:
if m.obs is not None:
params.extend(m.obs.data)
return params
[docs] def bind_new_parameters(
self, params: Sequence[TensorLike], indices: Sequence[int]
) -> "QuantumScript":
"""Create a new tape with updated parameters.
This function takes a list of new parameters as input, and returns
a new :class:`~.tape.QuantumScript` containing the new parameters at the provided indices,
with the parameters at all other indices remaining the same.
Args:
params (Sequence[TensorLike]): New parameters to create the tape with. This
must have the same length as ``indices``.
indices (Sequence[int]): The parameter indices to update with the given parameters.
The index of a parameter is defined as its index in ``tape.get_parameters()``.
Returns:
.tape.QuantumScript: New tape with updated parameters
**Example**
>>> ops = [qml.RX(0.432, 0), qml.RY(0.543, 0),
... qml.CNOT((0,"a")), qml.RX(0.133, "a")]
>>> qscript = QuantumScript(ops, [qml.expval(qml.Z(0))])
A new tape can be created by passing new parameters along with the indices
to be updated. To modify all parameters in the above qscript:
>>> new_qscript = qscript.bind_new_parameters([0.1, 0.2, 0.3], [0, 1, 2])
>>> new_qscript.get_parameters()
[0.1, 0.2, 0.3]
The original ``qscript`` remains unchanged:
>>> qscript.get_parameters()
[0.432, 0.543, 0.133]
A subset of parameters can be modified as well, defined by the parameter indices:
>>> newer_qscript = new_qscript.bind_new_parameters([-0.1, 0.5], [0, 2])
>>> newer_qscript.get_parameters()
[-0.1, 0.2, 0.5]
"""
# pylint: disable=no-member
if len(params) != len(indices):
raise ValueError("Number of provided parameters does not match number of indices")
# determine the ops that need to be updated
op_indices = {}
for param_idx, idx in enumerate(sorted(indices)):
pinfo = self.par_info[idx]
op_idx, p_idx = pinfo["op_idx"], pinfo["p_idx"]
if op_idx not in op_indices:
op_indices[op_idx] = {}
op_indices[op_idx][p_idx] = param_idx
new_ops = self.circuit
for op_idx, p_indices in op_indices.items():
op = new_ops[op_idx]
data = op.data if isinstance(op, Operator) else op.obs.data
new_params = [params[p_indices[i]] if i in p_indices else d for i, d in enumerate(data)]
if isinstance(op, Operator):
new_op = qml.ops.functions.bind_new_parameters(op, new_params)
else:
new_obs = qml.ops.functions.bind_new_parameters(op.obs, new_params)
new_op = op.__class__(obs=new_obs)
new_ops[op_idx] = new_op
new_operations = new_ops[: len(self.operations)]
new_measurements = new_ops[len(self.operations) :]
return self.__class__(
new_operations,
new_measurements,
shots=self.shots,
trainable_params=self.trainable_params,
)
# ========================================================
# MEASUREMENT SHAPE
#
# We can extract the private static methods to a new class later
# ========================================================
[docs] def shape(
self, device: Union["qml.devices.Device", "qml.devices.LegacyDevice"]
) -> Union[tuple[int, ...], tuple[tuple[int, ...], ...]]:
"""Produces the output shape of the quantum script by inspecting its measurements
and the device used for execution.
.. note::
The computed shape is not stored because the output shape may be
dependent on the device used for execution.
Args:
device (pennylane.devices.Device): the device that will be used for the script execution
Returns:
Union[tuple[int], tuple[tuple[int]]]: the output shape(s) of the quantum script result
**Examples**
.. code-block:: pycon
>>> dev = qml.device('default.qubit', wires=2)
>>> qs = QuantumScript(measurements=[qml.state()])
>>> qs.shape(dev)
(4,)
>>> m = [qml.state(), qml.expval(qml.Z(0)), qml.probs((0,1))]
>>> qs = QuantumScript(measurements=m)
>>> qs.shape(dev)
((4,), (), (4,))
"""
num_device_wires = len(device.wires) if device.wires else len(self.wires)
def get_shape(mp, _shots):
# depends on num_device_wires and self.batch_size from closure
standard_shape = mp.shape(shots=_shots, num_device_wires=num_device_wires)
if self.batch_size:
return (self.batch_size, *standard_shape)
return standard_shape
shape = []
for s in self.shots if self.shots else [None]:
shots_shape = tuple(get_shape(mp, s) for mp in self.measurements)
shots_shape = shots_shape[0] if len(shots_shape) == 1 else tuple(shots_shape)
shape.append(shots_shape)
return tuple(shape) if self.shots.has_partitioned_shots else shape[0]
@property
def numeric_type(self) -> Union[type, tuple[type, ...]]:
"""Returns the expected numeric type of the quantum script result by inspecting
its measurements.
Returns:
Union[type, Tuple[type]]: The numeric type corresponding to the result type of the
quantum script, or a tuple of such types if the script contains multiple measurements
**Example:**
.. code-block:: pycon
>>> dev = qml.device('default.qubit', wires=2)
>>> qs = QuantumScript(measurements=[qml.state()])
>>> qs.numeric_type
complex
"""
types = tuple(observable.numeric_type for observable in self.measurements)
return types[0] if len(types) == 1 else types
# ========================================================
# Transforms: QuantumScript to QuantumScript
# ========================================================
[docs] def copy(self, copy_operations: bool = False, **update) -> "QuantumScript":
"""Returns a copy of the quantum script. If any attributes are defined via keyword argument,
those are used on the new tape - otherwise, all attributes match the original tape. The copy
is a shallow copy if `copy_operations` is False and no tape attributes are updated via keyword
argument.
Args:
copy_operations (bool): If True, the operations are also shallow copied.
Otherwise, if False, the copied operations will simply be references
to the original operations; changing the parameters of one script will likewise
change the parameters of all copies. If any keyword arguments are passed to update,
this argument will be treated as True.
Keyword Args:
operations (Iterable[Operator]): An iterable of the operations to be performed. If provided, these
operations will replace the copied operations on the new tape.
measurements (Iterable[MeasurementProcess]): All the measurements to be performed. If provided, these
measurements will replace the copied measurements on the new tape.
shots (None, int, Sequence[int], ~.Shots): Number and/or batches of shots for execution. If provided, these
shots will replace the copied shots on the new tape.
trainable_params (None, Sequence[int]): The indices for which parameters are trainable. If provided, these
parameter indices will replace the copied parameter indices on the new tape.
Returns:
QuantumScript : A copy of the quantum script, with modified attributes if specified by keyword argument.
**Example**
.. code-block:: python
tape = qml.tape.QuantumScript(
ops= [qml.X(0), qml.Y(1)],
measurements=[qml.expval(qml.Z(0))],
shots=2000)
new_tape = tape.copy(measurements=[qml.expval(qml.X(1))])
>>> tape.measurements
[qml.expval(qml.Z(0)]
>>> new_tape.measurements
[qml.expval(qml.X(1))]
>>> new_tape.shots
Shots(total_shots=2000, shot_vector=(ShotCopies(2000 shots x 1),))
"""
if update:
if "ops" in update:
update["operations"] = update["ops"]
for k in update:
if k not in ["ops", "operations", "measurements", "shots", "trainable_params"]:
raise TypeError(
f"{self.__class__}.copy() got an unexpected key '{k}' in update dict"
)
if copy_operations or update:
# Perform a shallow copy of all operations in the operation and measurement
# queues. The operations will continue to share data with the original script operations
# unless modified.
_ops = update.get("operations", [copy.copy(op) for op in self.operations])
_measurements = update.get("measurements", [copy.copy(op) for op in self.measurements])
else:
# Perform a shallow copy of the operation and measurement queues. The
# operations within the queues will be references to the original script operations;
# changing the original operations will always alter the operations on the copied script.
_ops = self.operations.copy()
_measurements = self.measurements.copy()
update_trainable_params = "operations" in update or "measurements" in update
# passing trainable_params=None will re-calculate trainable_params
default_trainable_params = None if update_trainable_params else self.trainable_params
new_qscript = self.__class__(
ops=_ops,
measurements=_measurements,
shots=update.get("shots", self.shots),
trainable_params=update.get("trainable_params", default_trainable_params),
)
# copy cached properties when relevant
new_qscript._graph = None if copy_operations or update else self._graph
if not update.get("operations"):
# batch size may change if operations were updated
new_qscript._batch_size = self._batch_size
if not update.get("measurements"):
# obs may change if measurements were updated
new_qscript._obs_sharing_wires = self._obs_sharing_wires
new_qscript._obs_sharing_wires_id = self._obs_sharing_wires_id
if not (update.get("measurements") or update.get("operations")):
# output_dim may change if either measurements or operations were updated
new_qscript._output_dim = self._output_dim
return new_qscript
def __copy__(self) -> "QuantumScript":
return self.copy(copy_operations=True)
[docs] def expand(
self,
depth: int = 1,
stop_at: Optional[Callable[[Union[Operation, MeasurementProcess]], bool]] = None,
expand_measurements: bool = False,
) -> "QuantumScript":
"""Expand all operations to a specific depth.
Args:
depth (int): the depth the script should be expanded
stop_at (Callable): A function which accepts a queue object,
and returns ``True`` if this object should *not* be expanded.
If not provided, all objects that support expansion will be expanded.
expand_measurements (bool): If ``True``, measurements will be expanded
to basis rotations and computational basis measurements.
.. seealso:: :func:`~.pennylane.devices.preprocess.decompose` for a transform that
performs the same job and fits into the current transform architecture.
.. warning::
This method cannot be used with a tape with non-commuting measurements, even if
``expand_measurements=False``.
>>> mps = [qml.expval(qml.X(0)), qml.expval(qml.Y(0))]
>>> tape = qml.tape.QuantumScript([], mps)
>>> tape.expand()
QuantumFunctionError: Only observables that are qubit-wise commuting Pauli words
can be returned on the same wire, some of the following measurements do not commute:
[expval(X(0)), expval(Y(0))]
Since commutation is determined by pauli word arithmetic, non-pauli words cannot share
wires with other measurements, even if they commute:
>>> measurements = [qml.expval(qml.Projector([0], 0)), qml.probs(wires=0)]
>>> tape = qml.tape.QuantumScript([], measurements)
>>> tape.expand()
QuantumFunctionError: Only observables that are qubit-wise commuting Pauli words
can be returned on the same wire, some of the following measurements do not commute:
[expval(Projector(array([0]), wires=[0])), probs(wires=[0])]
For this reason, we recommend the use of :func:`~.pennylane.devices.preprocess.decompose` instead.
.. details::
:title: Usage Details
>>> ops = [qml.Permute((2,1,0), wires=(0,1,2)), qml.X(0)]
>>> measurements = [qml.expval(qml.X(0))]
>>> tape = qml.tape.QuantumScript(ops, measurements)
>>> expanded_tape = tape.expand()
>>> print(expanded_tape.draw())
0: ─╭SWAP──Rϕ──RX──Rϕ─┤ <X>
2: ─╰SWAP─────────────┤
Specifying a depth greater than one decomposes operations multiple times.
>>> expanded_tape2 = tape.expand(depth=2)
>>> print(expanded_tape2.draw())
0: ─╭●─╭X─╭●──RZ──GlobalPhase──RX──RZ──GlobalPhase─┤ <Z>
2: ─╰X─╰●─╰X──────GlobalPhase──────────GlobalPhase─┤
The ``stop_at`` callable allows the specification of terminal
operations that should no longer be decomposed. In this example, the ``X``
operator is not decomposed because ``stop_at(qml.X(0)) == True``.
>>> def stop_at(obj):
... return isinstance(obj, qml.X)
>>> expanded_tape = tape.expand(stop_at=stop_at)
>>> print(expanded_tape.draw())
0: ─╭SWAP──X─┤ <X>
2: ─╰SWAP────┤
.. warning::
If an operator does not have a decomposition, it will not be decomposed, even if
``stop_at(obj) == False``. If you want to decompose to reach a certain gateset,
you will need an extra validation pass to ensure you have reached the gateset.
>>> def stop_at(obj):
... return getattr(obj, "name", "") in {"RX", "RY"}
>>> tape = qml.tape.QuantumScript([qml.RZ(0.1, 0)])
>>> tape.expand(stop_at=stop_at).circuit
[RZ(0.1, wires=[0])]
If more than one observable exists on a wire, the diagonalizing gates will be applied
and the observable will be substituted for an analogous combination of ``qml.Z`` operators.
This will happen even if ``expand_measurements=False``.
>>> mps = [qml.expval(qml.X(0)), qml.expval(qml.X(0) @ qml.X(1))]
>>> tape = qml.tape.QuantumScript([], mps)
>>> expanded_tape = tape.expand()
>>> print(expanded_tape.draw())
0: ──RY─┤ <Z> ╭<Z@Z>
1: ──RY─┤ ╰<Z@Z>
Setting ``expand_measurements=True`` applies any diagonalizing gates and converts
the measurement into a wires+eigvals representation.
.. warning::
Many components of PennyLane do not support the wires + eigvals representation.
Setting ``expand_measurements=True`` should be used with extreme caution.
>>> tape = qml.tape.QuantumScript([], [qml.expval(qml.X(0))])
>>> tape.expand(expand_measurements=True).circuit
[Hadamard(wires=[0]), expval(eigvals=[ 1. -1.], wires=[0])]
"""
return qml.tape.tape.expand_tape(
self, depth=depth, stop_at=stop_at, expand_measurements=expand_measurements
)
[docs] def adjoint(self) -> "QuantumScript":
"""Create a quantum script that is the adjoint of this one.
Adjointed quantum scripts are the conjugated and transposed version of the
original script. Adjointed ops are equivalent to the inverted operation for unitary
gates.
Returns:
~.QuantumScript: the adjointed script
"""
ops = self.operations[self.num_preps :]
prep = self.operations[: self.num_preps]
with qml.QueuingManager.stop_recording():
ops_adj = [qml.adjoint(op, lazy=False) for op in reversed(ops)]
return self.__class__(ops=prep + ops_adj, measurements=self.measurements, shots=self.shots)
# ========================================================
# Transforms: QuantumScript to Information
# ========================================================
@property
def graph(self) -> "qml.CircuitGraph":
"""Returns a directed acyclic graph representation of the recorded
quantum circuit:
>>> ops = [qml.StatePrep([0, 1], 0), qml.RX(0.432, 0)]
>>> qscript = QuantumScript(ops, [qml.expval(qml.Z(0))])
>>> qscript.graph
<pennylane.circuit_graph.CircuitGraph object at 0x7fcc0433a690>
Note that the circuit graph is only constructed once, on first call to this property,
and cached for future use.
Returns:
.CircuitGraph: the circuit graph object
"""
if self._graph is None:
self._graph = qml.CircuitGraph(
self.operations,
self.measurements,
self.wires,
self.par_info,
self.trainable_params,
)
return self._graph
@property
def specs(self) -> dict[str, Any]:
"""Resource information about a quantum circuit.
Returns:
dict[str, Union[defaultdict,int]]: dictionaries that contain quantum script specifications
**Example**
>>> ops = [qml.Hadamard(0), qml.RX(0.26, 1), qml.CNOT((1,0)),
... qml.Rot(1.8, -2.7, 0.2, 0), qml.Hadamard(1), qml.CNOT((0, 1))]
>>> qscript = QuantumScript(ops, [qml.expval(qml.Z(0) @ qml.Z(1))])
Asking for the specs produces a dictionary of useful information about the circuit:
>>> qscript.specs['num_observables']
1
>>> qscript.specs['gate_sizes']
defaultdict(<class 'int'>, {1: 4, 2: 2})
>>> print(qscript.specs['resources'])
wires: 2
gates: 6
depth: 4
shots: Shots(total=None)
gate_types:
{'Hadamard': 2, 'RX': 1, 'CNOT': 2, 'Rot': 1}
gate_sizes:
{1: 4, 2: 2}
"""
# pylint: disable=protected-access
if self._specs is None:
resources = qml.resource.resource._count_resources(self)
algo_errors = qml.resource.error._compute_algo_error(self)
self._specs = {
"resources": resources,
"errors": algo_errors,
"num_observables": len(self.observables),
"num_diagonalizing_gates": len(self.diagonalizing_gates),
"num_trainable_params": self.num_params,
}
return self._specs
# pylint: disable=too-many-arguments
[docs] def draw(
self,
wire_order: Optional[Iterable[Hashable]] = None,
show_all_wires: bool = False,
decimals: Optional[int] = None,
max_length: int = 100,
show_matrices: bool = True,
) -> str:
"""Draw the quantum script as a circuit diagram. See :func:`~.drawer.tape_text` for more information.
Args:
wire_order (Sequence[Any]): the order (from top to bottom) to print the wires of the circuit
show_all_wires (bool): If True, all wires, including empty wires, are printed.
decimals (int): How many decimal points to include when formatting operation parameters.
Default ``None`` will omit parameters from operation labels.
max_length (Int) : Maximum length of a individual line. After this length, the diagram will
begin anew beneath the previous lines.
show_matrices=True (bool): show matrix valued parameters below all circuit diagrams
Returns:
str: the circuit representation of the quantum script
"""
return qml.drawer.tape_text(
self,
wire_order=wire_order,
show_all_wires=show_all_wires,
decimals=decimals,
max_length=max_length,
show_matrices=show_matrices,
)
[docs] def to_openqasm(
self,
wires: Optional[WiresLike] = None,
rotations: bool = True,
measure_all: bool = True,
precision: Optional[int] = None,
) -> str:
"""Serialize the circuit as an OpenQASM 2.0 program.
Measurements are assumed to be performed on all qubits in the computational basis. An
optional ``rotations`` argument can be provided so that output of the OpenQASM circuit is
diagonal in the eigenbasis of the quantum script's observables. The measurement outputs can be
restricted to only those specified in the script by setting ``measure_all=False``.
.. note::
The serialized OpenQASM program assumes that gate definitions
in ``qelib1.inc`` are available.
Args:
wires (Wires or None): the wires to use when serializing the circuit
rotations (bool): in addition to serializing user-specified
operations, also include the gates that diagonalize the
measured wires such that they are in the eigenbasis of the circuit observables.
measure_all (bool): whether to perform a computational basis measurement on all qubits
or just those specified in the script
precision (int): decimal digits to display for parameters
Returns:
str: OpenQASM serialization of the circuit
"""
wires = wires or self.wires
# add the QASM headers
qasm_str = 'OPENQASM 2.0;\ninclude "qelib1.inc";\n'
if self.num_wires == 0:
# empty circuit
return qasm_str
# create the quantum and classical registers
qasm_str += f"qreg q[{len(wires)}];\n"
qasm_str += f"creg c[{len(wires)}];\n"
# get the user applied circuit operations without interface information
[transformed_tape], _ = qml.transforms.convert_to_numpy_parameters(self)
operations = transformed_tape.operations
if rotations:
# if requested, append diagonalizing gates corresponding
# to circuit observables
operations += self.diagonalizing_gates
# decompose the queue
# pylint: disable=no-member
just_ops = QuantumScript(operations)
operations = just_ops.expand(
depth=10, stop_at=lambda obj: obj.name in OPENQASM_GATES
).operations
# create the QASM code representing the operations
for op in operations:
try:
gate = OPENQASM_GATES[op.name]
except KeyError as e:
raise ValueError(f"Operation {op.name} not supported by the QASM serializer") from e
wire_labels = ",".join([f"q[{wires.index(w)}]" for w in op.wires.tolist()])
params = ""
if op.num_params > 0:
# If the operation takes parameters, construct a string
# with parameter values.
if precision is not None:
params = "(" + ",".join([f"{p:.{precision}}" for p in op.parameters]) + ")"
else:
# use default precision
params = "(" + ",".join([str(p) for p in op.parameters]) + ")"
qasm_str += f"{gate}{params} {wire_labels};\n"
# apply computational basis measurements to each quantum register
# NOTE: This is not strictly necessary, we could inspect self.observables,
# and then only measure wires which are requested by the user. However,
# some devices which consume QASM require all registers be measured, so
# measure all wires by default to be safe.
if measure_all:
for wire in range(len(wires)):
qasm_str += f"measure q[{wire}] -> c[{wire}];\n"
else:
measured_wires = Wires.all_wires([m.wires for m in self.measurements])
for w in measured_wires:
wire_indx = self.wires.index(w)
qasm_str += f"measure q[{wire_indx}] -> c[{wire_indx}];\n"
return qasm_str
[docs] @classmethod
def from_queue(
cls: type[QS], queue: qml.queuing.AnnotatedQueue, shots: Optional[ShotsLike] = None
) -> QS:
"""Construct a QuantumScript from an AnnotatedQueue."""
return cls(*process_queue(queue), shots=shots)
[docs] def map_to_standard_wires(self) -> "QuantumScript":
"""
Map a circuit's wires such that they are in a standard order. If no
mapping is required, the unmodified circuit is returned.
Returns:
QuantumScript: The circuit with wires in the standard order
The standard order is defined by the operator wires being increasing
integers starting at zero, to match array indices. If there are any
measurement wires that are not in any operations, those will be mapped
to higher values.
**Example:**
>>> circuit = qml.tape.QuantumScript([qml.X("a")], [qml.expval(qml.Z("b"))])
>>> circuit.map_to_standard_wires().circuit
[X(0), expval(Z(1))]
If any measured wires are not in any operations, they will be mapped last:
>>> circuit = qml.tape.QuantumScript([qml.X(1)], [qml.probs(wires=[0, 1])])
>>> circuit.map_to_standard_wires().circuit
[X(0), probs(wires=[1, 0])]
If no wire-mapping is needed, then the returned circuit *is* the inputted circuit:
>>> circuit = qml.tape.QuantumScript([qml.X(0)], [qml.expval(qml.Z(1))])
>>> circuit.map_to_standard_wires() is circuit
True
"""
op_wires = Wires.all_wires(op.wires for op in self.operations)
meas_wires = Wires.all_wires(mp.wires for mp in self.measurements)
num_op_wires = len(op_wires)
meas_only_wires = set(meas_wires) - set(op_wires)
if set(op_wires) == set(range(num_op_wires)) and meas_only_wires == set(
range(num_op_wires, num_op_wires + len(meas_only_wires))
):
return self
wire_map = {w: i for i, w in enumerate(op_wires + meas_only_wires)}
tapes, fn = qml.map_wires(self, wire_map)
return fn(tapes)
# TODO: Use "ParamSpecs" when min Python version is 3.10
[docs]def make_qscript(
fn: Callable[..., Any], shots: Optional[ShotsLike] = None
) -> Callable[..., QuantumScript]:
"""Returns a function that generates a qscript from a quantum function without any
operation queuing taking place.
This is useful when you would like to manipulate or transform
the qscript created by a quantum function without evaluating it.
Args:
fn (function): the quantum function to generate the qscript from
shots (None, int, Sequence[int], ~.Shots): number and/or
batches of executions
Returns:
function: The returned function takes the same arguments as the quantum
function. When called, it returns the generated quantum script
without any queueing occurring.
**Example**
Consider the following quantum function:
.. code-block:: python
def qfunc(x):
qml.Hadamard(wires=0)
qml.CNOT(wires=[0, 1])
qml.RX(x, wires=0)
We can use ``make_qscript`` to extract the qscript generated by this
quantum function, without any of the operations being queued by
any existing queuing contexts:
>>> with qml.queuing.AnnotatedQueue() as active_queue:
... _ = qml.RY(1.0, wires=0)
... qs = make_qscript(qfunc)(0.5)
>>> qs.operations
[Hadamard(wires=[0]), CNOT(wires=[0, 1]), RX(0.5, wires=[0])]
Note that the currently recording queue did not queue any of these quantum operations:
>>> active_queue.queue
[RY(1.0, wires=[0])]
"""
def wrapper(*args, **kwargs):
with AnnotatedQueue() as q:
fn(*args, **kwargs)
return QuantumScript.from_queue(q, shots)
return wrapper
QuantumScriptBatch = Sequence[QuantumScript]
QuantumScriptOrBatch = Union[QuantumScript, QuantumScriptBatch]
register_pytree(QuantumScript, QuantumScript._flatten, QuantumScript._unflatten)
def _get_base_obs(observables):
overlapping_ops_observables = []
while any(isinstance(o, (qml.ops.CompositeOp, qml.ops.SymbolicOp)) for o in observables):
new_obs = []
for observable in observables:
if isinstance(observable, qml.ops.CompositeOp):
if any(len(o) > 1 for o in observable.overlapping_ops):
overlapping_ops_observables.append(observable)
else:
new_obs.extend(observable.operands)
elif isinstance(observable, qml.ops.SymbolicOp):
new_obs.append(observable.base)
else:
new_obs.append(observable)
observables = new_obs
# removes duplicates from list without disrupting order - basically an ordered set
return list(dict.fromkeys(observables + overlapping_ops_observables))
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