Source code for pennylane.decomposition.decomposition_rule
# Copyright 2025 Xanadu Quantum Technologies Inc.
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"""Defines the ``DecompositionRule`` class to represent a decomposition rule."""
from __future__ import annotations
import inspect
from collections import Counter, defaultdict
from textwrap import dedent
from typing import Callable, Optional, Type, overload
from pennylane.operation import Operator
from .resources import CompressedResourceOp, Resources, resource_rep
from .utils import translate_op_alias
@overload
def register_condition(condition: Callable) -> Callable[[Callable], DecompositionRule]: ...
@overload
def register_condition(condition: Callable, qfunc: Callable) -> DecompositionRule: ...
[docs]
def register_condition(
condition: Callable[..., bool], qfunc: Optional[Callable] = None
) -> Callable[[Callable], DecompositionRule] | DecompositionRule:
"""Binds a condition to a decomposition rule for when it is applicable.
.. note::
This function is only relevant when the new experimental graph-based decomposition system
(introduced in v0.41) is enabled via :func:`~pennylane.decomposition.enable_graph`. This new way of
performing decompositions is generally more resource-efficient and accommodates multiple alternative
decomposition rules for an operator. In this new system, custom decomposition rules are
defined as quantum functions, and it is currently required that every decomposition rule
declares its required resources using :func:`~.register_resources`.
Args:
condition (Callable): a function which takes the resource parameters of an operator as
arguments and returns ``True`` or ``False`` based on whether the decomposition rule
is applicable to an operator with the given resource parameters.
qfunc (Callable): the quantum function that implements the decomposition. If ``None``,
returns a decorator for acting on a function.
Returns:
DecompositionRule:
a data structure that represents a decomposition rule, which contains a PennyLane
quantum function representing the decomposition, and its resource function.
**Example**
This function can be used as a decorator to bind a condition function to a quantum function
that implements a decomposition rule.
.. code-block:: python
import pennylane as qml
from pennylane.math.decomposition import zyz_rotation_angles
# The parameters must be consistent with ``qml.QubitUnitary.resource_keys``
def _zyz_condition(num_wires):
return num_wires == 1
@qml.register_condition(_zyz_condition)
@qml.register_resources({qml.RZ: 2, qml.RY: 1, qml.GlobalPhase: 1})
def zyz_decomposition(U, wires, **__):
# Assumes that U is a 2x2 unitary matrix
phi, theta, omega, phase = zyz_rotation_angles(U, return_global_phase=True)
qml.RZ(phi, wires=wires[0])
qml.RY(theta, wires=wires[0])
qml.RZ(omega, wires=wires[0])
qml.GlobalPhase(-phase)
# This decomposition will be ignored for `QubitUnitary` on more than one wire.
qml.add_decomps(qml.QubitUnitary, zyz_decomposition)
"""
def _decorator(_qfunc) -> DecompositionRule:
if isinstance(_qfunc, DecompositionRule):
_qfunc.set_condition(condition)
return _qfunc
return DecompositionRule(_qfunc, condition=condition)
return _decorator(qfunc) if qfunc else _decorator
@overload
def register_resources(resources: Callable | dict) -> Callable[[Callable], DecompositionRule]: ...
@overload
def register_resources(resources: Callable | dict, qfunc: Callable) -> DecompositionRule: ...
[docs]
def register_resources(
resources: Callable | dict, qfunc: Optional[Callable] = None
) -> Callable[[Callable], DecompositionRule] | DecompositionRule:
"""Binds a quantum function to its required resources.
.. note::
This function is only relevant when the new experimental graph-based decomposition system
(introduced in v0.41) is enabled via :func:`~pennylane.decomposition.enable_graph`. This new way of
doing decompositions is generally more resource efficient and accommodates multiple alternative
decomposition rules for an operator. In this new system, custom decomposition rules are
defined as quantum functions, and it is currently required that every decomposition rule
declares its required resources using ``qml.register_resources``.
Args:
resources (dict or Callable): a dictionary mapping unique operators within the given
``qfunc`` to their number of occurrences therein. If a function is provided instead
of a static dictionary, a dictionary must be returned from the function. For more
information, consult the "Quantum Functions as Decomposition Rules" section below.
qfunc (Callable): the quantum function that implements the decomposition. If ``None``,
returns a decorator for acting on a function.
Returns:
DecompositionRule:
a data structure that represents a decomposition rule, which contains a PennyLane
quantum function representing the decomposition, and its resource function.
**Example**
This function can be used as a decorator to bind a quantum function to its required resources
so that it can be used as a decomposition rule within the new graph-based decomposition system.
.. code-block:: python
from functools import partial
import pennylane as qml
qml.decomposition.enable_graph()
@qml.register_resources({qml.H: 2, qml.CZ: 1})
def my_cnot(wires, **_):
qml.H(wires=wires[1])
qml.CZ(wires=wires)
qml.H(wires=wires[1])
@partial(qml.transforms.decompose, gate_set={qml.CZ, qml.H}, fixed_decomps={qml.CNOT: my_cnot})
@qml.qnode(qml.device("default.qubit"))
def circuit():
qml.CNOT(wires=[0, 1])
return qml.state()
>>> print(qml.draw(circuit, level="device")())
0: ────╭●────┤ State
1: ──H─╰Z──H─┤ State
Alternatively, the decomposition rule can be created in-line:
>>> my_cnot = qml.register_resources({qml.H: 2, qml.CZ: 1}, my_cnot)
.. details::
:title: Quantum Functions as Decomposition Rules
Quantum functions representing decomposition rules within the new decomposition system
are expected to take ``(*op.parameters, op.wires, **op.hyperparameters)`` as arguments,
where ``op`` is an instance of the operator type that the decomposition is for.
.. details::
:title: Operators with Dynamic Resource Requirements
In many cases, the resource requirement of an operator's decomposition is not static; some
operators have properties that directly affect the resource estimate of its decompositions,
i.e., the types of gates that exist in the decomposition and their number of occurrences.
For each operator class, the set of parameters that affects the type of gates and their
number of occurrences in its decompositions is given by the ``resource_keys`` attribute.
For example, the number of gates in the decomposition for ``qml.MultiRZ`` changes based
on the number of wires it acts on, in contrast to the decomposition for ``qml.CNOT``:
>>> qml.CNOT.resource_keys
set()
>>> qml.MultiRZ.resource_keys
{'num_wires'}
The output of ``resource_keys`` indicates that custom decompositions for the operator
should be registered to a resource function (as opposed to a static dictionary) that
accepts those exact arguments and returns a dictionary.
.. code-block:: python
def _multi_rz_resources(num_wires):
return {
qml.CNOT: 2 * (num_wires - 1),
qml.RZ: 1
}
@qml.register_resources(_multi_rz_resources)
def multi_rz_decomposition(theta, wires, **__):
for w0, w1 in zip(wires[-1:0:-1], wires[-2::-1]):
qml.CNOT(wires=(w0, w1))
qml.RZ(theta, wires=wires[0])
for w0, w1 in zip(wires[1:], wires[:-1]):
qml.CNOT(wires=(w0, w1))
Additionally, if a custom decomposition for an operator contains gates that, in turn,
have properties that affect their own decompositions, this information must also be
included in the resource function. For example, if a decomposition rule produces a
``MultiRZ`` gate, it is not sufficient to declare the existence of a ``MultiRZ`` in the
resource function; the number of wires it acts on must also be specified.
Consider a fictitious operator with the following decomposition:
.. code-block:: python
def my_decomp(theta, wires):
qml.MultiRZ(theta, wires=wires[:-1])
qml.MultiRZ(theta, wires=wires)
qml.MultiRZ(theta, wires=wires[1:])
It contains two ``MultiRZ`` gates acting on ``len(wires) - 1`` wires (the first and last
``MultiRZ``) and one ``MultiRZ`` gate acting on exactly ``len(wires)`` wires. This
distinction must be reflected in the resource function:
.. code-block:: python
def my_resources(num_wires):
return {
qml.resource_rep(qml.MultiRZ, num_wires=num_wires - 1): 2,
qml.resource_rep(qml.MultiRZ, num_wires=num_wires): 1
}
my_decomp = qml.register_resources(my_resources, my_decomp)
where :func:`~pennylane.resource_rep` is a utility function that wraps an operator type and any
additional information relevant to its resource estimate into a compressed data structure.
To check what (if any) additional information is required to declare an operator type
in a resource function, refer to the ``resource_keys`` attribute of the :class:`~pennylane.operation.Operator`
class. Operators with non-empty ``resource_keys`` must be declared using ``qml.resource_rep``,
with keyword arguments matching its ``resource_keys`` exactly.
.. seealso::
:func:`~pennylane.resource_rep`
"""
def _decorator(_qfunc) -> DecompositionRule:
if isinstance(_qfunc, DecompositionRule):
_qfunc.set_resources(resources)
return _qfunc
return DecompositionRule(_qfunc, resources=resources)
return _decorator(qfunc) if qfunc else _decorator
[docs]
class DecompositionRule:
"""Represents a decomposition rule for an operator."""
def __init__(
self,
func: Callable,
resources: Optional[Callable | dict] = None,
condition: Optional[Callable[..., bool]] = None,
):
self._impl = func
try:
self._source = inspect.getsource(func)
except OSError: # pragma: no cover
# OSError is raised if the source code cannot be retrieved
self._source = "" # pragma: no cover
if isinstance(resources, dict):
def resource_fn(*_, **__):
return resources
self._compute_resources = resource_fn
else:
self._compute_resources = resources
self._condition = condition
def __call__(self, *args, **kwargs):
return self._impl(*args, **kwargs)
def __str__(self):
return dedent(self._source).strip()
[docs]
def compute_resources(self, *args, **kwargs) -> Resources:
"""Computes the resources required to implement this decomposition rule."""
if self._compute_resources is None:
raise NotImplementedError("No resource estimation found for this decomposition rule.")
raw_gate_counts = self._compute_resources(*args, **kwargs)
assert isinstance(raw_gate_counts, dict), "Resource function must return a dictionary."
gate_counter = Counter()
for op, count in raw_gate_counts.items():
if count > 0:
gate_counter.update({_auto_wrap(op): count})
return Resources(dict(gate_counter))
[docs]
def is_applicable(self, *args, **kwargs) -> bool:
"""Checks whether this decomposition rule is applicable."""
if self._condition is None:
return True
return self._condition(*args, **kwargs)
[docs]
def set_condition(self, condition: Callable[..., bool]) -> None:
"""Sets the condition for this decomposition rule."""
self._condition = condition
[docs]
def set_resources(self, resources: Callable | dict) -> None:
"""Sets the resources for this decomposition rule."""
if isinstance(resources, dict):
def resource_fn(*_, **__):
return resources
self._compute_resources = resource_fn
else:
self._compute_resources = resources
def _auto_wrap(op_type):
"""Conveniently wrap an operator type in a resource representation."""
if isinstance(op_type, CompressedResourceOp):
return op_type
if not issubclass(op_type, Operator):
raise TypeError(
"The keys of the dictionary returned by the resource function must be a subclass of "
"Operator or a CompressedResourceOp constructed with qml.resource_rep"
)
try:
return resource_rep(op_type)
except TypeError as e:
raise TypeError(
f"Operator {op_type.__name__} has non-empty resource_keys. A resource "
f"representation must be explicitly constructed using qml.resource_rep"
) from e
_decompositions = defaultdict(list)
"""dict[str, list[DecompositionRule]]: A dictionary mapping operator names to decomposition rules."""
[docs]
def add_decomps(op_type: Type[Operator] | str, *decomps: DecompositionRule) -> None:
"""Globally registers new decomposition rules with an operator class.
.. note::
This function is only relevant when the new experimental graph-based decomposition system
(introduced in v0.41) is enabled via :func:`~pennylane.decomposition.enable_graph`. This new way of
doing decompositions is generally more resource efficient and accommodates multiple alternative
decomposition rules for an operator. In this new system, custom decomposition rules are
defined as quantum functions, and it is currently required that every decomposition rule
declares its required resources using :func:`~pennylane.register_resources`
In the new system of decompositions, multiple decomposition rules can be registered for the
same operator class. The specified decomposition rules in ``add_decomps`` serve as alternative
decomposition rules that may be chosen if they lead to a more resource-efficient decomposition.
Args:
op_type (type or str): the operator type for which new decomposition rules are specified.
For symbolic operators, use strings such as ``"Adjoint(RY)"``, ``"Pow(H)"``, ``"C(RX)"``, etc.
decomps (DecompositionRule): new decomposition rules to add to the given ``op_type``.
A decomposition is a quantum function registered with a resource estimate using
``qml.register_resources``.
.. seealso:: :func:`~pennylane.register_resources` and :class:`~pennylane.list_decomps`
**Example**
This example demonstrates adding two new decomposition rules to the ``qml.Hadamard`` operator.
.. code-block:: python
import pennylane as qml
import numpy as np
@qml.register_resources({qml.RZ: 2, qml.RX: 1, qml.GlobalPhase: 1})
def my_hadamard1(wires):
qml.RZ(np.pi / 2, wires=wires)
qml.RX(np.pi / 2, wires=wires)
qml.RZ(np.pi / 2, wires=wires)
qml.GlobalPhase(-np.pi / 2, wires=wires)
@qml.register_resources({qml.RZ: 1, qml.RY: 1, qml.GlobalPhase: 1})
def my_hadamard2(wires):
qml.RZ(np.pi, wires=wires)
qml.RY(np.pi / 2, wires=wires)
qml.GlobalPhase(-np.pi / 2)
qml.add_decomps(qml.Hadamard, my_hadamard1, my_hadamard2)
These two new decomposition rules for ``qml.Hadamard`` will be subsequently stored within the
scope of this program, and they will be taken into account for all circuit decompositions
for the duration of the session. To add alternative decompositions for a particular circuit
as opposed to globally, use the ``alt_decomps`` argument of the :func:`~pennylane.transforms.decompose` transform.
Custom decomposition rules can also be specified for symbolic operators. In this case, the
operator type can be specified as a string. For example,
.. code-block:: python
@register_resources({qml.RY: 1})
def adjoint_ry(phi, wires, **_):
qml.RY(-phi, wires=wires)
qml.add_decomps("Adjoint(RY)", adjoint_ry)
.. seealso:: :func:`~pennylane.transforms.decompose`
"""
if not all(isinstance(d, DecompositionRule) for d in decomps):
raise TypeError(
"A decomposition rule must be a qfunc with a resource estimate "
"registered using qml.register_resources"
)
if isinstance(op_type, type):
op_type = op_type.__name__
_decompositions[translate_op_alias(op_type)].extend(decomps)
[docs]
def list_decomps(op_type: Type[Operator] | str) -> list[DecompositionRule]:
"""Lists all stored decomposition rules for an operator class.
.. note::
This function is only relevant when the new experimental graph-based decomposition system
(introduced in v0.41) is enabled via :func:`~pennylane.decomposition.enable_graph`. This new way of
doing decompositions is generally more resource efficient and accommodates multiple alternative
decomposition rules for an operator.
Args:
op_type (type or str): the operator class to retrieve decomposition rules for. For symbolic
operators, use strings such as ``"Adjoint(RY)"``, ``"Pow(H)"``, ``"C(RX)"``, etc.
Returns:
list[DecompositionRule]: a list of decomposition rules registered for the given operator.
**Example**
>>> import pennylane as qml
>>> qml.list_decomps(qml.CRX)
[<pennylane.decomposition.decomposition_rule.DecompositionRule at 0x136da9de0>,
<pennylane.decomposition.decomposition_rule.DecompositionRule at 0x136da9db0>,
<pennylane.decomposition.decomposition_rule.DecompositionRule at 0x136da9f00>]
Each decomposition rule can be inspected:
>>> print(qml.list_decomps(qml.CRX)[0])
@register_resources(_crx_to_rx_cz_resources)
def _crx_to_rx_cz(phi, wires, **__):
qml.RX(phi / 2, wires=wires[1]),
qml.CZ(wires=wires),
qml.RX(-phi / 2, wires=wires[1]),
qml.CZ(wires=wires),
>>> print(qml.draw(qml.list_decomps(qml.CRX)[0])(0.5, wires=[0, 1]))
0: ───────────╭●────────────╭●─┤
1: ──RX(0.25)─╰Z──RX(-0.25)─╰Z─┤
"""
if isinstance(op_type, type):
op_type = op_type.__name__
return _decompositions[translate_op_alias(op_type)][:]
[docs]
def has_decomp(op_type: Type[Operator] | str) -> bool:
"""Checks whether an operator has decomposition rules defined.
.. note::
This function is only relevant when the new experimental graph-based decomposition system
(introduced in v0.41) is enabled via :func:`~pennylane.decomposition.enable_graph`. This new way of
doing decompositions is generally more resource efficient and accommodates multiple alternative
decomposition rules for an operator.
Args:
op_type (type or str): the operator class to check for decomposition rules. For symbolic
operators, use strings such as ``"Adjoint(RY)"``, ``"Pow(H)"``, ``"C(RX)"``, etc.
Returns:
bool: whether decomposition rules are defined for the given operator.
"""
if isinstance(op_type, type):
op_type = op_type.__name__
op_type = translate_op_alias(op_type)
return op_type in _decompositions and len(_decompositions[op_type]) > 0
@register_resources({})
def null_decomp(*_, **__):
"""A decomposition rule that does nothing."""
return
_modules/pennylane/decomposition/decomposition_rule
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