qml.transforms.core.Transform¶

class Transform(tape_transform=None, pass_name=None, *, expand_transform=None, classical_cotransform=None, is_informative=False, final_transform=False, use_argnum_in_expand=False, plxpr_transform=None)[source]¶

Bases: object

Generalizes a function that transforms tapes to work with additional circuit-like objects such as a QNode.

transform should be applied to a function that transforms tapes. Once validated, the result will be an object that is able to transform PennyLane’s range of circuit-like objects: QuantumTape, quantum function and QNode. A circuit-like object can be transformed either via decoration or by passing it functionally through the created transform.

Parameters:

tape_transform (Callable | None) –
The input quantum transform must be a function that satisfies the following requirements:
- Accepts a QuantumScript as its first input and returns a sequence of QuantumScript and a processing function.
- The transform must have the following structure (type hinting is optional): my_tape_transform(tape: qml.tape.QuantumScript, ...) -> tuple[qml.tape.QuantumScriptBatch, qml.typing.PostprocessingFn]
pass_name (str | None) – the name of the associated MLIR pass to be applied when Catalyst is used. See Usage Details for more information.

Keyword Arguments:

expand_transform=None (Optional[Callable]) – An optional transform that is applied directly before the input transform. It must be a function that satisfies the same requirements as tape_transform.
classical_cotransform=None (Optional[Callable]) – A classical co-transform is a function to post-process the classical jacobian and the quantum jacobian and has the signature: my_cotransform(qjac, cjac, tape) -> tensor_like
is_informative=False (bool) – Whether or not a transform is informative. If true, the transform is queued at the end of the compile pipeline and the tapes or qnode aren’t executed.
final_transform=False (bool) – Whether or not the transform is terminal. If true, the transform is queued at the end of the compile pipeline. is_informative supersedes final_transform.
use_argnum_in_expand=False (bool) – Whether to use argnum of the tape to determine trainable parameters during the expansion transform process.
plxpr_transform=None (Optional[Callable]) – Function for transforming plxpr. Experimental

Example

First define an input tape transform with the necessary structure defined above. In this example, we copy the tape and sum the results of the execution of the two tapes.

from pennylane.tape import QuantumScript, QuantumScriptBatch
from pennylane.typing import PostprocessingFn

def my_quantum_transform(tape: QuantumScript) -> tuple[QuantumScriptBatch, PostprocessingFn]:
    tape1 = tape
    tape2 = tape.copy()

    def post_processing_fn(results):
        return qml.math.sum(results)

    return [tape1, tape2], post_processing_fn

We want to be able to apply this transform on both a qfunc and a pennylane.QNode and will use transform to achieve this. transform validates the signature of your input quantum transform and makes it capable of transforming qfunc and pennylane.QNode in addition to quantum tapes. Let’s define a circuit as a pennylane.QNode:

dev = qml.device("default.qubit")

@qml.qnode(device=dev)
def qnode_circuit(a):
    qml.Hadamard(wires=0)
    qml.CNOT(wires=[0, 1])
    qml.X(0)
    qml.RZ(a, wires=1)
    return qml.expval(qml.Z(0))

We first apply transform to my_quantum_transform:

>>> dispatched_transform = qml.transform(my_quantum_transform)

Now you can use the dispatched transform directly on a pennylane.QNode.

For pennylane.QNode, the dispatched transform populates the CompilePipeline of your QNode. The transform and its processing function are applied in the execution.

>>> transformed_qnode = dispatched_transform(qnode_circuit)
>>> transformed_qnode
<QNode: device='<default.qubit device at ...>', interface='auto', diff_method='best', shots='Shots(total=None)'>

>>> transformed_qnode.transform_program
CompilePipeline(my_quantum_transform)

If we apply dispatched_transform a second time to the pennylane.QNode, we would add it to the compile pipeline again and therefore the transform would be applied twice before execution.

>>> transformed_qnode = dispatched_transform(transformed_qnode)
>>> transformed_qnode.transform_program
CompilePipeline(my_quantum_transform, my_quantum_transform)

When a transformed QNode is executed, the QNode’s compile pipeline is applied to the generated tape and creates a sequence of tapes to be executed. The execution results are then post-processed in the reverse order of the compile pipeline to obtain the final results.

Dispatch a transform onto a batch of tapes

We can compose multiple transforms when working in the tape paradigm and apply them to more than one tape. The following example demonstrates how to apply a transform to a batch of tapes.

Example

In this example, we apply sequentially a transform to a tape and another one to a batch of tapes. We then execute the transformed tapes on a device and post-process the results.

import pennylane as qml

H = qml.PauliY(2) @ qml.PauliZ(1) + 0.5 * qml.PauliZ(2) + qml.PauliZ(1)
measurement = [qml.expval(H)]
operations = [qml.Hadamard(0), qml.RX(0.2, 0), qml.RX(0.6, 0), qml.CNOT((0, 1))]
tape = qml.tape.QuantumTape(operations, measurement)

batch1, function1 = qml.transforms.split_non_commuting(tape)
batch2, function2 = qml.transforms.merge_rotations(batch1)

dev = qml.device("default.qubit", wires=3)
result = dev.execute(batch2)

The first split_non_commuting transform splits the original tape, returning a batch of tapes batch1 and a processing function function1. The second merge_rotations transform is applied to the batch of tapes returned by the first transform. It returns a new batch of tapes batch2, each of which has been transformed by the second transform, and a processing function function2.

>>> batch2
(<QuantumTape: wires=[0, 1, 2], params=1>, <QuantumTape: wires=[0, 1, 2], params=1>)

>>> type(function2)
<class 'function'>

We can combine the processing functions to post-process the results of the execution.

>>> function1(function2(result))
np.float64(0.499...)

Signature of a transform

A dispatched transform is able to handle several PennyLane circuit-like objects:

pennylane.QNode
a quantum function (callable)
pennylane.tape.QuantumScript
a batch of pennylane.tape.QuantumScript
pennylane.devices.Device.

For each object, the transform will be applied in a different way, but it always preserves the underlying tape-based quantum transform behaviour.

The return of a dispatched transform depends upon which of the above objects is passed as an input:

For a QNode input, the underlying transform is added to the QNode’s CompilePipeline and the return is the transformed QNode. For each execution of the pennylane.QNode, it first applies the compile pipeline on the original captured circuit. Then the transformed circuits are executed by a device and finally the post-processing function is applied on the results.

When experimental program capture is enabled, transforming a QNode returns a new function to which the transform has been added as a higher-order primitive.
For a quantum function (callable) input, the transform builds the tape when the quantum function is executed and then applies itself to the tape. The resulting tape is then converted back to a quantum function (callable). It therefore returns a transformed quantum function (Callable). The limitation is that the underlying transform can only return a sequence containing a single tape, because quantum functions only support a single circuit.

When experimental program capture is enabled, transforming a function (callable) returns a new function to which the transform has been added as a higher-order primitive.
For a QuantumScript. It returns a sequence of QuantumScript and a processing function to be applied after execution.
For a batch of pennylane.tape.QuantumScript, the quantum transform is mapped across all the tapes. It returns a sequence of QuantumScript and a processing function to be applied after execution. Each tape in the sequence is transformed by the transform.
For a Device, the transform is added to the device’s compile pipeline and a transformed pennylane.devices.Device is returned. The transform is added to the end of the device program and will be last in the overall compile pipeline.

Transforms with Catalyst

If a compilation pass is written in MLIR, using it in a qjit’d workflow requires that it have a transform with a matching pass_name. This ensures that the transform is properly applied as part of the lower-level compilation.

For example, we can create a transform that will apply the cancel-inverses pass, like the in-built qml.transforms.cancel_inverses transform.

my_transform = qml.transform(pass_name="cancel-inverses")

@qml.qjit
@my_transform
@qml.qnode(qml.device('lightning.qubit', wires=4))
def circuit():
    qml.X(0)
    qml.X(0)
    return qml.expval(qml.Z(0))

We can see that the instruction to apply "cancel-inverses" is present in the initial MLIR.

>>> circuit()
Array(1., dtype=float64)
>>> print(circuit.mlir[200:600])
tensor<f64>
}
module @module_circuit {
    module attributes {transform.with_named_sequence} {
    transform.named_sequence @__transform_main(%arg0: !transform.op<"builtin.module">) {
        %0 = transform.apply_registered_pass "cancel-inverses" to %arg0 : (!transform.op<"builtin.module">) -> !transform.op<"builtin.module">
        transform.yield
    }
    }
    func.func public @circui

Transforms can have both tape-based and pass_name-based definitions. For example, the transform below called my_transform has both definitions. In this case, the MLIR pass will take precedence when being qjit’d if only MLIR passes can occur after.

from functools import partial

@partial(qml.transform, pass_name="my-pass-name")
def my_transform(tape):
    return (tape, ), lambda res: res[0]

Note that any transform with only a pass_name definition must occur after any purely tape-based transform, as tape transforms occur prior to lowering to MLIR.

>>> @qml.qjit
... @qml.defer_measurements
... @qml.transform(pass_name="cancel-inverses")
... @qml.qnode(qml.device('lightning.qubit', wires=4))
... def c():
...     qml.X(0)
...     qml.X(0)
...     return qml.expval(qml.Z(0))
...
Traceback (most recent call last):
    ...
ValueError: <cancel-inverses((), {})> without a tape definition occurs before tape transform <defer_measurements((), {})>.

Transforms with experimental program capture

To define a transform that can be applied directly to plxpr without the need to create QuantumScripts, users must provide the plxpr_transform argument. If this argument is not provided, executing transformed functions is not guaranteed to work. More details about this are provided below. The plxpr_transform argument should be a function that applies the respective transform to jax.extend.core.Jaxpr and returns a transformed jax.extend.core.ClosedJaxpr. plxpr_transform can assume that no transform primitives are present in the input plxpr, and its implementation does not need to account for these primitives. The exact expected signature of plxpr_transform is shown in the example below:

def dummy_plxpr_transform(
    jaxpr: jax.extend.core.Jaxpr, consts: list, targs: list, tkwargs: dict, *args
) -> jax.extend.core.ClosedJaxpr:
    ...

Once the plxpr_transform argument is provided, the transform can be easily used with program capture enabled! To do so, apply the transform as you normally would:

qml.capture.enable()

@qml.transforms.cancel_inverses
def circuit():
    qml.X(0)
    qml.S(1)
    qml.X(0)
    qml.adjoint(qml.S(1))
    return qml.expval(qml.Z(1))

>>> jax.make_jaxpr(circuit)()
{ lambda ; . let
    a:AbstractMeasurement(n_wires=None) = transform[
    args_slice=(0, 0, None)
    consts_slice=(0, 0, None)
    inner_jaxpr={ lambda ; . let
        _:AbstractOperator() = PauliX[n_wires=1] 0:i...[]
        _:AbstractOperator() = S[n_wires=1] 1:i...[]
        _:AbstractOperator() = PauliX[n_wires=1] 0:i...[]
        b:AbstractOperator() = S[n_wires=1] 1:i...[]
        _:AbstractOperator() = Adjoint b
        c:AbstractOperator() = PauliZ[n_wires=1] 1:i...[]
        d:AbstractMeasurement(n_wires=None) = expval_obs c
      in (d,) }
    targs_slice=(0, None, None)
    tkwargs=()
    transform=<transform: cancel_inverses>
  ]
in (a,) }

As shown, the transform gets applied as a higher-order primitive, with the jaxpr representation of the function being transformed stored in the inner_jaxpr parameter of the transform’s primitive.

Fallback implementation of plxpr transforms:

If a transform that does not define a plxpr_transform is applied to a function, a fallback implementation of the transform is used. This fallback implementation converts the function into a QuantumScript(), which is then transformed as a traditional tape. However, because of the constraints of program capture, many transforms will not be compatible with this fallback implementation:

Transforms that return multiple tapes are not compatible.
Transforms that require non-trivial post-processing of results are not compatible.
Dynamically shaped arrays are not compatible.
Functions that are being transformed that contain control flow dependent on dynamic parameters are not compatible. This includes:
- pennylane.cond() with dynamic parameters as predicates.
- pennylane.for_loop() with dynamic parameters for start, stop, or step.
- pennylane.while_loop() does not work.

Warning

Currently, executing a function to which a transform has been applied will raise a NotImplementedError. See below for details on how to use functions that are transformed.

To perform the transform, the pennylane.capture.expand_plxpr_transforms() function should be used. This function accepts a function to which transforms have been applied as an input, and returns a new function that has been transformed:

>>> transformed_circuit = qml.capture.expand_plxpr_transforms(circuit)
>>> jax.make_jaxpr(transformed_circuit)()
{ lambda ; . let
    a:AbstractOperator() = PauliZ[n_wires=1] 1:i...[]
    b:AbstractMeasurement(n_wires=None) = expval_obs a
in (b,) }

Attributes

`classical_cotransform`	The classical co-transform.
`expand_transform`	The expand transform.
`is_final_transform`	`True` if the transformed tapes must be executed.
`is_informative`	`True` if the transform is informative.
`pass_name`	The name of the equivalent MLIR pass.
`plxpr_transform`	Function for transforming plxpr.
`register`	Returns a decorator for registering a specific application behavior for a given transform and a new class.
`tape_transform`	The tape transform.

classical_cotransform¶: The classical co-transform.

expand_transform¶: The expand transform.

is_final_transform¶: True if the transformed tapes must be executed.

is_informative¶: True if the transform is informative.

pass_name¶: The name of the equivalent MLIR pass.

plxpr_transform¶: Function for transforming plxpr.

register¶

Returns a decorator for registering a specific application behavior for a given transform and a new class.

@qml.transform
def printer(tape):
    print("I have a tape: ", tape)
    return (tape, ), lambda x: x[0]

@printer.register
def _(obj: qml.operation.Operator, *targs, **tkwargs):
    print("I have an operator:", obj)
    return obj

>>> printer(qml.X(0))
I have an operator: X(0)
X(0)

tape_transform¶: The tape transform.

Methods

`custom_qnode_transform`(fn)	Register a custom QNode execution wrapper function for the batch transform.
`default_qnode_transform`(qnode, targs, tkwargs)	The default method that takes in a QNode and returns another QNode with the transform applied.
`generic_apply_transform`(obj, targs, *tkwargs)	Generic application of a transform that forms the default for all transforms.
`generic_register`(arg)	Returns a decorator for registering a default application behavior for a transform for a new class.

custom_qnode_transform(fn)[source]¶

Register a custom QNode execution wrapper function for the batch transform.

Example

@transform
def my_transform(tape, *targs, **tkwargs):
    ...
    return tapes, processing_fn

@my_transform.custom_qnode_transform
def my_custom_qnode_wrapper(self, qnode, targs, tkwargs):
    new_tkwargs = dict(tkwargs)
    new_tkwargs['shots'] = 100
    return self.generic_apply_transform(qnode, *targs, **new_tkwargs)

The custom QNode execution wrapper must have arguments self (the batch transform object), qnode (the input QNode to transform and execute), targs and tkwargs (the transform arguments and keyword arguments respectively).

It should return a QNode that accepts the same arguments as the input QNode with the transform applied.

The default generic_apply_transform() method may be called if only pre- or post-processing dependent on QNode arguments is required.

default_qnode_transform(qnode, targs, tkwargs)[source]¶: The default method that takes in a QNode and returns another QNode with the transform applied.

generic_apply_transform(obj, *targs, **tkwargs)[source]¶

Generic application of a transform that forms the default for all transforms.

Parameters:

obj – The object we want to transform
*targs – The arguments for the transform
**tkwargs – The keyword arguments for the transform.

static generic_register(arg)[source]¶

Returns a decorator for registering a default application behavior for a transform for a new class.

Given a special new class, we can register how transforms should apply to them via:

class Subroutine:

    def __repr__(self):
        return f"<Subroutine: {self.ops}>"

    def __init__(self, ops):
        self.ops = ops

from pennylane.transforms.core import Transform

@Transform.generic_register
def apply_to_subroutine(obj: Subroutine, transform, *targs, **tkwargs):
    tape = qml.tape.QuantumScript(obj.ops)
    batch, _ = transform(tape, *targs, **tkwargs)
    return Subroutine(batch[0].operations)

>>> qml.transforms.cancel_inverses(Subroutine([qml.Y(0), qml.X(0), qml.X(0)]))
<Subroutine: [Y(0)]>

The type can also be explicitly provided like:

@Transform.generic_register(Subroutine)
def apply_to_subroutine(obj: Subroutine, transform, *targs, **tkwargs):
    tape = qml.tape.QuantumScript(obj.ops)
    batch, _ = transform(tape, *targs, **tkwargs)
    return Subroutine(batch[0].operations)

qml.transforms.core.Transform¶

Dispatch a transform onto a batch of tapes

Signature of a transform

Transforms with Catalyst

Transforms with experimental program capture

Attributes

Methods

Contents