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qml.to_bloq

to_bloq(circuit, map_ops=True, custom_mapping=None, **kwargs)[source]

Converts a PennyLane QNode, Qfunc, or Operation to the corresponding Qualtran Bloq.

Note

This class requires the latest version of Qualtran. We recommend installing the latest release via pip:

pip install qualtran
Parameters:

  • circuit (QNode| Qfunc | Operation) – a PennyLane QNode, Qfunc, or operator to be wrapped as a Qualtran Bloq.

  • map_ops (bool) – Whether to map operations to a Qualtran Bloq. Operations are wrapped as a ToBloq when False. Default is True.

  • custom_mapping (dict) – Dictionary to specify a mapping between a PennyLane operator and a Qualtran Bloq. A default mapping is used if not defined.

Returns:

The Qualtran Bloq that corresponds to the given circuit or Operation and options.

Return type:

Bloq

See also

ToBloq for the Bloq objects created when no Qualtran equivalent is found

Example

This example shows how to use qml.to_bloq:

>>> from qualtran.resource_counting.generalizers import generalize_rotation_angle
>>> op = qml.QuantumPhaseEstimation(
...     qml.RX(0.2, wires=[0]), estimation_wires=[1, 2]
... )
>>> op_as_bloq = qml.to_bloq(op)
>>> graph, sigma = op_as_bloq.call_graph(generalize_rotation_angle)
>>> sigma
{Allocate(dtype=QFxp(bitsize=2, num_frac=2, signed=False), dirty=False): 1,
Hadamard(): 4,
Controlled(subbloq=Rx(angle=0.2, eps=1e-11), ctrl_spec=CtrlSpec(qdtypes=(QBit(),), cvs=(array(1),))): 3,
And(cv1=1, cv2=1, uncompute=True): 1,
And(cv1=1, cv2=1, uncompute=False): 1,
ZPowGate(exponent=\phi, eps=1e-10): 1,
TwoBitSwap(): 1}

Some PennyLane operators don’t have a direct equivalent in Qualtran. For example, in Qualtran, there are many varieties of Quantum Phase Estimation. When qml.to_bloq is called on QuantumPhaseEstimation, a smart default is chosen.

>>> qml.to_bloq(qml.QuantumPhaseEstimation(
...     unitary=qml.RX(0.1, wires=0), estimation_wires=range(1, 5)
... ))
TextbookQPE(unitary=Rx(angle=0.1, eps=1e-11), ctrl_state_prep=RectangularWindowState(bitsize=4), qft_inv=Adjoint(subbloq=QFTTextBook(bitsize=4, with_reverse=True)))

Note that the chosen Qualtran Bloq may not be an exact equivalent. If an exact equivalent is needed, we recommend setting map_ops to False. This will wrap the input PennyLane operator as a Qualtran Bloq, enabling Qualtran functions such as decompose_bloq or call_graph, but maintaining the PennyLane decomposition definition of the operator.

>>> qml.to_bloq(qml.QuantumPhaseEstimation(
...     unitary=qml.RX(0.1, wires=0), estimation_wires=range(1, 5)
... ), map_ops=False)
ToBloq(QuantumPhaseEstimation)

Alternatively, users can provide a custom mapping that maps a PennyLane operator to a specific Qualtran Bloq. It is recommended to map operators at the high level, rather than attempt to map operators that appear in the operator’s decomposition.

>>> from qualtran.bloqs.phase_estimation import TextbookQPE
>>> from qualtran.bloqs.phase_estimation.lp_resource_state import LPResourceState
>>> op = qml.QuantumPhaseEstimation(
...         unitary=qml.RX(0.1, wires=0), estimation_wires=range(1, 5)
...     )
>>> custom_mapping = {
...     op : TextbookQPE(
...         unitary=qml.to_bloq(qml.RX(0.1, wires=0)),
...         ctrl_state_prep=LPResourceState(4),
...     )
... }
>>> qml.to_bloq(op, custom_mapping=custom_mapping)
TextbookQPE(unitary=Rx(angle=0.1, eps=1e-11), ctrl_state_prep=LPResourceState(bitsize=4), qft_inv=Adjoint(subbloq=QFTTextBook(bitsize=4, with_reverse=True)))
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