iqm.iqm_client.models.DDStrategy

iqm.iqm_client.models.DDStrategy#

class iqm.iqm_client.models.DDStrategy(*, merge_contiguous_waits: bool = True, target_qubits: frozenset[str] | None = None, skip_leading_wait: bool = True, skip_trailing_wait: bool = True, gate_sequences: list[tuple[int, str | list[tuple[float, float]], str]] = <factory>)#

Bases: BaseModel

Describes a particular dynamical decoupling strategy.

The current standard DD stategy can be found in STANDARD_DD_STRATEGY, but users can use this class to provide their own dynamical decoupling strategies.

See Ezzell et al., Phys. Rev. Appl. 20, 064027 (2022) for information on DD sequences.

Attributes

model_config

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

merge_contiguous_waits

Merge contiguous Wait instructions into one if they are separated only by Block instructions.

target_qubits

Qubits on which dynamical decoupling should be applied.

skip_leading_wait

Skip processing leading Wait instructions.

skip_trailing_wait

Skip processing trailing Wait instructions.

gate_sequences

Available decoupling gate sequences to chose from in this strategy.

Methods

Parameters:
merge_contiguous_waits: bool#

Merge contiguous Wait instructions into one if they are separated only by Block instructions.

target_qubits: frozenset[str] | None#

Qubits on which dynamical decoupling should be applied. If None, all qubits are targeted.

skip_leading_wait: bool#

Skip processing leading Wait instructions.

skip_trailing_wait: bool#

Skip processing trailing Wait instructions.

gate_sequences: list[tuple[int, str | PRXSequence, str]]#

Available decoupling gate sequences to chose from in this strategy.

Each sequence is defined by a tuple of (ratio, gate pattern, align):

  • ratio: Minimal duration for the sequence (in PRX gate durations) as integer.

  • gate pattern: Gate pattern can be defined in two ways. It can be a string containing “X” and “Y” characters, encoding a PRX gate sequence. For example, “YXYX” corresponds to the XY4 sequence, “XYXYYXYX” to the EDD sequence, etc. If more flexibility is needed, a gate pattern can be defined as a sequence of PRX gate argument tuples (that contain a rotation angle and a phase angle). For example, sequence “YX” could be written as [(math.pi, math.pi / 2), (math.pi, 0)].

  • align: Controls the alignment of the sequence within the time window it is inserted in. Supported values:

    • “asap”: Corresponds to a ASAP-aligned sequence with no waiting time before the first pulse.

    • “center”: Corresponds to a symmetric sequence.

    • “alap”: Corresponds to a ALAP-aligned sequence.

The Dynamical Decoupling algorithm uses the best fitting gate sequence by first sorting them by ratio in descending order. Then the longest fitting pattern is determined by comparing ratio with the duration of the time window divided by the PRX gate duration.

model_config: ClassVar[ConfigDict] = {}#

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

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