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PadDynamicalDecoupling

qiskit.transpiler.passes.PadDynamicalDecoupling(*args, **kwargs) GitHub(opens in a new tab)

Bases: BasePadding

Dynamical decoupling insertion pass.

This pass works on a scheduled, physical circuit. It scans the circuit for idle periods of time (i.e. those containing delay instructions) and inserts a DD sequence of gates in those spots. These gates amount to the identity, so do not alter the logical action of the circuit, but have the effect of mitigating decoherence in those idle periods.

As a special case, the pass allows a length-1 sequence (e.g. [XGate()]). In this case the DD insertion happens only when the gate inverse can be absorbed into a neighboring gate in the circuit (so we would still be replacing Delay with something that is equivalent to the identity). This can be used, for instance, as a Hahn echo.

This pass ensures that the inserted sequence preserves the circuit exactly (including global phase).

import numpy as np
from qiskit.circuit import QuantumCircuit
from qiskit.circuit.library import XGate
from qiskit.transpiler import PassManager, InstructionDurations
from qiskit.transpiler.passes import ALAPScheduleAnalysis, PadDynamicalDecoupling
from qiskit.visualization import timeline_drawer
circ = QuantumCircuit(4)
circ.h(0)
circ.cx(0, 1)
circ.cx(1, 2)
circ.cx(2, 3)
circ.measure_all()
durations = InstructionDurations(
    [("h", 0, 50), ("cx", [0, 1], 700), ("reset", None, 10),
     ("cx", [1, 2], 200), ("cx", [2, 3], 300),
     ("x", None, 50), ("measure", None, 1000)]
)
 
# balanced X-X sequence on all qubits
dd_sequence = [XGate(), XGate()]
pm = PassManager([ALAPScheduleAnalysis(durations),
                  PadDynamicalDecoupling(durations, dd_sequence)])
circ_dd = pm.run(circ)
timeline_drawer(circ_dd)
 
# Uhrig sequence on qubit 0
n = 8
dd_sequence = [XGate()] * n
def uhrig_pulse_location(k):
    return np.sin(np.pi * (k + 1) / (2 * n + 2)) ** 2
spacing = []
for k in range(n):
    spacing.append(uhrig_pulse_location(k) - sum(spacing))
spacing.append(1 - sum(spacing))
pm = PassManager(
    [
        ALAPScheduleAnalysis(durations),
        PadDynamicalDecoupling(durations, dd_sequence, qubits=[0], spacing=spacing),
    ]
)
circ_dd = pm.run(circ)
timeline_drawer(circ_dd)
../_images/qiskit-transpiler-passes-PadDynamicalDecoupling-1_00.png../_images/qiskit-transpiler-passes-PadDynamicalDecoupling-1_01.png
Note

You may need to call alignment pass before running dynamical decoupling to guarantee your circuit satisfies acquisition alignment constraints.

Dynamical decoupling initializer.

Parameters

  • durations – Durations of instructions to be used in scheduling.

  • dd_sequence – Sequence of gates to apply in idle spots.

  • qubits – Physical qubits on which to apply DD. If None, all qubits will undergo DD (when possible).

  • spacing – A list of spacings between the DD gates. The available slack will be divided according to this. The list length must be one more than the length of dd_sequence, and the elements must sum to 1. If None, a balanced spacing will be used [d/2, d, d, …, d, d, d/2].

  • skip_reset_qubits – If True, does not insert DD on idle periods that immediately follow initialized/reset qubits (as qubits in the ground state are less susceptile to decoherence).

  • pulse_alignment – The hardware constraints for gate timing allocation. This is usually provided from backend.configuration().timing_constraints. If provided, the delay length, i.e. spacing, is implicitly adjusted to satisfy this constraint.

  • extra_slack_distribution

    The option to control the behavior of DD sequence generation. The duration of the DD sequence should be identical to an idle time in the scheduled quantum circuit, however, the delay in between gates comprising the sequence should be integer number in units of dt, and it might be further truncated when pulse_alignment is specified. This sometimes results in the duration of the created sequence being shorter than the idle time that you want to fill with the sequence, i.e. extra slack. This option takes following values.

    • ”middle”: Put the extra slack to the interval at the middle of the sequence.
    • ”edges”: Divide the extra slack as evenly as possible into intervals at beginning and end of the sequence.
  • target – The Target representing the target backend. Target takes precedence over other arguments when they can be inferred from target. Therefore specifying target as well as other arguments like durations or pulse_alignment will cause those other arguments to be ignored.

Raises


Attributes

is_analysis_pass

Check if the pass is an analysis pass.

If the pass is an AnalysisPass, that means that the pass can analyze the DAG and write the results of that analysis in the property set. Modifications on the DAG are not allowed by this kind of pass.

is_transformation_pass

Check if the pass is a transformation pass.

If the pass is a TransformationPass, that means that the pass can manipulate the DAG, but cannot modify the property set (but it can be read).


Methods

execute

execute(passmanager_ir, state, callback=None) GitHub(opens in a new tab)

Execute optimization task for input Qiskit IR.

Parameters

Returns

Optimized Qiskit IR and state of the workflow.

Return type

tuple(opens in a new tab)[Any(opens in a new tab), qiskit.passmanager.compilation_status.PassManagerState]

name

name() GitHub(opens in a new tab)

Name of the pass.

Return type

str(opens in a new tab)

run

run(dag) GitHub(opens in a new tab)

Run the padding pass on dag.

Parameters

dag (DAGCircuit) – DAG to be checked.

Returns

DAG with idle time filled with instructions.

Return type

DAGCircuit

Raises

TranspilerError – When a particular node is not scheduled, likely some transform pass is inserted before this node is called.

update_status

update_status(state, run_state) GitHub(opens in a new tab)

Update workflow status.

Parameters

  • state (PassManagerState) – Pass manager state to update.
  • run_state (RunState) – Completion status of current task.

Returns

Updated pass manager state.

Return type

PassManagerState

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