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DynamicalDecoupling

class qiskit.transpiler.passes.DynamicalDecoupling(*args, **kwargs)

GitHub(opens in a new tab)

Bases: TransformationPass

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 ALAPSchedule, DynamicalDecoupling
from qiskit.visualization import timeline_drawer
 
# Because the legacy passes do not propagate the scheduling information correctly, it is
# necessary to run a no-op "re-schedule" before the output circuits can be drawn.
def draw(circuit):
    from qiskit import transpile
 
    scheduled = transpile(
        circuit,
        optimization_level=0,
        instruction_durations=InstructionDurations(),
        scheduling_method="alap",
    )
    return timeline_drawer(scheduled)
 
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([ALAPSchedule(durations),
                  DynamicalDecoupling(durations, dd_sequence)])
circ_dd = pm.run(circ)
draw(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(
    [
        ALAPSchedule(durations),
        DynamicalDecoupling(durations, dd_sequence, qubits=[0], spacing=spacing),
    ]
)
circ_dd = pm.run(circ)
draw(circ_dd)
../_images/qiskit-transpiler-passes-DynamicalDecoupling-1_00.png../_images/qiskit-transpiler-passes-DynamicalDecoupling-1_01.png

Dynamical decoupling initializer.

Deprecated since version 0.21.0_pending

The class qiskit.transpiler.passes.scheduling.dynamical_decoupling.DynamicalDecoupling is pending deprecation as of qiskit 0.21.0. It will be marked deprecated in a future release, and then removed no earlier than 3 months after the release date. Instead, use PadDynamicalDecoupling, which performs the same function but requires scheduling and alignment analysis passes to run prior to it.

Parameters

  • durations (InstructionDurations) – Durations of instructions to be used in scheduling.
  • dd_sequence (list(opens in a new tab)[Gate]) – sequence of gates to apply in idle spots.
  • qubits (list(opens in a new tab)[int(opens in a new tab)]) – physical qubits on which to apply DD. If None, all qubits will undergo DD (when possible).
  • spacing (list(opens in a new tab)[float(opens in a new tab)]) – 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 (bool(opens in a new tab)) – 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).
  • target (Target) – The Target representing the target backend, if both durations and this are specified then this argument will take precedence and durations will be ignored.

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)

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()

Name of the pass.

Return type

str(opens in a new tab)

run

run(dag)

Run the DynamicalDecoupling pass on dag.

Parameters

dag (DAGCircuit) – a scheduled DAG.

Returns

equivalent circuit with delays interrupted by DD,

where possible.

Return type

DAGCircuit

Raises

TranspilerError – if the circuit is not mapped on physical qubits.

update_status

update_status(state, run_state)

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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