DynamicalDecoupling

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

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)

Dynamical decoupling initializer.

Parameters

• durations (InstructionDurations) – Durations of instructions to be used in scheduling.
• dd_sequence (list[Gate]) – sequence of gates to apply in idle spots.
• qubits (list[int]) – physical qubits on which to apply DD. If None, all qubits will undergo DD (when possible).
• spacing (list[float]) – 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) – if True, does not insert DD on idle periods that immediately follow initialized/reset qubits (as qubits in the ground state are less susceptible 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

is_transformation_pass

Methods

execute

name

run

update_status