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  • Open Access

Empirical Learning of Dynamical Decoupling on Quantum Processors

Christopher Tong1,2,*, Helena Zhang2,†, and Bibek Pokharel2,‡

  • *Contact author: lctong@mit.edu
  • †Contact author: helena.zhang@ibm.com
  • ‡Contact author: bibek.pokharel@ibm.com
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PRX Quantum 6, 030319 – Published 1 August, 2025

DOI: https://doi.org/10.1103/h7pq-s159

Abstract

Dynamical decoupling (DD) is a low-overhead method for quantum error suppression. Despite extensive work in DD design, finding pulse sequences that optimally decouple computational qubits is not well understood. Using IBM’s superconducting-qubit-based quantum processors, we demonstrate how to empirically tailor DD strategies for an arbitrary quantum circuit and device. These learned DD strategies significantly improve error suppression relative to canonical sequences, with relative improvement increasing with problem size and circuit sophistication. We leverage this to study mirror randomized benchmarking on 100 qubits, Greenberger-Horne-Zeilinger state preparation on 50 qubits, and the Bernstein-Vazirani algorithm on 27 qubits. Our empirical learning method finds strategies, in constant time independent of circuit width and depth, provides stable performance over long time periods without retraining, and generalizes to larger circuits when trained on small subcircuit structures.

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Popular Summary

When information leaks from the quantum computer to the environment, it is not irretrievably lost. It is possible to recover information lost due to the system-environment coupling by applying a sequence of carefully chosen control pulses, called dynamical decoupling (DD) pulses, that aim to cancel the effect of noise on the device.

Dynamical decoupling pulses are frequently used and demonstrably effective in suppressing errors in programmable quantum computers. Nonetheless, there is a gap between the theory and experimental implementation of DD sequences, with most theoretically sophisticated DD sequences often poorly suited for the resource constraints imposed by current quantum computers. Central to this mismatch is the dual requirement to first accurately characterize the quantum computer’s error sources and then to design a DD strategy that works well for those errors and the quirks of the device at hand.

Here, we remedy these issues by providing an empirical framework to perform a real-time search of possible DD sequences tailored to the device’s imperfections and the quantum task and to do so in a scalable manner.

We show that it is possible to empirically train DD sequences on a simplified version of the quantum circuits to produce nontrivial and previously unseen pulse sequences that are effectively generalized to deep quantum circuits whose outcomes are unknown a priori. We not only provide a training procedure but also demonstrate its efficacy on a randomized benchmarking experiment (100-qubit mirror randomized benchmarking), a state preparation circuit (50-qubit Greenberger-Horne-Zeilinger state preparation), and an oracular quantum algorithm (27-qubit Bernstein-Vazirani algorithm).

Article Text

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PRX Quantum 6, 030319– Published 1 August, 2025
  • Received 20 November 2024
  • Revised 28 March 2025
  • Accepted 22 May 2025
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