Empirical Learning of Dynamical Decoupling on Quantum Processors
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h7pq-s159.pdf
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1.7 MB
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Checksum (MD5)
c2836a58aa68e7e3c7701184f7e1c8ba
Author(s)
Tong, Christopher
Zhang, Helena
Pokharel, Bibek
Date Issued
August 1, 2025
Journal
PRX Quantum
Publisher
American Physical Society (APS)
Version
Final published version
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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DOI of Published Version
https://doi.org/10.1103/h7pq-s159
