Empirical Learning of Dynamical Decoupling on Quantum Processors
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 empiri...
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| Format: | Article |
| Language: | English |
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American Physical Society
2025-08-01
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| Series: | PRX Quantum |
| Online Access: | http://doi.org/10.1103/h7pq-s159 |
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| author | Christopher Tong Helena Zhang Bibek Pokharel |
| author_facet | Christopher Tong Helena Zhang Bibek Pokharel |
| author_sort | Christopher Tong |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-325e1bde4e8441d6883bc67450f4d51e |
| institution | Kabale University |
| issn | 2691-3399 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | American Physical Society |
| record_format | Article |
| series | PRX Quantum |
| spelling | doaj-art-325e1bde4e8441d6883bc67450f4d51e2025-08-20T03:34:10ZengAmerican Physical SocietyPRX Quantum2691-33992025-08-016303031910.1103/h7pq-s159Empirical Learning of Dynamical Decoupling on Quantum ProcessorsChristopher TongHelena ZhangBibek PokharelDynamical 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.http://doi.org/10.1103/h7pq-s159 |
| spellingShingle | Christopher Tong Helena Zhang Bibek Pokharel Empirical Learning of Dynamical Decoupling on Quantum Processors PRX Quantum |
| title | Empirical Learning of Dynamical Decoupling on Quantum Processors |
| title_full | Empirical Learning of Dynamical Decoupling on Quantum Processors |
| title_fullStr | Empirical Learning of Dynamical Decoupling on Quantum Processors |
| title_full_unstemmed | Empirical Learning of Dynamical Decoupling on Quantum Processors |
| title_short | Empirical Learning of Dynamical Decoupling on Quantum Processors |
| title_sort | empirical learning of dynamical decoupling on quantum processors |
| url | http://doi.org/10.1103/h7pq-s159 |
| work_keys_str_mv | AT christophertong empiricallearningofdynamicaldecouplingonquantumprocessors AT helenazhang empiricallearningofdynamicaldecouplingonquantumprocessors AT bibekpokharel empiricallearningofdynamicaldecouplingonquantumprocessors |