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Cited by 130Quantum Computing Algorithms and ArchitectureQuantum Information and CryptographyReinforcement Learning in Robotics

Model-Free Quantum Control with Reinforcement Learning

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Sivak, V. V., Eickbusch, A., Liu, H., Royer, B., Tsioutsios, I., Devoret, M. H.·2022·Physical Review X
DOI 10.1103/physrevx.12.011059
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Abstract

A machine-learning agent learns how to control a quantum system---a key element of any quantum technology without any prior knowledge---by studying how a real quantum system responds to various actions.

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  • The optimized GKP ququart achieved beyond break-even error correction with a gain of 1.87 ± 0.03.

    “Using a reinforcement learning agent21,22, we optimized the Gottesman–Kitaev–Preskill qutrit (ququart) as a ternary (quaternary) quantum memory and achieved beyond break-even error correction with a gain of 1.82 ± 0.03 (1.87 ± 0.03).”

    From Quantum error correction of qudits beyond break-even (2025) · cites this paper as [21]

    Section B, “Preparation of stabilizer states” (page unavailable), describes training a reinforcement-learning agent to prepare a GKP grid state. It does not report an optimized GKP ququart memory, beyond break-even error correction, or a gain of 1.87 ± 0.03. (LLM, conf=0.86)

    Experimental — accuracy not yet measured

  • The optimized GKP qutrit achieved beyond break-even error correction with a gain of 1.82 ± 0.03.

    “Using a reinforcement learning agent21,22, we optimized the Gottesman–Kitaev–Preskill qutrit (ququart) as a ternary (quaternary) quantum memory and achieved beyond break-even error correction with a gain of 1.82 ± 0.03 (1.87 ± 0.03).”

    From Quantum error correction of qudits beyond break-even (2025) · cites this paper as [21]

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Cited by

Sorted by citations

Authors
Sivak, V. V., Eickbusch, A., Liu, H., Royer, B., Tsioutsios, I., & Devoret, M. H.
Affiliations
Yale University (US)
ORCID
Venue
Physical Review X
Publisher
American Physical Society (APS)
Published
2022-03-28
Volume
12
Issue
1
ISSN
2160-3308 (ISSN-L)
DOI
10.1103/physrevx.12.011059
OpenAlex
W3158332310
Cited by
130
References
10
Citations by year
2026: 22 · 2025: 45 · 2024: 24 · 2023: 25 · 2022: 12 · 2021: 2
FWCI
8.72
Type
journal article
Language
en
Open access
Licence
Locations
  • Physical Review X (publishedVersion, cc-by)
  • arXiv (Cornell University) (submittedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
  • arXiv (Cornell University)
Funders
Army Research Office (W911NF-18-1-0212)
Topics
  • Physical Sciences › Computer Science › Artificial Intelligence › Quantum Computing Algorithms and Architecture
  • Physical Sciences › Computer Science › Artificial Intelligence › Quantum Information and Cryptography
  • Physical Sciences › Computer Science › Artificial Intelligence › Reinforcement Learning in Robotics
Keywords
reinforcement learning, quantum control, machine learning agents, quantum systems

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