Reinforcement-learning-assisted quantum optimization
Open accessAbstract
We propose a reinforcement learning (RL) scheme for feedback quantum control within the quantum approximate optimization algorithm (QAOA).We reformulate the QAOA variational minimization as a learning task, where an RL agent chooses the control parameters for the unitaries, given partial information on the system.Such an RL scheme finds a policy converging to the optimal adiabatic solution of the quantum Ising chain that can also be successfully transferred between systems with different sizes, even in the presence of disorder.This allows for immediate experimental verification of our proposal on more complicated models: the RL agent is trained on a small control system, simulated on classical hardware, and then tested on a larger physical sample.
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The experimental results of machine learning approaches for parameter optimization suggest a high degree of transferability and robustness to noise as well as an increased solution quality.
“The experimental results suggest a high degree of transferability and robustness to noise as well as an increased solution quality.”
From Quantum Combinatorial Optimization in the NISQ Era: A Systematic Mapping Study (2024) · cites this paper as [141]
The Results report that an RL policy trained on a small disordered Ising chain transferred to a larger one, and that adding local optimization improved solution quality. The discussion reports only preliminary robustness to noise in initial-state preparation, not robustness to noise generally. The paper describes simulated tests rather than results from a physical experiment; page numbers are unavailable in our copy. (LLM, conf=0.87)
Experimental — accuracy not yet measuredMethods to conduct classical parameter optimization of a VQA beyond standard classical optimizers comprise machine learning approaches and sequential minimal optimization.
“Methods to conduct the classical parameter optimization of a VQA that go beyond the standard classical optimizers (cf. [166]) comprise machine learning approaches [51, 75, 76, 141, 143, 146] and sequential minimal optimization [99].”
From Quantum Combinatorial Optimization in the NISQ Era: A Systematic Mapping Study (2024) · cites this paper as [141]
Comparative evaluation studies have demonstrated the character of QAOA as a digitized version of QA.
“Last, comparative evaluation studies between VQAs and QA have demonstrated (i) the reachability issues of QAOA compared to VQE [10], (ii) the ability of QAOA to harness diabatic processes in contrast to QA [154], (iii) the superiority of QA in terms of performance [142], and (iv) the character of QAOA as a digitized version of QA [141].”
From Quantum Combinatorial Optimization in the NISQ Era: A Systematic Mapping Study (2024) · cites this paper as [141]
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References 27 · Cited by 80
Research works printed but not linked: 30
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- Authors
- Wauters, M. M., Panizon, E., Mbeng, G. B., & Santoro, G. E.
- Affiliations
- Scuola Internazionale Superiore di Studi Avanzati (IT)
- University of Konstanz (DE)
- Universität Innsbruck (AT)
- The Abdus Salam International Centre for Theoretical Physics (ICTP) (IT)
- Venue
- Physical Review Research
- Publisher
- American Physical Society (APS)
- Published
- 2020-09-18
- Volume
- 2
- Issue
- 3
- ISSN
- 2643-1564 (ISSN-L)
- OpenAlex
- W3019761397
- Cited by
- 80
- References
- 27
- Citations by year
- 2026: 8 · 2025: 15 · 2024: 14 · 2023: 13 · 2022: 16 · 2021: 10 · 2020: 4
- FWCI
- 4.22
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Physical Review Research (publishedVersion, cc-by)
- arXiv (Cornell University) (submittedVersion)
- SDL (submittedVersion, cc-by)
- Iris (University of Trento) (submittedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
- arXiv (Cornell University)
- Funders
- Horizon 2020 (834402)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Quantum Computing Algorithms and Architecture
- Physical Sciences › Physics and Astronomy › Atomic and Molecular Physics, and Optics › Quantum many-body systems
- Physical Sciences › Computer Science › Artificial Intelligence › Quantum Information and Cryptography
- Keywords
- reinforcement learning, quantum optimization, quantum approximate optimization algorithm, variational minimization, quantum Ising chain, policy transfer, disorder
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