The DOI matches its Crossref record; 21 of the 21 references it cites were found in a registry or an index.
Reinforcement Learning with Neural Networks for Quantum Feedback
Open accessAbstract
Machine learning with artificial neural networks is revolutionizing science.The most advanced challenges require discovering answers autonomously.In the domain of reinforcement learning, control strategies are improved according to a reward function.The power of neural-network-based reinforcement learning has been highlighted by spectacular recent successes such as playing Go, but its benefits for physics are yet to be demonstrated.Here, we show how a network-based "agent" can discover complete quantum-error-correction strategies, protecting a collection of qubits against noise.These strategies require feedback adapted to measurement outcomes.Finding them from scratch without human guidance and tailored to different hardware resources is a formidable challenge due to the combinatorially large search space.To solve this challenge, we develop two ideas: two-stage learning with teacher and student networks and a reward quantifying the capability to recover the quantum information stored in a multiqubit system.Beyond its immediate impact on quantum computation, our work more generally demonstrates the promise of neural-network-based reinforcement learning in physics.
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Cites
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Human-level control through deep reinforcement learning
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Cited by
Polino, E., Valeri, M., Spagnolo, N., & Sciarrino, F. (2020) · AVS Quantum Science · doi:10.1116/5.0007577 · Cited by 414
Artificial intelligence and machine learning for quantum technologies
Krenn, M., Landgraf, J., Foesel, T., & Marquardt, F. (2023) · Physical Review A · doi:10.1103/physreva.107.010101 · Cited by 162
Self-citationModel-Free Quantum Control with Reinforcement Learning
Sivak, V. V., Eickbusch, A., Liu, H., Royer, B., Tsioutsios, I., & Devoret, M. H. (2022) · Physical Review X · doi:10.1103/physrevx.12.011059 · Cited by 130
Neural predictor based quantum architecture search
Zhang, S.-X., Hsieh, C.-Y., Zhang, S., & Yao, H. (2021) · Machine Learning: Science and Technology · doi:10.1088/2632-2153/ac28dd · Cited by 88
Reinforcement-learning-assisted quantum optimization
Wauters, M. M., Panizon, E., Mbeng, G. B., & Santoro, G. E. (2020) · Physical Review Research · doi:10.1103/physrevresearch.2.033446 · Cited by 80
Metadata
- Authors
- Fösel, T., Tighineanu, P., Weiss, T., & Marquardt, F.
- Venue
- Physical Review X
- Publisher
- American Physical Society (APS)
- Published
- 2018-09-27
- Volume
- 8
- Issue
- 3
- ISSN
- 2160-3308 (ISSN-L)
- OpenAlex
- W2786295636
- Cited by
- 231
- References
- 21
- FWCI
- 13.30
- Type
- journal article
- Language
- en
- Open access
- gold
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- Fösel, T., Tighineanu, P., Weiss, T., & Marquardt, F. (2018). Reinforcement Learning with Neural Networks for Quantum Feedback. Physical Review X. https://doi.org/10.1103/physrevx.8.031084
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