Robust two-qubit gate with reinforcement learning and dropout
In the realm of quantum control, reinforcement learning, a prominent branch of machine learning, emerges as a competitive candidate for computer-assisted optimal experiment design. This paper investigates the extent to which guidance from human experts is necessary for effectively implementing reinforcement learning in the design of quantum control protocols. Specifically, our focus lies on engineering a robust two-qubit gate, utilizing a combination of analytical solutions as prior knowledge and techniques from computer science. Through thorough benchmarking of various models, we identify dropout---a widely used method for mitigating overfitting in machine learning---as a particularly robust approach. Our findings demonstrate the potential of integrating computer science concepts to propel the development of advanced quantum technologies.
Authors
- Tian-Niu Xu (ORCID: https://orcid.org/0000-0001-6131-4749)
- José D. Martín‐Guerrero (ORCID: https://orcid.org/0000-0001-9378-0285)
- Xi Chen (ORCID: https://orcid.org/0000-0003-4221-4288)
- Yongcheng Ding
Institutions
- Shanghai University (CN)
- Universitat de València (ES)
- University of the Basque Country (ES)
- Qilu Normal University (CN)
- Instituto de Ciencia de Materiales de Madrid (ES)
- Artificial Intelligence Research Institute (ES)
- Research Network (United States) (US)
Publication Details
- Journal
- Physical review. A/Physical review, A
- Published
- 2024-09-13
- DOI
- https://doi.org/10.1103/physreva.110.032614
- Citations
- 2
- Primary Topic
- Quantum Computing Algorithms and Architecture
- Type
- article
- Field-Weighted Citation Impact
- 0.69
Funders
- National Natural Science Foundation of China
- Eusko Jaurlaritza
- Generalitat Valenciana
- Science and Technology Commission of Shanghai Municipality
- European Regional Development Fund
- Agencia Estatal de Investigación
- H2020 Future and Emerging Technologies