Year: 2024
Type: article
Abstract: 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.
Source: Physical Review A
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
Funders: European Commission, 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, NextGenerationEU
Awards: G1622782223, G2052874935, G2262748287, Electronic-photonic integrated quantum simulator platform, G34346518, G3480869486, G3681454997, G3760940442, G3777708657, G3809973144, G3861891927, G4042783231, G425668148, G438544507, Electronic-photonic integrated quantum simulator platform, G451917667, G498075875, Open Superconducting Quantum Computers (OpenSuperQPlus), G5967599077, G5987792135, G6293336981, G6604905309, G67347242, G7084143925, G8217939449, G8580240239, G8738040877, G876203175, G8879127082, G8953647516
OpenAlex ID: W4402514020