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- W4313305672 abstract "Given the recent developments in quantum techniques, modeling the physical Hamiltonian of a target quantum many-body system is becoming an increasingly practical and vital research direction. Here, we propose an efficient quantum strategy that mingles maximum-likelihood-estimate state and supervised machine learning. Given the measurement outcomes, we optimize the target model Hamiltonian and density operator via a series of quantum descent, which we prove is negative semi-definite with respect to the negative-log-likelihood function. In addition to such optimization efficiency, supervised Hamiltonian learning respects the locality of a given quantum system, therefore, extends readily to larger systems. Compared with previous approaches, it also exhibits better accuracy and overall stability toward noises, fluctuations, and temperature ranges, which we demonstrate with various examples." @default.
- W4313305672 created "2023-01-06" @default.
- W4313305672 creator A5044544424 @default.
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- W4313305672 date "2022-12-28" @default.
- W4313305672 modified "2023-09-27" @default.
- W4313305672 title "Supervised Hamiltonian learning via efficient and robust quantum descent" @default.
- W4313305672 doi "https://doi.org/10.48550/arxiv.2212.13718" @default.
- W4313305672 hasPublicationYear "2022" @default.
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