分类： 天文学 >> 天文仪器与技术 提交时间： 2024-02-07 合作期刊: 《天文技术与仪器（英文）》
摘要：Artificial Intelligence (AI) is an interdisciplinary research field with widespread applications. It aims at developing theoretical, methodological, technological, and applied systems that simulate, enhance, and assist human intelligence. Recently, notable accomplishments of artificial intelligence technology have been achieved in astronomical data processing, establishing this technology as central to numerous astronomical research areas such as radio astronomy, stellar and galactic (Milky Way) studies, exoplanets surveys, cosmology, and solar physics. This article systematically reviews representative applications of artificial intelligence technology to astronomical data processing, with comprehensive description of specific cases: pulsar candidate identification, fast radio burst detection, gravitational wave detection, spectral classification, and radio frequency interference mitigation. Furthermore, it discusses possible future applications to provide perspectives for astronomical research in the artificial intelligence era.
分类： 天文学 >> 天文仪器与技术 提交时间： 2024-02-07 合作期刊: 《天文技术与仪器（英文）》
摘要：Artificial Intelligence (AI) is an interdisciplinary research field with widespread applications. It aims at developing theoretical, methodological, technological, and applied systems that simulate, enhance, and assist human intelligence. Recently, notable accomplishments of artificial intelligence technology have been achieved in astronomical data processing, establishing this technology as central to numerous astronomical research areas such as radio astronomy, stellar and galactic (Milky Way) studies, exoplanets surveys, cosmology, and solar physics. This article systematically reviews representative applications of artificial intelligence technology to astronomical data processing, with comprehensive description of specific cases: pulsar candidate identification, fast radio burst detection, gravitational wave detection, spectral classification, and radio frequency interference mitigation. Furthermore, it discusses possible future applications to provide perspectives for astronomical research in the artificial intelligence era.
分类： 光学 >> 量子光学 提交时间： 2023-02-19
摘要： A crucial subroutine for various quantum computing and communication algorithms is to efficiently extract different classical properties of quantum states. In a notable recent theoretical work by Huang, Kueng, and Preskill [Nat. Phys. 16, 1050 (2020)], a thrifty scheme showed how to project the quantum state into classical shadows and simultaneously predict $M$ different functions of a state with only $\mathcal{O}(\log_2 M)$ measurements, independent of the system size and saturating the information-theoretical limit. Here, we experimentally explore the feasibility of the scheme in the realistic scenario with a finite number of measurements and noisy operations. We prepare a four-qubit GHZ state and show how to estimate expectation values of multiple observables and Hamiltonians. We compare the measurement strategies with uniform, biased, and derandomized classical shadows to conventional ones that sequentially measure each state function exploiting either importance sampling or observable grouping. We next demonstrate the estimation of nonlinear functions using classical shadows and analyze the entanglement of the prepared quantum state. Our experiment verifies the efficacy of exploiting (derandomized) classical shadows and sheds light on efficient quantum computing with noisy intermediate-scale quantum hardware.
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