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• W3166218109 abstract "One of the most promising applications of quantum computing is simulating quantum many-body systems. However, there is still a need for methods to efficiently investigate these systems in a native way, capturing their full complexity. Here, we propose variational quantum anomaly detection, an unsupervised quantum machine learning algorithm to analyze quantum data from quantum simulation. The algorithm is used to extract the phase diagram of a system with no prior physical knowledge and can be performed end-to-end on the same quantum device that the system is simulated on. We showcase its capabilities by mapping out the phase diagram of the one-dimensional extended Bose Hubbard model with dimerized hoppings, which exhibits a symmetry protected topological phase. Further, we show that it can be used with readily accessible devices nowadays and perform the algorithm on a real quantum computer." @default.
• W3166218109 created "2021-06-22" @default.
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• W3166218109 date "2021-12-16" @default.
• W3166218109 modified "2023-10-16" @default.
• W3166218109 title "Variational quantum anomaly detection: Unsupervised mapping of phase diagrams on a physical quantum computer" @default.
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• W3166218109 doi "https://doi.org/10.1103/physrevresearch.3.043184" @default.
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