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• W2912228780 abstract "Abstract We introduce a hybrid machine learning algorithm for designing quantum optics experiments to produce specific quantum states. Our algorithm successfully found experimental schemes to produce all 5 states we asked it to, including Schrödinger cat states and cubic phase states, all to a fidelity of over 96%. Here, we specifically focus on designing realistic experiments, and hence all of the algorithm’s designs only contain experimental elements that are available with current technology. The core of our algorithm is a genetic algorithm that searches for optimal arrangements of the experimental elements, but to speed up the initial search, we incorporate a neural network that classifies quantum states. The latter is of independent interest, as it quickly learned to accurately classify quantum states given their photon number distributions." @default.
• W2912228780 created "2019-02-21" @default.
• W2912228780 creator A5041438434 @default.
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• W2912228780 date "2019-03-27" @default.
• W2912228780 modified "2023-10-12" @default.
• W2912228780 title "A hybrid machine learning algorithm for designing quantum experiments" @default.
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• W2912228780 doi "https://doi.org/10.1007/s42484-019-00003-8" @default.
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