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• W3105640580 abstract "The quantum autoencoder is a recent paradigm in the field of quantum machine learning, which may enable an enhanced use of resources in quantum technologies. To this end, quantum neural networks with less nodes in the inner than in the outer layers were considered. Here, we propose a useful connection between approximate quantum adders and quantum autoencoders. Specifically, this link allows us to employ optimized approximate quantum adders, obtained with genetic algorithms, for the implementation of quantum autoencoders for a variety of initial states. Furthermore, we can also directly optimize the quantum autoencoders via genetic algorithms. Our approach opens a different path for the design of quantum autoencoders in controllable quantum platforms." @default.
• W3105640580 created "2020-11-23" @default.
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• W3105640580 date "2018-10-16" @default.
• W3105640580 modified "2023-09-26" @default.
• W3105640580 title "Quantum autoencoders via quantum adders with genetic algorithms" @default.
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• W3105640580 doi "https://doi.org/10.1088/2058-9565/aae22b" @default.
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