Quantum autoencoders via quantum adders with genetic algorithms

Quantum autoencoders via quantum adders with genetic algorithms

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 connecti...

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Veröffentlicht in:Quantum science and technology 2019-01, Vol.4 (1), p.14007
Hauptverfasser: Lamata, L, Alvarez-Rodriguez, U, Martín-Guerrero, J D, Sanz, M, Solano, E
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genetic algorithms
quantum adder
quantum autoencoder
quantum machine learning
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container_issue 1
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container_title Quantum science and technology
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creator Lamata, L
Alvarez-Rodriguez, U
Martín-Guerrero, J D
Sanz, M
Solano, E
description 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 quantum autoencoders and quantum adders, which approximately add two unknown quantum states supported in different quantum systems. 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.
doi_str_mv 10.1088/2058-9565/aae22b
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subjects genetic algorithms
quantum adder
quantum autoencoder
quantum machine learning
title Quantum autoencoders via quantum adders with genetic algorithms
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