2017/09/30 by L Lamata, L. Lamata, U Alvarez-Rodriguez +7 · 59 citations
Computer Science · Physics and Astronomy · #Artificial neural network #Autoencoder #Field (mathematics) #Quantum #Quantum Computing Algorithms and Architecture #Quantum Information and Cryptography #Quantum algorithm #Quantum computer #Quantum many-body systems #Quantum network #Quantum phase estimation algorithm #cs.LG #cs.NE #quant-ph
paper · pdf · doi:10.1088/2058-9565/aae22b
published in Quantum Science and Technology 4(1), 014007 (IOP Publishing)
openalex created_date 2017/10/06 · openalex publication_date 2018/09/18 · arxiv created 2018/10/17 · arxiv updated 2018/10/18 · openalex updated_date 2026/08/05
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 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.