Machine Learning

Quantum Autoencoders via Quantum Adders with Genetic Algorithms

This topic contains 0 replies, has 1 voice, and was last updated by  arXiv 1 year, 5 months ago.


  • arXiv
    5 pts

    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 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.

    Quantum Autoencoders via Quantum Adders with Genetic Algorithms
    by L. Lamata, U. Alvarez-Rodriguez, J. D. Martín-Guerrero, M. Sanz, E. Solano
    https://arxiv.org/pdf/1709.07409v1.pdf

You must be logged in to reply to this topic.