Quantum autoencoders via quantum adders with genetic algorithms
This work addresses the challenge of resource-efficient quantum machine learning for quantum technologies, but it appears incremental as it builds on existing paradigms with a novel connection.
The paper tackles the problem of designing quantum autoencoders by connecting them to approximate quantum adders optimized with genetic algorithms, enabling implementation for various initial states and direct optimization, which opens a new path for design in controllable quantum platforms.
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.