Benchmarking the performance of quantum reservoir computing platforms of particles of distinct statistics

Abstract

Trabajo presentado en el IFISC Poster Party (online).-- The IFISC Poster Party is an annual activity where PhD students and postdoctoral researchers of IFISC present their research in a poster format.-- Transport and Information in Quantum Systems.Reservoir computing (RC) is a neuro-inspired machine learning approach to time series processing. As such, it forms an example of a natural unconventional analog computer designed to perform a given computational task. Its power in solving nonlinear and temporal tasks depends on the reservoir possessing a high dimensional state space and the ability to retain memory of information for sufficiently long time. Quantum systems, with their large number of degrees of freedom and their complex real time dynamics satisfy both requirements, and for this reason are good candidates to serve as substrates for RC. In addition, quantum effects such as superposition could lead to improvement in the performance of a RC. An important issue we explore here in order to establish the potential of quantum reservoirs computing (QRC) is the role of the particle statistics of the units composing the complex network reservoir. Considering the simplest interaction, we assess the performance of fermions bosons and the commonly used spins for QRC.Peer reviewe