The Role of Particle Statistics in Quantum Reservoir Computing

Trabajo presentado en la Conference on Complex Systems (CCS), celebrada en Lyon del 25 al 29 de octubre de 2021.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 [1]. In addition, quantum effects such as superposition could lead to improvement in the performance of a RC. Recently the role of dynamical phases of closed quantum systems on the performance of quantum reservoir computers was studied [2]. In general, an open question in quantum reservoir computing (QRC) is the following: what properties a physical system must exhibit in order to be a good QRC? The aim of this particular work is to establish the relation between particle statistics of the particles comprising the complex computing system and its performance as a QRC.Peer reviewe