Benchmarking the Role of Particle Statistics in Quantum Reservoir Computing. Issue 1 (29th November 2022) - Llodrà, Guillem; Charalambous, Christos; Giorgi, Gian Luca; Zambrini, Roberta
Abstract: Quantum reservoir computing is a neuro‐inspired machine learning approach harnessing the rich dynamics of quantum systems to solve temporal tasks. It has gathered attention for its suitability for NISQ devices, for easy and fast trainability, and for potential quantum advantage. Although several types of systems have been proposed as quantum reservoirs, differences arising from particle statistics have not been established yet. In this work, the ability of bosons, fermions, and qubits are assessed and compared to store information from past inputs by measuring linear and nonlinear memory capacity. While, in general, the performance of the system improves with the Hilbert space size, it is shown that also the information spreading capability is a key factor. For the simplest reservoir Hamiltonian choice, and for each boson limited to at most one excitation, fermions provide the best reservoir due to their intrinsic nonlocal properties. On the other hand, a tailored input injection strategy allows the exploitation of the abundance of degrees of freedom of the Hilbert space for bosonic quantum reservoir computing and enhances the computational power compared to both qubits and fermions. Abstract : In the context of quantum reservoir computing, this work assesses and compares the ability of bosons, fermions, and spins to store information from past inputs by measuring their linear and nonlinear memory capacity. It is shown that the two main key factors are the Hilbert space size and the information spreading capability of each kind of particle.