  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, we assess and
compare the ability of bosons, fermions, and qubits 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, we
show 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.
