  Physical reservoir computing is a framework for brain-inspired information
processing that utilizes nonlinear and high-dimensional dynamics in
non-von-Neumann systems. In recent years, spintronic devices have been proposed
for use as physical reservoirs, but their practical application remains a major
challenge, mainly because thermal noise prevents them from retaining short-term
memory, the essence of neuromorphic computing. Here, we propose a framework for
spintronic physical reservoirs that exploits frequency domain dynamics in
interacting spins. Through the effective use of frequency filters, we
demonstrate, for a model of frustrated magnets, both robustness to thermal
fluctuations and feasibility of frequency division multiplexing. This scheme
can be coupled with parallelization in spatial domain even down to the level of
a single spin, yielding a vast number of spatiotemporal computational units.
Furthermore, the nonlinearity via the exchange interaction allows information
processing among different frequency threads. Our findings establish a design
principle for high-performance spintronic reservoirs with the potential for
highly integrated devices.
