Computational processing capacity of quantum reservoirs across the classical-to-quantum transition

Quantum reservoir computing is a resource-efficient machine-learning paradigm ideally suited to near-term quantum implementations, due to its relaxed requirements on hardware control and training volume. An important open question is the possible computational advantage of a quantum reservoir in comparison to its classical counterpart. We address this question directly by introducing a framework for quantum reservoir computing [1] that enables the same physical reservoir to be operated in quantum or classical regimes. Our approach is built upon a quantum-mechanical description of the complete measurement chain including a nonlinear multimode physical reservoir, taking into account realistic input schemes and quantum measurement overhead, realizable in the superconducting circuit architecture. We define a metric for information processing capacity which can be used to compare a physically-motivated classical limit of reservoir operation at high-excitation powers, to a well-defined quantum limit, thus isolating the role played by quantum dynamics. We also analyze the impact of quantum correlations and entanglement as resources for reservoir computing using multimode quantum reservoirs.

[1] Khan et al., arXiv:2110.13849