Quantum-Fisher-information-aware optimization yields a quantum reservoir advantage over tuned classical reservoirs

Abstract:We show that a quantum reservoir computer optimized with its quantum Fisher information (QFI) has a quantum advantage over a classical reservoir of equal readout cost, provided by a quantum-information advantage: optimizing the quantum parameters with the QFI metric. On Mackey-Glass forecasting, a driven Jaynes-Cummings reservoir with one to three atoms beats a tuned echo-state network at every size from 4 to 30 trained weights, by 25-69% in held-out error, and is ahead of 99.5% of 10,000 random networks. After grid and Bayesian searches saturate, the QFI-aware stage lowers the error by 29-51% at 12-30 weights, beyond equal-budget black-box search or classical-parameter gradients alone, and at 30 weights turns a loss into a win. We then derive a fundamental QFI bound on the first-order rate of change of the retrained loss and factorize the gap to this bound into five link efficiencies that identify the physical mechanism limiting optimization.

arXiv:2610.14454 [quant-ph]

(or arXiv:2610.14454v1 [quant-ph] for this version)

https://doi.org/10.48550/arXiv.2610.14454 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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