Quantum Reservoir Computing for Attention-like Selective Reproduction

Abstract:Choosing which information to use as the context changes is a basic cognitive operation. We introduce a new task of this kind for quantum reservoir computing (QRC): a binary cue indicates which of two simultaneous input streams the reservoir must reproduce. In simulated spin reservoirs with a trained linear readout, infinitely many measurements (exact expectation values) let the reservoir reproduce the cued stream accurately. At a finite number of measurements \(N_M\), the score exceeds that of a linear readout of the raw inputs only at large \(N_M\) because of sampling noise. Performance increases with both entanglement and coherence in the computational basis, and then saturates. These results show that QRC can select and reproduce a cued stream, and they are a starting point for harder cognitive tasks.

arXiv:2610.19272 [quant-ph]

(or arXiv:2610.19272v1 [quant-ph] for this version)

https://doi.org/10.48550/arXiv.2610.19272 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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