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Squeezing is known to be a quantum resource in many applications inmetrology, cryptography, and computing, being related to entanglement inmultimode settings. In this work, we address the effects of squeezing inneuromorphic machine learning for time series processing. In particular, weconsider a loop-based photonic architecture for reservoir computing and addressthe effect of squeezing in the reservoir, considering a Hamiltonian with bothactive and passive coupling terms. Interestingly, squeezing can be eitherdetrimental or beneficial for quantum reservoir computing when moving fromideal to realistic models, accounting for experimental noise. We demonstratethat multimode squeezing enhances its accessible memory, which improves theperformance in several benchmark temporal tasks. The origin of this improvementis traced back to the robustness of the reservoir to readout noise as squeezingincreases.

Squeezing as a resource for time series processing in quantum reservoir computing