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

TL;DR

This paper investigates how quantum squeezing affects photonic reservoir computing for time series analysis, revealing that squeezing can enhance memory and robustness against noise, thereby improving task performance.

Contribution

It introduces the role of multimode squeezing in quantum reservoir computing, showing its potential to improve memory and noise resilience in photonic architectures.

Findings

Multimode squeezing enhances reservoir memory capacity.

Squeezing improves performance on benchmark temporal tasks.

Squeezing increases robustness to readout noise.

Abstract

Squeezing is known to be a quantum resource in many applications in metrology, cryptography, and computing, being related to entanglement in multimode settings. In this work, we address the effects of squeezing in neuromorphic machine learning for time series processing. In particular, we consider a loop-based photonic architecture for reservoir computing and address the effect of squeezing in the reservoir, considering a Hamiltonian with both active and passive coupling terms. Interestingly, squeezing can be either detrimental or beneficial for quantum reservoir computing when moving from ideal to realistic models, accounting for experimental noise. We demonstrate that multimode squeezing enhances its accessible memory, which improves the performance in several benchmark temporal tasks. The origin of this improvement is traced back to the robustness of the reservoir to readout noise as squeezing increases.