García-Beni Jorge, Luca Giorgi Gian, Soriano Miguel C, Zambrini Roberta
Opt Express. 2024 Feb 12;32(4):6733-6747. doi: 10.1364/OE.507684.
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, which is increased with squeezing.
在计量学、密码学和计算等众多应用中，压缩被认为是一种量子资源，它与多模环境中的纠缠有关。在这项工作中，我们研究了压缩在用于时间序列处理的神经形态机器学习中的作用。具体而言，我们考虑一种基于环路的光子储层计算架构，并考虑一个同时包含有源和无源耦合项的哈密顿量，研究压缩在储层中的影响。有趣的是，从理想模型过渡到考虑实验噪声的现实模型时，压缩对量子储层计算可能是有害的，也可能是有益的。我们证明多模压缩增强了其可访问的内存，这在几个基准时间任务中提高了性能。这种性能提升的根源可追溯到储层对读出噪声的鲁棒性，而这种鲁棒性会随着压缩而增强。
