Lead Zirconate Titanate Reservoir Computing for Classification of Written and Spoken Digits

ArXi:2604.00207v1 Announce Type: new In this paper we extend our earlier work of (Rietman 2022) presenting an application of physical Reservoir Computing (RC) to the classification of handwritten and spoken digits. We utilize an unpoled cube of Lead Zirconate Titanate (PZT) as a computational substrate to process these datasets. Our results nstrate that the PZT reservoir achieves 89.0% accuracy on MNIST handwritten digits, representing a 2.4%age point improvement over logistic regression baselines applied to the same preprocessed data.