[2604.00207] Lead Zirconate Titanate Reservoir Computing for Classification of Written and Spoken Digits

Computer Science > Machine Learning arXiv:2604.00207 (cs) [Submitted on 31 Mar 2026] Title:Lead Zirconate Titanate Reservoir Computing for Classification of Written and Spoken Digits Authors:Thomas Buckley, Leslie Schumm, Manor Askenazi, Edward Rietman View a PDF of the paper titled Lead Zirconate Titanate Reservoir Computing for Classification of Written and Spoken Digits, by Thomas Buckley and 3 other authors View PDF HTML (experimental) Abstract:In this paper we extend our earlier work of (Rietman et al. 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 demonstrate that the PZT reservoir achieves 89.0% accuracy on MNIST handwritten digits, representing a 2.4 percentage point improvement over logistic regression baselines applied to the same preprocessed data. However, for the AudioMNIST spoken digits dataset, the reservoir system (88.2% accuracy) performs equivalently to baseline methods (88.1% accuracy), suggesting that reservoir computing provides the greatest benefits for classification tasks of intermediate difficulty where linear methods underperform but the problem remains learnable. PZT is a well-known material already used in semiconductor applications, presenting a low-power computational substrate that can be integrated with digital algorithms. Our findings ind...