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

Source: [Lead Zirconate Titanate Reservoir Computing for Classification of Written and Spoken Digits](<https://www.sota2.com/research/paper/69cdf7f20636e5bf4afcc9a2>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2026-04-03T05:00:04.568Z

Authors: Thomas Buckley, Leslie Schumm, Manor Askenazi, Edward Rietman

Published: 2026-03-31T20:19:28.000Z

## 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 indicate that physical reservoirs excel when the task difficulty exceeds the capability of simple linear classifiers but remains within the computational capacity of the reservoir dynamics.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

- [Handwritten Digit Classification on MNIST 1D binarized / original 2D (5,000 random samples, 10-fold cross val)](<https://www.sota2.com/research/sota/handwritten-digit-classification-on-mnist-1d-binarized-original-2d-5-000-random-samples-10-fold-cross-val.md>): Handwritten digit classification; MNIST 1D binarized / original 2D (5,000 random samples, 10-fold cross val); Test Accuracy: 89.

- [Spoken Digit Classification on AudioMNIST Spoken Digits 1D binarized original 2D](<https://www.sota2.com/research/sota/spoken-digit-classification-on-audiomnist-spoken-digits-1d-binarized-original-2d.md>): Spoken Digit Classification; AudioMNIST Spoken Digits 1D binarized original 2D; Test Accuracy: 88.2.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2604.00207>)

[PDF](<https://arxiv.org/pdf/2604.00207.pdf>)