August 1, 2025
Journal Article

Quantum time dynamics mediated by the Yang-Baxter equation and artificial neural networks

Abstract

Quantum computing shows great potential, but errors pose a significant challenge. This study explores new strategies for mitigating quantum errors using artificial neural networks (ANN) and the Yang-Baxter equation (YBE). Unlike traditional error correction methods, which are computationally intensive, we investigate artificial error mitigation. The manuscript introduces the basics of quantum error sources and explores the potential of using classical computation for error mitigation. The Yang-Baxter equation plays a crucial role, allowing us to compress time dynamics simulations into constant-depth circuits. By introducing controlled noise through the YBE, we enhance the dataset for error mitigation. We train an ANN model on partial data from quantum simulations, demonstrating its effectiveness in correcting errors in time-evolving quantum states.

Published: August 1, 2025

Citation

Gulania S., Y. Alexeev, G. Stephen, B. Peng, and N. Govind. 2025. Quantum time dynamics mediated by the Yang-Baxter equation and artificial neural networks. Journal of Chemical Theory and Computation 21, no. 13:6280–6291. PNNL-SA-200133. doi:10.1021/acs.jctc.5c00353