Machine learning algorithms have been proven effective for essential quantum computation tasks such as quantum error correction and quantum control. Efficient hardware implementation of these algorithms at cryogenic temperatures is essential. Here, we utilize magnetic topological insulators as memristors (termed magnetic topological memristors) and introduce a cryogenic in-memory computing scheme based on the coexistence of the chiral edge state and the topological surface state. The memristive switching and reading of the giant anomalous Hall effect exhibit high energy efficiency, high stability, and low stochasticity. We achieve high accuracy in a proof-of-concept classification task using four magnetic topological memristors. Furthermore, our algorithm-level and circuit-level simulations of large-scale neural networks demonstrate software-level accuracy and lower energy consumption for image recognition and quantum state preparation compared with existing magnetic memristor and CMOS technologies. Our results not only showcase a new application of chiral edge states but also may inspire further topological quantum physics-based novel computing schemes.
@article{arxiv.2209.09443,
title = {Cryogenic in-memory computing using magnetic topological insulators},
author = {Yuting Liu and Albert Lee and Kun Qian and Peng Zhang and Zhihua Xiao and Haoran He and Zheyu Ren and Shun Kong Cheung and Ruizi Liu and Yaoyin Li and Xu Zhang and Zichao Ma and Jianyuan Zhao and Weiwei Zhao and Guoqiang Yu and Xin Wang and Junwei Liu and Zhongrui Wang and Kang L. Wang and Qiming Shao},
journal= {arXiv preprint arXiv:2209.09443},
year = {2025}
}
Comments
56 pages, 23 figures, 5 tables, accepted version; we have corrected the upper panel in Fig. 3c of the published version