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<p>Machine learning algorithms have proven to be 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 a chiral edge state and a 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 complementary metal-oxide-semiconductor 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.</p>

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Cryogenic in-memory computing using magnetic topological insulators

  • 期刊论文

  • 发表日期：

    2025年1月27日

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  • Yuting Liu,
  • Albert Lee,
  • Kun Qian,
  • Peng Zhang,
  • Zhihua Xiao,
  • Haoran He,
  • Zheyu Ren,
  • Shun Kong Cheung,
  • Ruizi Liu,
  • Yaoyin Li,
  • Xu Zhang,
  • Zichao Ma,
  • Jianyuan Zhao,
  • Weiwei Zhao,
  • Guoqiang Yu,
  • Xin Wang,
  • Junwei Liu,
  • Zhongrui Wang,
  • Kang L. Wang,
  • Qiming Shao

摘要

Machine learning algorithms have proven to be 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 a chiral edge state and a 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 complementary metal-oxide-semiconductor 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.