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| Những tác giả chính: | , , , , , , , , , , , , , , , , , , , |
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| Định dạng: | Preprint |
| Được phát hành: |
2022
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| Những chủ đề: | |
| Truy cập trực tuyến: | https://arxiv.org/abs/2209.09443 |
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| _version_ | 1866913871319531520 |
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| author | Liu, Yuting Lee, Albert Qian, Kun Zhang, Peng Xiao, Zhihua He, Haoran Ren, Zheyu Cheung, Shun Kong Liu, Ruizi Li, Yaoyin Zhang, Xu Ma, Zichao Zhao, Jianyuan Zhao, Weiwei Yu, Guoqiang Wang, Xin Liu, Junwei Wang, Zhongrui Wang, Kang L. Shao, Qiming |
| author_facet | Liu, Yuting Lee, Albert Qian, Kun Zhang, Peng Xiao, Zhihua He, Haoran Ren, Zheyu Cheung, Shun Kong Liu, Ruizi Li, Yaoyin Zhang, Xu Ma, Zichao Zhao, Jianyuan Zhao, Weiwei Yu, Guoqiang Wang, Xin Liu, Junwei Wang, Zhongrui Wang, Kang L. Shao, Qiming |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2209_09443 |
| institution | arXiv |
| publishDate | 2022 |
| record_format | arxiv |
| spellingShingle | Cryogenic in-memory computing using magnetic topological insulators Liu, Yuting Lee, Albert Qian, Kun Zhang, Peng Xiao, Zhihua He, Haoran Ren, Zheyu Cheung, Shun Kong Liu, Ruizi Li, Yaoyin Zhang, Xu Ma, Zichao Zhao, Jianyuan Zhao, Weiwei Yu, Guoqiang Wang, Xin Liu, Junwei Wang, Zhongrui Wang, Kang L. Shao, Qiming Mesoscale and Nanoscale Physics Emerging Technologies Applied Physics 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. |
| title | Cryogenic in-memory computing using magnetic topological insulators |
| topic | Mesoscale and Nanoscale Physics Emerging Technologies Applied Physics |
| url | https://arxiv.org/abs/2209.09443 |