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Cryogenic in-memory computing using magnetic topological insulators

delete2025-01-27
delete1
PRE
AI
Y
Yuting Liu
A
Albert Lee
Z
Zhang, Peng
R
Ren, Zheyu
C
Cheung, Shun Kong
L
Li, Yaoyin
Z
Zhang, Xu
M
Ma, Zichao
Z
Zhao, Jianyuan
Z
Zhao, Weiwei
L
Liu, Junwei
K
Kang L. Wang
S
Shao, Qiming *
DOI：10.1038/s41563-024-02088-4delete
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摘要

摘要

En 中文
机器学习算法已被证明对量子错误纠正和量子控制等基本量子计算任务有效。这些算法在低温下的高效硬件实现至关重要。本文利用磁性拓扑绝缘体作为忆阻器（称为磁性拓扑忆阻器），并基于手性边缘态与拓扑表面态的共存，提出了一种低温内存储器计算方案。巨反常霍尔效应的忆阻器开关与读取表现出高能量效率、高稳定性和低随机性。我们使用四个磁性拓扑忆阻器在概念验证分类任务中实现了高精度。此外，我们针对大规模神经网络的算法级和电路级模拟表明，与现有的磁性忆阻器和互补金属氧化物半导体技术相比，在图像识别和量子态制备方面，本方案在软件层面具有更高的精度和更低的能耗。我们的研究不仅展示了手性边缘态的新应用，也可能启发基于拓扑量子物理学的进一步新型计算方案。
Keyword:
SPIN-ORBIT TORQUE

期刊

Nature Materials 封面图
Nature Materials
IF:
38.5
论文数:
6.8K
被引数:
11.5W

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