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机器学习辅助精密制造中的原子量子位

Aaron D Tranter¹, Ludwik Kranz^2,3, Sam Sutherland^2,3

¹Centre of Excellence for Quantum Computation and Communication Technology, Department of Quantum Science and Technology, Research School of Physics, The Australian National University, Acton 2601, Australia.

|July 17, 2024

概括

机器学习在制造过程中准确地预测量子位中的供体原子数. 扫描道显微镜 (STM) 石版的这一突破推动了可扩展量子计算和传感技术的发展.

科学领域:

量子计算是一种量子计算.
材料科学是一种材料科学.
机器学习是机器学习.

背景情况:

基于捐赠者的量子比特为量子计算提供了一个可扩展的平台.
精确的捐赠原子放置对于量子比特性能至关重要.
目前的制造方法缺乏对捐赠原子配置的实时反.

研究的目的:

开发机器学习 (ML) 技术,在量子比特制造过程中实时预测捐助原子数.
为了实现高可靠性量子比特 (qubits) 的自动制造.

主要方法:

在扫描道显微镜 (STM) 图像上利用机器学习图像识别.
开发了卷积神经网络 (CNN) 来预测捐赠原子分布.
实施了减轻过度拟合的技术,包括减少模型复杂性,数据预处理和增强.

主要成果:

在量子比特站点预测捐赠原子号的准确率超过90%.
在开发的ML模型中展示了一致的性能.
验证了ML在实时制造反中的有效性.

结论:

机器学习提供了一种准确和自动化的方法来控制量子比特中的捐赠原子配置.
这项工作是迈向量子计算和传感量子比特自动制造的重要一步.
开发的ML技术可以加速量子技术的扩展.

关键词:

在 STM 石版画上.机器学习是机器学习.是一种的物质.量子点是一个量子点.是一种.

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