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arXiv 2107.12975cond-mat.mes-hallcs.LGquant-ph
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基于机器学习的硅基与SiGe基量子器件的跨架构调谐

Cross-architecture Tuning of Silicon and SiGe-based Quantum Devices Using Machine Learning

University of Oxford(牛津大学);University of Basel(巴塞尔大学);Institute of Science and Technology Austria(奥地利科技学院);Politecnico di Milano(米兰理工大学)
B. Severin, D. T. Lennon, L. C. Camenzind, F. Vigneau, F. Fedele, D. Jirovec, A. Ballabio, D. Chrastina, G. Isella, M. de Kruijf, M. J. Carballido, S. Svab, A. … 展开作者

B. Severin, D. T. Lennon, L. C. Camenzind, F. Vigneau, F. Fedele, D. Jirovec, A. Ballabio, D. Chrastina, G. Isella, M. de Kruijf, M. J. Carballido, S. Svab, A. V. Kuhlmann, F. R. Braakman, S. Geyer, F. N. M. Froning, H. Moon, M. A. Osborne, D. Sejdinovic, G. Katsaros, D. M. Zumbühl, G. A. D. Briggs, N. Ares

AI总结:

针对硅基与SiGe基量子器件的差异性调谐难题,提出无需修改即可跨架构调谐三类不同量子器件的机器学习算法,实现对应调谐时间并揭示参数空间图景,证明机器学习可提供量子器件调谐的通用方案。

摘要:

硅基与SiGe基器件在量子电路规模化方面的潜力受到器件差异性的制约,每个器件都需要被调谐至工作状态。我们提出一种算法,为解决这一差异性问题迈出关键一步:该算法无需修改,即可从零开始调谐4栅极Si FinFET、5栅极GeSi纳米线以及7栅极SiGe异质结双量子点器件。我们实现的调谐时间分别为30分钟、10分钟和92分钟。该算法还能揭示每种器件的参数空间图景。这些结果表明,机器学习为量子器件调谐提供了通用解决方案。

英文摘要:

The potential of Si and SiGe-based devices for the scaling of quantum circuits is tainted by device variability. Each device needs to be tuned to operation conditions. We give a key step towards tackling this variability with an algorithm that, without modification, is capable of tuning a 4-gate Si FinFET, a 5-gate GeSi nanowire and a 7-gate SiGe heterostructure double quantum dot device from scratch. We achieve tuning times of 30, 10, and 92 minutes, respectively. The algorithm also provides insight into the parameter space landscape for each of these devices. These results show that overarching solutions for the tuning of quantum devices are enabled by machine learning.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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