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arXiv 2102.11784quant-phcs.LG
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基于光线的量子点器件状态识别框架

Ray-based framework for state identification in quantum dot devices

National Institute of Standards and Technology(美国国家标准与技术研究院);University of Wisconsin-Madison(威斯康星大学麦迪逊分校);University of Maryland(马里兰大学);Joint Quantum Institute(联合量子研究所);Joint Center for Quantum Information and Computer Science(量子信息与计算机科学联合中心)

Justyna P. Zwolak, Thomas McJunkin, Sandesh S. Kalantre, Samuel F. Neyens, E. R. MacQuarrie, Mark A. Eriksson, Jacob M. Taylor

摘要:

利用静电门限定的量子点(QD)是用于可扩展的量子计算实现的领先平台。然而,随着量子比特数量的增加,控制参数空间的复杂性也会增加。传统的测量技术依赖于通过器件响应的双参数扫描(图像)进行完全或接近完全的探索,随着门数量的增加,这种技术很快变得不切实际。在这里,我们建议规避这一挑战,通过引入一种测量技术,依赖于一维投影的设备响应的多维参数空间。被称为“基于射线的分类(RBC)框架”,我们使用这种机器学习方法来实现QD状态的分类器,从而实现量子位相关参数机制的自动识别。我们表明,RBC超过了82%的准确率基准,从实验实施的基于图像的分类技术从以前的工作,同时减少所需的测量点的数量高达70%。测量成本的降低是时间密集型QD测量的显著增益,并且是朝着这些设备的可扩展性迈出的一步。我们还讨论了如何基于RBC的优化器,它调整设备到多量子位制度,当在由控制量子点的柱塞和势垒门定义的二维和三维参数空间中进行调谐时,可以执行。这项工作提供了实验验证,可以利用机器学习技术对具有高维参数空间和时间的量子系统中的非传统测量进行有效的状态识别和优化。密集的测量。

英文摘要:

Quantum dots (QDs) defined with electrostatic gates are a leading platform for a scalable quantum computing implementation. However, with increasing numbers of qubits, the complexity of the control parameter space also grows. Traditional measurement techniques, relying on complete or near-complete exploration via two-parameter scans (images) of the device response, quickly become impractical with increasing numbers of gates. Here we propose to circumvent this challenge by introducing a measurement technique relying on one-dimensional projections of the device response in the multidimensional parameter space. Dubbed the ``ray-based classification (RBC) framework,'' we use this machine learning approach to implement a classifier for QD states, enabling automated recognition of qubit-relevant parameter regimes. We show that RBC surpasses the 82 % accuracy benchmark from the experimental implementation of image-based classification techniques from prior work while reducing the number of measurement points needed by up to 70 %. The reduction in measurement cost is a significant gain for time-intensive QD measurements and is a step forward toward the scalability of these devices. We also discuss how the RBC-based optimizer, which tunes the device to a multiqubit regime, performs when tuning in the two-dimensional and three-dimensional parameter spaces defined by plunger and barrier gates that control the QDs.This work provides experimental validation of both efficient state identification and optimization with machine learning techniques for non-traditional measurements in quantum systems with high-dimensional parameter spaces and time-intensive measurements.

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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