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arXiv 2201.10263quant-phphysics.data-an
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在钻石中使用自旋处理器进行量子异常检测

Quantum Anomaly Detection with a Spin Processor in Diamond

University of Science and Technology of China(中国科学技术大学);CAS Key Laboratory of Microscale Magnetic Resonance(中国科学院微观磁共振重点实验室);Hefei National Laboratory(合肥国家实验室);CAS Center for Excellence in Quantum Information and Quantum Physics(中国科学院量子信息与量子物理卓越创新中心)

Zihua Chai, Ying Liu, Mengqi Wang, Yuhang Guo, Fazhan Shi, Zhaokai Li, Ya Wang, Jiangfeng Du

AI总结:

本文利用三量子位自旋处理器对音频样本编码的量子态进行异常检测,通过少量正常样本训练实现15.4%的最小误判率,展示量子异常检测在机器学习任务中的潜力。

摘要:

在量子计算处理过程中,分析和学习量子数据的模式对于许多任务至关重要。量子机器学习算法不仅能处理前序量子过程生成的量子态,还能处理编码经典问题的量子寄存器。在本工作中,我们实验性地展示了使用由钻石中固态自旋组成的三量子位量子处理器对编码音频样本的量子态进行异常检测。通过用少量正常样本训练量子机器,该机器可以以最小15.4%的误判率检测异常样本。这些结果展示了量子异常检测在处理机器学习任务中的能力,以及检测量子设备异常输出的潜力。

英文摘要:

In the processing of quantum computation, analyzing and learning the pattern of the quantum data are essential for many tasks. Quantum machine learning algorithms can not only deal with the quantum states generated in the preceding quantum procedures, but also the quantum registers encoding classical problems. In this work, we experimentally demonstrate the anomaly detection of quantum states encoding audio samples with a three-qubit quantum processor consisting of solid-state spins in diamond. By training the quantum machine with a few normal samples, the quantum machine can detect the anomaly samples with a minimum error rate of 15.4%. These results show the power of quantum anomaly detection in dealing with machine learning tasks and the potential to detect abnormal output of quantum devices.

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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