论文标题
用钻石中的自旋处理器检测量子异常
Quantum Anomaly Detection with a Spin Processor in Diamond
论文作者
论文摘要
在处理量子计算时，分析和学习量子数据的模式对于许多任务至关重要。量子机学习算法不仅可以处理前面量子程序中产生的量子状态，还可以处理编码经典问题的量子寄存器。在这项工作中，我们通过实验证明了用三Q量量子处理器编码音频样品的量子状态的异常检测，该量子由钻石中的固态旋转组成。通过用一些普通样品训练量子机，量子机可以检测到最小错误率为15.4％的异常样品。这些结果表明，量子异常检测在处理机器学习任务以及检测量子设备异常输出的潜力方面具有功能。
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.
