利用机器学习技术检测重构量子态中的异常
Anomaly detection in reconstructed quantum states using a machine-learning technique
摘要：
精确检测给定密度矩阵中的微小偏差对于量子信息处理具有重要意义。在这里，我们提出了一个新的方法的基础上的概念，数据挖掘。我们表明，所提出的方法可以更准确地检测重建的密度矩阵，其中包含由于有限的样本数量的内在波动小的错误偏差，比一个天真的方法检查的平均给定的密度矩阵的轨迹距离。这种方法有可能成为一个关键的工具，在广泛的物理学领域中，使用有限数量的样本重建的量子态的小偏差的检测是必不可少的。
英文摘要：
The accurate detection of small deviations in given density matrices is important for quantum information processing. Here we propose a new method based on the concept of data mining. We demonstrate that the proposed method can more accurately detect small erroneous deviations in reconstructed density matrices, which contain intrinsic fluctuations due to the limited number of samples, than a naive method of checking the trace distance from the average of the given density matrices. This method has the potential to be a key tool in broad areas of physics where the detection of small deviations of quantum states reconstructed using a limited number of samples are essential.
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。
