Anomaly detection in reconstructed quantum states using a machine-learning technique
Quantum Physics
2015-06-18 v1 Applications
Machine Learning
Abstract
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.
Cite
@article{arxiv.1401.4785,
title = {Anomaly detection in reconstructed quantum states using a machine-learning technique},
author = {Satoshi Hara and Takafumi Ono and Ryo Okamoto and Takashi Washio and Shigeki Takeuchi},
journal= {arXiv preprint arXiv:1401.4785},
year = {2015}
}
Comments
Accepted for Physical Review A