Anomaly detection in reconstructed quantum states using a machine-learning technique
This work addresses a key challenge in quantum information processing and physics, offering a potential tool for detecting anomalies in quantum states with limited data, though it appears incremental as it builds on existing data mining concepts.
The paper tackles the problem of detecting small deviations in reconstructed quantum states, which are affected by intrinsic fluctuations from limited samples, and demonstrates that their proposed method achieves more accurate detection compared to a naive trace distance approach.
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.