Experimentally detecting a quantum change point via Bayesian inference
Abstract
Detecting a change point is a crucial task in statistics that has been recently extended to the quantum realm. A source state generator that emits a series of single photons in a default state suffers an alteration at some point and starts to emit photons in a mutated state. The problem consists in identifying the point where the change took place. In this work, we consider a learning agent that applies Bayesian inference on experimental data to solve this problem. This learning machine adjusts the measurement over each photon according to the past experimental results finds the change position in an online fashion. Our results show that the local-detection success probability can be largely improved by using such a machine learning technique. This protocol provides a tool for improvement in many applications where a sequence of identical quantum states is required.
Cite
@article{arxiv.1801.07508,
title = {Experimentally detecting a quantum change point via Bayesian inference},
author = {Shang Yu and Chang-Jiang Huang and Jian-Shun Tang and Zhih-Ahn Jia and Yi-Tao Wang and Zhi-Jin Ke and Wei Liu and Xiao Liu and Zong-Quan Zhou and Ze-Di Cheng and Jin-Shi Xu and Yu-Chun Wu and Yuan-Yuan Zhao and Guo-Yong Xiang and Chuan-Feng Li and Guang-Can Guo and Gael Sentís and Ramon Muñoz-Tapia},
journal= {arXiv preprint arXiv:1801.07508},
year = {2018}
}