Experimentally detecting a quantum change point via Bayesian inference
This provides a tool for improvement in applications requiring sequences of identical quantum states, but it is incremental as it extends classical change point detection to the quantum realm with a specific method.
The paper tackled the problem of detecting a change point in a quantum system where a source switches from emitting photons in a default state to a mutated state, and the result showed that using Bayesian inference with adaptive measurements can largely improve the local-detection success probability.
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.