Experimentally detecting a quantum change point via Bayesian inference

TL;DR

This paper presents a Bayesian inference-based method for detecting quantum change points in a sequence of photons, significantly improving success probability through adaptive measurements, with applications in quantum state sequences.

Contribution

Introduces a learning agent that uses Bayesian inference to adapt measurements and detect quantum change points in real-time, enhancing detection success.

Findings

Success probability is significantly improved with the Bayesian learning approach.

The method enables online detection of change points in quantum state sequences.

Applicable to various quantum information processing tasks.

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.