  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.
