Quantum Sensing Leverages ML to Track Three-Level System Phase

Researchers affiliated with the Dipartimento di Fisica e Astronomia ”Ettore Majorana”, Università di Catania, Italy have successfully trained a multi-layer perceptron (MLP) to estimate the plaquette phase within a three-level system, demonstrating a new method for extracting information using artificial intelligence. The team utilized STImulated Raman Adiabatic Passage (STIRAP) population transfer efficiencies as the data source for the machine learning model, establishing a direct link between a specific quantum control technique and AI-driven analysis. This plaquette phase profoundly affects system dynamics by breaking coherent population trapping and inducing a non-trivial phase dependence, according to the work. The results highlight how…