Machine learning applied to quantum synchronization-assisted probing
This work addresses the characterization of quantum environments with arbitrary spectral densities, which is an incremental advancement in quantum probing techniques.
The authors tackled the problem of probing an out-of-equilibrium quantum system by leveraging quantum synchronization between a probe qubit and the system, using machine learning to infer dissipation features like the environment Ohmicity index from probe observables, and showed that performance in classification and regression is significantly improved due to synchronization transitions.
A probing scheme is considered with an accessible and controllable qubit, used to probe an out-of equilibrium system consisting of a second qubit interacting with an environment. Quantum spontaneous synchronization between the probe and the system emerges in this model and, by tuning the probe frequency, can occur both in-phase and in anti-phase. We analyze the capability of machine learning in this probing scheme based on quantum synchronization. An artificial neural network is used to infer, from a probe observable, main dissipation features, such as the environment Ohmicity index. The efficiency of the algorithm in the presence of some noise in the dataset is also considered. We show that the performance in either classification and regression is significantly improved due to the in/anti-phase synchronization transition. This opens the way to the characterization of environments with arbitrary spectral densities.