Machine learning applied to quantum synchronization-assisted probing
arXiv:1901.05230 · doi:10.1002/qute.201800085
Abstract
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.
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Cited by in corpus (5)
- Spectral Density Classification For Environment Spectroscopy
- Predicting the Onset of Quantum Synchronization Using Machine Learning
- A machine learning based approach to the identification of spectral densities in quantum open systems
- Machine learning non-Markovian two-level quantum noise spectroscopy
- Multiclass classification of dephasing channels