Characterization of a Driven Two-Level Quantum System by Supervised Learning
- Resource Type
- article
- Authors
- Raphaël Couturier; Etienne Dionis; Stéphane Guérin; Christophe Guyeux; Dominique Sugny
- Source
- Entropy, Vol 25, Iss 3, p 446 (2023)
- Subject
- optimal control
supervised learning
system characterization
two-level quantum systems
Science
Astrophysics
QB460-466
Physics
QC1-999
- Language
- English
- ISSN
- 1099-4300
We investigate the extent to which a two-level quantum system subjected to an external time-dependent drive can be characterized by supervised learning. We apply this approach to the case of bang-bang control and the estimation of the offset and the final distance to a given target state. For any control protocol, the goal is to find the mapping between the offset and the distance. This mapping is interpolated using a neural network. The estimate is global in the sense that no a priori knowledge is required on the relation to be determined. Different neural network algorithms are tested on a series of data sets. We show that the mapping can be reproduced with very high precision in the direct case when the offset is known, while obstacles appear in the indirect case starting from the distance to the target. We point out the limits of the estimation procedure with respect to the properties of the mapping to be interpolated. We discuss the physical relevance of the different results.