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Characterization of a Driven Two-Level Quantum System by Supervised Learning
Raphaël Couturier1, Etienne Dionis2, Stéphane Guérin2
1Université de Franche-Comté, CNRS, Institut FEMTO-ST, F-90000 Belfort, France.
Entropy (Basel, Switzerland)
|March 29, 2023
Summary
Supervised learning can characterize quantum systems. Neural networks accurately map control parameters to quantum state distances, but face challenges when inferring parameters from distances.
Area of Science:
- Quantum mechanics
- Machine learning
- Quantum control
Background:
- Characterizing quantum systems is crucial for quantum technologies.
- Supervised learning offers a data-driven approach to complex system analysis.
- Quantum control protocols aim to steer systems to desired states.
Purpose of the Study:
- To investigate the application of supervised learning for characterizing two-level quantum systems under external drives.
- To assess the efficacy of neural networks in interpolating mappings between control offsets and target state distances.
- To analyze the performance and limitations of supervised learning in direct and indirect quantum state estimation.
Main Methods:
- Applied supervised learning, specifically neural networks, to analyze data from a driven two-level quantum system.
- Tested various neural network algorithms for interpolating the mapping between control offsets and distances to a target state.
- Evaluated the estimation accuracy in direct (offset known) and indirect (distance known) scenarios.
Main Results:
- Neural networks accurately reproduced the mapping in the direct case with high precision.
- Significant obstacles were encountered in the indirect case, where estimation starts from the distance to the target.
- The study identified limitations of the estimation procedure based on the properties of the mapping function.
Conclusions:
- Supervised learning provides a powerful, global estimation method for quantum system characterization.
- The direct estimation of quantum state properties is highly feasible with neural networks.
- Indirect estimation presents challenges, highlighting the need to understand mapping properties for effective quantum control and characterization.
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