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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
PubMed
Summary
This summary is machine-generated.

Supervised learning can characterize quantum systems. Neural networks accurately map control parameters to quantum state distances, but face challenges when inferring parameters from distances.

Keywords:
optimal controlsupervised learningsystem characterizationtwo-level quantum systems

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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.