Characterization of a driven two-level quantum system by Supervised Learning
arXiv:2212.11166 · doi:10.3390/e25030446
Abstract
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.
19 pages, 5 figures
References in corpus (29)
- Quantum-enhanced machine learning
- Introduction to the Pontryagin Maximum Principle for Quantum Optimal Control
- Artificial Intelligence and Machine Learning for Quantum Technologies
- Deep Reinforcement Learning for Quantum Gate Control
- Identifying optimal cycles in quantum thermal machines with reinforcement-learning
- Measurement Based Feedback Quantum Control With Deep Reinforcement Learning for Double-well Non-linear Potential
- Reinforcement Learning for Many-Body Ground-State Preparation Inspired by Counterdiabatic Driving
- Integrated tool-set for Control, Calibration and Characterization of quantum devices applied to superconducting qubits
- Hamiltonian identifiability assisted by single-probe measurement
- A Tutorial on Optimal Control and Reinforcement Learning methods for Quantum Technologies
- Reinforcement learning approach to non-equilibrium quantum thermodynamics
- Variational principle for optimal quantum controls in quantum metrology
- Breaking Adiabatic Quantum Control with Deep Learning
- Using deep learning to understand and mitigate the qubit noise environment
- Reinforcement learning-enhanced protocols for coherent population-transfer in three-level quantum systems
- Evolution-free Hamiltonian parameter estimation through Zeeman markers
- Estimating the degree of non-Markovianity using machine learning
- Learning quantum dynamics with latent neural ODEs
- Quantum imaginary time evolution steered by reinforcement learning
- Optimal Control for Quantum Metrology via Pontryagin's principle
- Robust quantum gates using smooth pulses and physics-informed neural networks
- Application of Pontryagin's Maximum Principle to Quantum Metrology in Dissipative Systems
- Machine Learning for Continuous Quantum Error Correction on Superconducting Qubits
- Machine-learning assisted quantum control in random environment
- Optimizing fingerprinting experiments for parameter identification: Application to spin systems
- Stochastic learning control of adiabatic speedup in a non-Markovian open qutrit system
- Robust optimization for quantum reinforcement learning control using partial observations
- A greedy reconstruction algorithm for the identification of spin distribution
- Newton algorithm for Hamiltonian characterization in quantum control