Sampling-based Learning Control for Quantum Systems with Uncertainties

Abstract

Robust control design for quantum systems has been recognized as a key task in the development of practical quantum technology. In this paper, we present a systematic numerical methodology of sampling-based learning control (SLC) for control design of quantum systems with uncertainties. The SLC method includes two steps of "training" and "testing". In the training step, an augmented system is constructed using artificial samples generated by sampling uncertainty parameters according to a given distribution. A gradient flow based learning algorithm is developed to find the control for the augmented system. In the process of testing, a number of additional samples are tested to evaluate the control performance where these samples are obtained through sampling the uncertainty parameters according to a possible distribution. The SLC method is applied to three significant examples of quantum robust control including state preparation in a three-level quantum system, robust entanglement generation in a two-qubit superconducting circuit and quantum entanglement control in a two-atom system interacting with a quantized field in a cavity. Numerical results demonstrate the effectiveness of the SLC approach even when uncertainties are quite large, and show its potential for robust control design of quantum systems.

11 pages, 9 figures, in press, IEEE Transactions on Control Systems Technology, 2015

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• Quantum Ensemble Classification: A Sampling-based Learning Control Approach
• Assessing three closed-loop learning algorithms by searching for high-quality quantum control pulses
• Learning control of quantum systems using frequency-domain optimization algorithms
• Supervised learning for robust quantum control in composite-pulse systems
• Robust Quantum Control against Clock Noises in Multi-Qubit Systems
• Risk-sensitive Optimization for Robust Quantum Controls
• Learning Control of Quantum Systems
• Reinforcement Learning vs. Gradient-Based Optimisation for Robust Energy Landscape Control of Spin-1/2 Quantum Networks
• Modelling and Control of Quantum Measurement Induced Backaction in Double Quantum Dots
• Fast State Stabilization using Deep Reinforcement Learning for Measurement-based Quantum Feedback Control
• Using optimal control to guide neural-network interpolation of continuously-parameterized gates