4 research outputs found
Control design of uncertain quantum systems with fuzzy estimators
published_or_final_versio
Sampling-based Learning Control for Quantum Systems with Uncertainties
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.Comment: 11 pages, 9 figures, in press, IEEE Transactions on Control Systems
Technology, 201
Closed-Loop and Robust Control of Quantum Systems
For most practical quantum control systems, it is important and difficult to attain robustness and reliability due to unavoidable uncertainties in the system dynamics or models. Three kinds of typical approaches (e.g., closed-loop learning control, feedback control, and robust control) have been proved to be effective to solve these problems. This work presents a self-contained survey on the closed-loop and robust control of quantum systems, as well as a brief introduction to a selection of basic theories and methods in this research area, to provide interested readers with a general idea for further studies. In the area of closed-loop learning control of quantum systems, we survey and introduce such learning control methods as gradient-based methods, genetic algorithms (GA), and reinforcement learning (RL) methods from a unified point of view of exploring the quantum control landscapes. For the feedback control approach, the paper surveys three control strategies including Lyapunov control, measurement-based control, and coherent-feedback control. Then such topics in the field of quantum robust control as H∞ control, sliding mode control, quantum risk-sensitive control, and quantum ensemble control are reviewed. The paper concludes with a perspective of future research directions that are likely to attract more attention