Control of open quantum systems

J. W. Clark, Dennis G. Lucarelli, Tzyh‐Jong Tarn

Fangfang Meng, Shuang Cong

Abstract State transition control of open quantum systems is a research hotspot and difficulty. The control method in the light of the Lyapunov stability theory is a common method. Current research shows that if the open quantum systems meet the constraints that the free Hamiltonian is strong regular and the interaction Hamiltonian is fully connected, by devising appropriate control fields in the light of the Lyapunov stability theory, the open quantum system can move to the desired final state. In view of the fact that most practical open quantum systems do not meet these two constraints, this paper propose a control method. The implicit function perturbation terms are introduced into the control fields to resolve the problem that the open quantum systems do not meet these two convergence constraints. And the main control fields are devised in the light of the Lyapunov stability theory to make the state transition of the open quantum systems converging to the desired final state.

Yunyan Lee, Ian R. Petersen, Daoyi Dong

This paper presents a robust control approach for a two-level open quantum system subject to bounded uncertainties through the application of model predictive control. We demonstrate two common cases: depolarizing decoherence and phase-damping decoherence in open quantum systems. We develop a model predictive control method using quantum measurements and provide a lower bound on the probability of obtaining the predicted state. Numerical results illustrate the effectiveness of this model predictive control approach in achieving stability and robustness in open quantum systems.

Manoj Kumar

P. Zoller, J. I. Cirac, S. M. Girvin +1 more

Yingying Sun, Chengzhi Wu, Zhangqing Zhu +1 more

Quantum control landscape is generally a physical objective defined as a functional of the control field and plays an important role for the analysis and manipulation of quantum systems. In this paper, we focus on typical learning methods (i.e., gradient decent method, genetic algorithm and deferential evolution) for the landscape control of open quantum systems and explores the characteristics of these different types of learning algorithms. Taking a two-level open quantum system as an example, the optimal value of the control landscape can be obtained under varying Lindblad operators that reflect the system's interactions with the environment. Numerical experiments demonstrate the learning performances to acquire the optimal control strategy by exploring the control landscape of this two-level open quantum system.