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Journal of the Graduate School of the Chinese Academy of Sciences

Optimal Control of Closed Quantum Systems Based on Optimized Search Step Length

Shuang Cong

Published Jan 1, 2006• 4 citations
OpenAlex

Abstract

Equation set of a class of optimal control problems is deduced in quantum mechanical systems based on the variational approach. Furthermore, an on-off variable metric numerical iterative arithmetic with optimized search step length is used to solve such an optimal problem. Finally, some simulation experiments on a spin-1/2 particle system are done. Experimental results demonstrate the validity of the method proposed. At the same time, the effectiveness of the system control under different parameters in the performance index is analyzed.

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SIAM Journal on Scientific Computing•Jan 1, 2015•29 citations
Equation set of a class of optimal control problems is deduced in quantum mechanical systems based on the variational approach. Furthermore, an on-off variable metric numerical iterative arithmetic with optimized search step length is used to solve such an optimal problem. Finally, some simulation experiments on a spin-1/2 particle system are done. Experimental results demonstrate the validity of the method proposed. At the same time, the effectiveness of the system control under different parameters in the performance index is analyzed.
Hybrid quantum-classical algorithms hold great promise for solving quantum control problems on near-term quantum computers. In this work, we employ the hybrid framework that integrates digital quantum simulation with classical optimization to achieve optimal engineering of quantum many-body systems. To evaluate the overall performance of this method, we introduce a general metric termed control optimality, which accounts for constraints on both classical and quantum components. As a concrete example, we investigate the time-optimal control for perfect state transfer in a one-dimensional spin model using the variational quantum algorithm, closely approaching the quantum speed limit. Moreover, we discuss the emergent gradient behavior and error robustness, demonstrating the feasibility of applying hybrid quantum algorithms to solve quantum optimal control problems. These results establish a systematic framework for hybrid algorithms to address quantum control problems on near-term quantum platforms.
By means of the achievement of bilinear systems theory research in the macroscopic fields, associated with the characteristic that the mathematical model of quantum mechanical system is bilinear, an iterative optimal controller for a quantum mechanical system is designed through the derivation of the optimal controller with two points boundary values problem. Also by the simulation experiment of states transfer control of the designed quantum system, the effects to control results with different control parameters are detailly analyzed.
Abstract Based on Bures distance, a Lyapunov function that represents the distance between a desired state and the actual state of a quantum system is selected. Considering the cases that an initial state is and is not orthogonal to the desired state respectively, we propose a class of control strategies with state feedback that ensures the stability of the closed-loop control system. Especially, the asymptotic stability of the control system is analyzed, deduced and proved in detail. Finally, a simulation experiment on a spin-1/2 particle system is done and the relation between the system state evolution time and control value is analyzed with different parameters. Research results have general theoretical meaning for control of quantum systems.
By means of the design method for bilinear systems in the macroscopic field in association with the characteristic that the mathematical model of pure state quantum system is bilinear,and of selecting the performance index of the controlled system,the optimal control strategy in quantum systems was proposed based on the Lyapunov stability theorem.The detailed design process of optimal control law was derived.Some simulation experiments were performed on a spin-1/2 particle system.Experimental results demonstrate the effectiveness of the method proposed.At the same time,the influence of different parameters on the system control effects is analyzed.
The design of optimal final-state controllers of quantum-mechanical systems that are insensitive to errors in the molecular Hamiltonian or to errors in the initial state of the system is considered. Control arises through the interaction of the system with an external field; the goal is optimal design of these latter fields for various physical objectives in the presence of system uncertainty. Sensitivity to modeling errors and other uncertainties in the molecular Hamiltonian is minimized by considering averaged costs for a family of Hamiltonian functions H0{\mathit{H}}_{0}H0​(\ensuremath{\alpha}) indexed by the random variable \ensuremath{\alpha} taking values on a compact set in Euclidean space. Similarly, sensitivity of the optimal control to the initial state is minimized by viewing the initial condition as a Hilbert-space-valued random variable and considering an optimization problem with a cost functional that is averaged over the class of initial conditions. A precise formulation of the control problem is given, and its well-posedness is established. Cost propagators are defined to display the dependence of the performance index on the initial conditions explicitly, which allows analytic averaging of initial conditions.The constrained optimization problem is reduced to an unconstrained optimization problem by the introduction of Lagrange-multiplier operators. Necessary conditions for the unconstrained problem provide the basis for a gradient search for an optimal solution. Finite-difference schemes are utilized to provide a numerical approximation of the optimal control problem. Numerical examples are given for final-state control of a diatomic molecule represented by a Morse potential illustrating design for systems with initial-phase uncertainty and parametric uncertainty. The resultant insensitive controllers execute different strategies depending on the design requirements. The controller designed to be insensitive to errors in the initial phases adopts a strategy of phase imprinting during the initial stages of the control interval to compensate for a lack of knowledge of the initial phases. It is also shown that it is not possible to coerce a system from a state with completely random initial phases to a correlated state using the class of averaged controllers considered here. The controller designed to be insensitive to parametric Hamiltonian errors adopts a strategy of amplitude restraint to prevent the wave packet from taking significant excursions into the regions where the potential is uncertain. The interesting structure exhibited by the controllers in response to the different design requirements, and the superior performance of the insensitive controllers when compared with controllers designed at nominal phases and parameters, illustrate the usefulness of the cost-averaging technique for design in the presence of uncertainties.
An efficient and robust computational framework for solving closed quantum spin optimal-control and exact-controllability problems with control constraints is presented. Closed spin systems are of fundamental importance in modern quantum technologies such as nuclear magnetic resonance (NMR) spectroscopy, quantum imaging, and quantum computing. These systems are modeled by the Liouville--von Neumann master (LvNM) equation describing the time evolution of the density operator representing the state of the system. A unifying setting is provided to discuss optimal-control and exact-controllability results. Different controllability results for the LvNM model are given, and necessary optimality conditions for the LvNM control problems are analyzed. Existence and regularity of optimal controls are proved. The computational framework is based on matrix-free reduced-Hessian semismooth Krylov--Newton schemes for solving optimal-control problems of the LvNM equation in a real vector space rotating-frame representation. A continuation technique is designed to solve closed spin exact-controllability problems that is based on the solution of an appropriately formulated optimal-control problem. These computational strategies are put into a rigorous theoretical framework, proving convergence to the solutions sought. Results of numerical experiments validate the theoretical results and demonstrate the computational ability of the proposed framework to solve closed quantum spin control problems.