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The Journal of Chemical Physics

Monotonically convergent algorithm for quantum optimal control with dissipation

Yukiyoshi Ohtsuki, Wusheng Zhu, HERSCHEL A. RABITZ

Published May 22, 1999• 172 citations
Publisher

Abstract

This paper extends a monotonically convergent algorithm for quantum optimal control to treat systems with dissipation. The algorithm working with the density matrix is proved to exhibit quadratic and monotonic convergence. Several numerical tests are implemented in three-level model systems. The algorithm is exploited to control various targets, including the expectation value of a Hermitian operator, the modulus square of the expectation value of a non-Hermitian operator, and off-diagonal elements of the density matrix.

DOI
10.1063/1.478036

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This paper extends a monotonically convergent algorithm for quantum optimal control to treat systems with dissipation. The algorithm working with the density matrix is proved to exhibit quadratic and monotonic convergence. Several numerical tests are implemented in three-level model systems. The algorithm is exploited to control various targets, including the expectation value of a Hermitian operator, the modulus square of the expectation value of a non-Hermitian operator, and off-diagonal elements of the density matrix.
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A uniform iteration method is presented for achieving quantum optimal control over any real objective with a positive semidefinite Hessian matrix. Theoretical analysis shows that this uniform algorithm exhibits quadratic and monotonic convergence. Numerical calculations verify that for this uniform algorithm, within a few steps, the optimized objective functional comes close to its converged limit. For some optimal control purposes, the objective itself is not required to be directly a physical observable, but it is only necessary that the objective have a suitable association with some desired physical observables. As an illustration of the algorithm, the control objective is chosen to achieve maximum population in a target state as well as minimum phase mismatch with the target state.
We develop a family of monotonically convergent iterative algorithms for solving a wide class of optimal control problems in which a dynamical system interacts nonlinearly with a control. The key idea is to divide a control into identical components and to introduce auxiliary steps to update each component at every iteration step. The algorithms are proved to exhibit monotonic convergence, which is also numerically confirmed through a case study of the control of molecular orientation. The numerical results show that high-quality solutions can be obtained by using the present algorithms.
A wide range of cost functionals that describe the criteria for designing optimal pulses can be reduced to two basic functionals by the introduction of product spaces. We extend previous monotonically convergent algorithms to solve the generalized pulse design equations derived from those basic functionals. The new algorithms are proved to exhibit monotonic convergence. Numerical tests are implemented in four-level model systems employing stationary and/or nonstationary targets in the absence and/or presence of relaxation. Trajectory plots that conveniently present the global nature of the convergence behavior show that slow convergence may often be attributed to "trapping" and that relaxation processes may remove such unfavorable behavior.
The non-linear optimization method developed by A. Konnov and V. Krotov [Autom. Remote Cont. (Engl. Transl.) 60, 1427 (1999)] has been used previously to extend the capabilities of optimal control theory from the linear to the non-linear Schrödinger equation [S. E. Sklarz and D. J. Tannor, Phys. Rev. A 66, 053619 (2002)]. Here we show that based on the Konnov-Krotov method, monotonically convergent algorithms are obtained for a large class of quantum control problems. It includes, in addition to nonlinear equations of motion, control problems that are characterized by non-unitary time evolution, nonlinear dependencies of the Hamiltonian on the control, time-dependent targets, and optimization functionals that depend to higher than second order on the time-evolving states. We furthermore show that the nonlinear (second order) contribution can be estimated either analytically or numerically, yielding readily applicable optimization algorithms. We demonstrate monotonic convergence for an optimization functional that is an eighth-degree polynomial in the states. For the "standard" quantum control problem of a convex final-time functional, linear equations of motion and linear dependency of the Hamiltonian on the field, the second-order contribution is not required for monotonic convergence but can be used to speed up convergence. We demonstrate this by comparing the performance of first- and second-order algorithms for two examples.