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arXiv:quant-ph/9906089 · Physical Review A

Efficient algorithm for optimal control of mixed-state quantum systems

S. G. Schirmer, Marvin D. Girardeau, J. V. Leahy

Published Dec 8, 1999• 51 citations
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Abstract

Zhu and Rabitz [J. Chem. Phys. 109, 385 (1998)] presented a rapidly convergent iterative algorithm for optimal control of the expectation value of a positive-definite observable in a pure-state quantum system. In this paper we generalize this algorithm to a quantum-statistical mechanics setting and show that it is both efficient in the mixed-state case and effective in achieving the control objective of maximizing the ensemble average of arbitrary observables in the cases studied.

DOI
10.1103/physreva.61.012101

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Zhu and Rabitz [J. Chem. Phys. 109, 385 (1998)] presented a rapidly convergent iterative algorithm for optimal control of the expectation value of a positive-definite observable in a pure-state quantum system. In this paper we generalize this algorithm to a quantum-statistical mechanics setting and show that it is both efficient in the mixed-state case and effective in achieving the control objective of maximizing the ensemble average of arbitrary observables in the cases studied.
Quantum dynamical simulations of statistical ensembles pose a significant computational challenge due to the fact that mixed states need to be represented. If the underlying dynamics is fully unitary, for example, in ultrafast coherent control at finite temperatures, then one approach to approximate time-dependent observables is to sample the density operator by solving the Schrödinger equation for a set of wave functions with randomized phases. We show that, on average, random-phase wave functions perform well for ensembles with high mixedness, whereas at higher purities a deterministic sampling of the energetically lowest-lying eigenstates becomes superior. We prove that minimization of the worst-case error for computing arbitrary observables is uniquely attained by eigenstate-based sampling. We show that this error can be used to form a qualitative estimate of the set of ensemble purities for which the sampling performance of the eigenstate-based approach is superior to random-phase wave functions. Furthermore, we present refinements to both schemes which remove redundant information from the sampling procedure to accelerate their convergence. Finally, we point out how the structure of low-rank observables can be exploited to further improve eigenstate-based sampling schemes.
The optimal control problem for open quantum systems can be formulated as a time-dependent Lindbladian that is parameterized by a number of time-dependent control variables. Given an observable and an initial state, the goal is to tune the control variables so that the expected value of some observable with respect to the final state is maximized. In this paper, we present algorithms for solving this optimal control problem efficiently, i.e., having a poly-logarithmic dependency on the system dimension, which is exponentially faster than best-known classical algorithms. Our algorithms are hybrid, consisting of both quantum and classical components. The quantum procedure simulates time-dependent Lindblad evolution that drives the initial state to the final state, and it also provides access to the gradients of the objective function via quantum gradient estimation. The classical procedure uses the gradient information to update the control variables. At the technical level, we provide the first (to the best of our knowledge) simulation algorithm for time-dependent Lindbladians with an ℓ1\ell_1ℓ1​-norm dependence. As an alternative, we also present a simulation algorithm in the interaction picture to improve the algorithm for the cases where the time-independent component of a Lindbladian dominates the time-dependent part. On the classical side, we heavily adapt the state-of-the-art classical optimization analysis to interface with the quantum part of our algorithms. Both the quantum simulation techniques and the classical optimization analyses might be of independent interest.
Upper and lower bounds are established for time-dependent ensemble averages of observables of driven quantum systems in mixed states. They limit controllability of observables independently of the control fields. Narrower bounds are established when the observable is a projector onto a pure quantum state or subspace. They are optimal in the sense of being kinematically achievable. Calculations on nonlinear optimal control of a four-level model indicate that these kinematical bounds are dynamically achievable asymptotically with increasing control pulse strength.
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.