Learning-based Quantum Robust Control: Algorithm, Applications and Experiments

TL;DR

This paper introduces an improved differential evolution algorithm, msMS_DE, for robust quantum control, demonstrating its effectiveness through numerical simulations and experimental laser control applications.

Contribution

The paper presents a novel msMS_DE algorithm with multiple sampling and mixed mutation strategies for robust quantum control, applied to quantum ensembles, networks, and femtosecond laser experiments.

Findings

msMS_DE outperforms existing algorithms in quantum control tasks

Successful experimental optimization of femtosecond laser pulses

Effective control of molecular processes using the proposed method

Abstract

Robust control design for quantum systems has been recognized as a key task in quantum information technology, molecular chemistry and atomic physics. In this paper, an improved differential evolution algorithm, referred to as \emph{msMS}\_DE, is proposed to search robust fields for various quantum control problems. In \emph{msMS}\_DE, multiple samples are used for fitness evaluation and a mixed strategy is employed for the mutation operation. In particular, the \emph{msMS}\_DE algorithm is applied to the control problems of (i) open inhomogeneous quantum ensembles and (ii) the consensus goal of a quantum network with uncertainties. Numerical results are presented to demonstrate the excellent performance of the improved machine learning algorithm for these two classes of quantum robust control problems. Furthermore, \emph{msMS}\_DE is experimentally implemented on femtosecond laser control applications to optimize two-photon absorption and control fragmentation of the molecule $\text{CH}_2\text{BrI}$. Experimental results demonstrate excellent performance of \emph{msMS}\_DE in searching for effective femtosecond laser pulses for various tasks.