Quantum Optimal Control: Practical Aspects and Diverse Methods

TL;DR

This paper reviews quantum optimal control (QOC), discussing its fundamental concepts, practical challenges, and a wide range of methods including gradient, variational, and machine learning approaches for implementing precise quantum operations.

Contribution

It provides a comprehensive overview of QOC methods, highlighting practical aspects and diverse techniques developed over the past two decades.

Findings

Various QOC methods have been proposed, including asymptotic, direct search, gradient, variational, and machine learning approaches.

Practical challenges in implementing QOC are discussed.

The review emphasizes the diversity and evolution of QOC techniques.

Abstract

Quantum controls realize the unitary or nonunitary operations employed in quantum computers, quantum simulators, quantum communications, and other quantum information devices. They implement the desired quantum dynamics with the help of electric, magnetic, or electromagnetic control fields. Quantum optimal control (QOC) deals with designing an optimal control field modulation that most precisely implements a desired quantum operation with minimum energy consumption and maximum robustness against hardware imperfections as well as external noise. Over the last two decades, numerous QOC methods have been proposed. They include asymptotic methods, direct search, gradient methods, variational methods, machine learning methods, etc. In this review, we shall introduce the basic ideas of QOC, discuss practical challenges, and then take an overview of the diverse QOC methods.