Taming quantum systems: A tutorial for using shortcuts-to-adiabaticity, quantum optimal control, and reinforcement learning

TL;DR

This paper provides a comprehensive tutorial on quantum control techniques, including shortcuts to adiabaticity, optimal control, and machine learning, aimed at newcomers to facilitate research and development in quantum technologies.

Contribution

It offers a pedagogical overview of core quantum control methods with detailed theoretical explanations and practical examples, bridging foundational concepts and applications.

Findings

Detailed analytical derivations of control methods

Extensive numerical simulations demonstrating techniques

Discussion on integration of control methods in quantum tech development

Abstract

Precise manipulation of quantum effects at the atomic and nanoscale has become an essential task in ongoing scientific and technological endeavours. Quantum control methods are thus routinely exploited for research in areas such as quantum materials, quantum chemistry, and atomic and molecular physics, as well as in the development of quantum technologies like computing, simulation, and sensing. Here, we present a pedagogical introduction to the basics of quantum control methods in tutorial form, with the aim of providing newcomers to the field with the core concepts and practical tools to use these methods in their research. We focus on three areas: shortcuts to adiabaticity, quantum optimal control, and machine-learning-based control. We lay out the basic theoretical elements of each area in a pedagogical way and describe their application to a series of example cases. For these, we include detailed analytical derivations as well as extensive numerical results. As an outlook, we discuss quantum control methods in the broader context of quantum technologies development and complex quantum systems research, outlining potential connections and synergies between them.