QuOCS: The Quantum Optimal Control Suite

TL;DR

QuOCS is a versatile software suite that unifies experimental and model-based quantum optimal control algorithms, facilitating improved performance across various quantum technologies and physical phenomena.

Contribution

It introduces an easy-to-use, integrated framework combining gradient-free and gradient-based algorithms for quantum optimal control, with flexible experimental connectivity.

Findings

Successfully applied to optimize quantum systems in open- and closed-loop settings

Supports integration with existing experimental control software like Qudi

Available as open-source software on GitHub and PyPI

Abstract

Quantum optimal control includes the family of pulse-shaping algorithms that aim to unlock the full potential of a variety of quantum technologies. Our Quantum Optimal Control Suite (QuOCS) unites experimental focus and model-based approaches in a unified framework. The easy usage and installation of QuOCS and the availability of various combinable optimization strategies is designed to improve the performance of many quantum technology platforms, such as color defects in diamond, superconducting qubits, atom- or ion-based quantum computers. It can also be applied to the study of more general phenomena in physics. In this paper, we describe the software and the main toolbox of gradient-free and gradient-based algorithms. We then show how the user can connect it to their experiment. In addition, we provide illustrative examples where our optimization suite solves typical quantum optimal control problems, in both open- and closed-loop settings. Integration into existing experimental control software is already provided for the experiment control software Qudi [J. M. Binder et al., SoftwareX, 6, 85-90, (2017)], and further extensions are investigated and highly encouraged. QuOCS is available from GitHub, under Apache License 2.0, and can be found on the PyPI repository.