QuOCS: The Quantum Optimal Control Suite

• URL: http://arxiv.org/abs/2212.11144v2
• Date: Thu, 22 Dec 2022 21:19:00 GMT
• Title: QuOCS: The Quantum Optimal Control Suite
• Authors: Marco Rossignolo, Thomas Reisser, Alastair Marshall, Phila Rembold, Alice Pagano, Philipp J. Vetter, Ressa S. Said, Matthias M. M\"uller, Felix Motzoi, Tommaso Calarco, Fedor Jelezko, Simone Montangero
• Abstract summary: Our Quantum Optimal Control Suite (QuOCS) unites experimental focus and model-based approaches in a unified framework. It 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.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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