  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.
