QuOCS: The quantum optimal control suite - Rossignolo, Marco; Reisser, Thomas; Marshall, Alastair; Rembold, Phila; Pagano, Alice; Vetter, Philipp J.; Said, Ressa S.; Müller, Matthias M.; Motzoi, Felix; Calarco, Tommaso; Jelezko, Fedor; Montangero, Simone (Computer Physics Communications, 2023)
Quantum optimal control includes a family of pulse-shaping algorithms that aim to unlock the full potential of a variety of quantum technologies. The Quantum Optimal Control Suite (QuOCS) unites experimental focus and model-based approaches in a unified framework. Easy usage and installation presented here 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 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 (Binder et al., 2017 [41]), 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. Program summary: Program Title: QuOCS - Quantum Optimal Control Suite CPC Library link to program files: https://doi.org/10.17632/wjjch757fk.1 Developer's repository link: https://github.com/Quantum-OCS/QuOCS Licensing provisions: Apache-2.0 Programming language: Python External routines: NumPy [1], SciPy [1], JAX [2] Nature of problem: Quantum systems are typically controlled by time-dependent electromagnetic fields to perform a certain set of quantum operations. Those operations may in turn provide building blocks for various quantum information processing tasks such as quantum computation, communication, simulation, sensing, and metrology. Numerous control strategies exist to design and improve such operations [3]. While some strategies are constructed to target a rather specific problem with high efficiency, others are more general to solve a wide range of applications [4,5]. To access the different algorithms, one has to download different optimization suites with different input and output parameters, making them hard to compare and combine. To benefit from the variety of algorithms, we have devised a customizable and intuitive optimization suite that simultaneously provides access to some of the most popular quantum optimal control algorithms. Solution method: We combine, in a unified framework, some of the frequently used optimal control algorithms which are the dressed Chopped Random Basis method (dCRAB) [6], and Gradient Ascent Pulse Engineering (GRAPE) [7], with an extension to make use of Automatic Differentiation (AD) [8]. The software is able to connect to both models of quantum dynamics in simulations and real-time quantum experiments to perform open- and closed-loop optimization, respectively. With minimal knowledge of optimal control theory, the user can manage to run optimizations of quantum processes using a variety of additional features such as stopping criteria and drift compensation. Logging and data management of the optimization progress and results are also handled by the suite. Its modular structure allows for extensions that accommodate customized algorithms and can be implemented by the user straightforwardly. Additional comments including unusual features: The connection to the experiments is performed by an extension that enables a direct integration to a laboratory control software Qudi [9]. References: [1] T.E. Oliphant, Comput. Sci. Eng. 9 (2007) 10, http://www.scipy.org/. [2] J. Bradbury, et al., JAX: composable transformations of Python+NumPy programs, http://github.com/google/jax, 2018. [3] S. Glaser, U. Boscain, T. Calarco, et al., Eur. Phys. J. D 69 (2015) 279. [4] C.P. Koch, U. Boscain, T. Calarco, et al., EPJ Quantum Technol. 9 (2022) 19. [5] Schaefer, Ido, Ronnie Kosloff, Phys. Rev. A 101 (2) (2020). [6] N. Rach, M.M. Müller, T. Calarco, S. Montangero, Phys. Rev. A 92 (2015) 6. [7] N. Khaneja, et al., J. Magn. Reson. 92 (6) (2015) 296–305. [8] N. Leung, et al., Phys. Rev. A 95 (2017) 4. [9] J.M. Binder, et al., SoftwareX 6 (2017) 85–90.
Created from Fastlane, Scopus look-up
Keywords: Adaptive (feedforward) optimization; Algorithm; Closed-loop; Customization; Optimal control; Python 3; Quantum