We present the results of a comprehensive study of optimization algorithms for the calibration of quantum devices. As part of our ongoing efforts to automate bring-up, tune-up, and system identification procedures, we investigate a broad range of optimizers within a simulated environment designed to closely mimic the challenges of real-world experimental conditions. Our benchmark includes widely used algorithms such as Nelder-Mead and the state-of-the-art Covariance Matrix Adaptation Evolution Strategy (CMA-ES). We evaluate performance in both low-dimensional settings, representing simple pulse shapes used in current optimal control protocols with a limited number of parameters, and high-dimensional regimes, which reflect the demands of complex control pulses with many parameters. Based on our findings, we recommend the CMA-ES algorithm and provide empirical evidence for its superior performance across all tested scenarios.
@article{arxiv.2509.08555,
title = {Benchmarking Optimization Algorithms for Automated Calibration of Quantum Devices},
author = {Kevin Pack and Shai Machnes and Frank K. Wilhelm},
journal= {arXiv preprint arXiv:2509.08555},
year = {2026}
}