SKQuant-Opt: Optimizers for Noisy Intermediate-Scale Quantum Devices

Classical optimizers play an important role in quantum computing, e.g., in the hybrid Variational Quantum Eigensolver (VQE) and Quantum Approximate Optimization algorithms. They are used in hyperparameter tuning, calibration, machine learning, etc. Unfortunately, most of the easily accessible optimizers do not handle noise well, leaving them below threshold for use with Noisy Intermediate-Scale Quantum (NISQ) devices. We present skquant-opt, part of scikit-quant.org, a set of optimizers tuned for the needs of NISQ. We have taken the state-of-the-art optimizers and tested them on a range of VQE applications and on hyperparameter tuning for optimization on D-Wave. Mesh methods, including hybrids that add local models, yield the best results. We present these results as well as guidance on use. Collected in skquant-opt, we provide the best optimizers in Python, the most used language in quantum computing, through the standard channels. The interfaces are made consistent with default parameters attuned to quantum computing problems, allowing for easy application and fast evaluation.