@inproceedings{
tecot2023randomized,
title={Randomized Benchmarking of Local Zeroth-Order Optimizers for Variational Quantum Systems},
author={Lucas Tecot and Cho-Jui Hsieh},
booktitle={NeurIPS 2023 AI for Science Workshop},
year={2023},
url={https://openreview.net/forum?id=uBQrcgtYwK}
}
In the field of quantum information, classical optimizers play an important role. From experimentalists optimizing their physical devices to theorists exploring variational quantum algorithms, many aspects of quantum information require the use of a classical optimizer. For this reason, there are many papers that benchmark the effectiveness of different optimizers for specific quantum learning tasks and choices of parameterized algorithms. However, for researchers exploring new algorithms or physical devices, the insights from these studies don't necessarily translate. To address this concern, we compare the performance of classical optimizers across a series of partially-randomized tasks to more broadly sample the space of quantum learning problems. We focus on local zeroth-order optimizers due to their generally favorable performance and query-efficiency on quantum systems. We discuss insights from these experiments that can help motivate future works to improve these optimizers for use on quantum systems.
Randomized Benchmarking of Local Zeroth-Order Optimizers for Variational Quantum Systems
Lucas Tecot Cho-Jui Hsieh