Year: 2024
Type: conference-paper
Abstract: 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 variational quantum algorithm 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 variational quantum algorithm 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.
Authors: Lucas Tecot, Cho-Jui Hsieh
Institution: University of California, Los Angeles
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W4406460251