Randomized Benchmarking of Local Zeroth-Order Optimizers for Variational Quantum Systems
This work addresses the need for generalizable optimizer benchmarks in quantum information research, though it is incremental as it builds on existing benchmarking studies.
The paper tackled the problem of benchmarking classical optimizers for variational quantum systems by comparing local zeroth-order optimizers across randomized tasks to broadly sample quantum optimization problems, resulting in insights to motivate future improvements.
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 optimization 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 optimization 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.