# Randomized Benchmarking of Local Zeroth-Order Optimizers for Variational Quantum Systems

arXiv: https://arxiv.org/abs/2310.09468 · HTML: https://mlsanity.com/paper/2310.09468/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 25 references that are in the map.
- First posted on arXiv 14 Oct 2023.

## Builds on (most cited first, 6 of 6)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Variational Quantum Algorithms](https://mlsanity.com/paper/2012.09265/) (2020)
- [On quantum backpropagation, information reuse, and cheating measurement collapse](https://mlsanity.com/paper/2305.13362/) (2023)
- [Do Quantum Circuit Born Machines Generalize?](https://mlsanity.com/paper/2207.13645/) (2022)
- [Natural Evolutionary Strategies for Variational Quantum Computation](https://mlsanity.com/paper/2012.00101/) (2020)
- [Adaptive shot allocation for fast convergence in variational quantum algorithms](https://mlsanity.com/paper/2108.10434/) (2021)

## Abstract (arXiv)

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.