# Multistart Methods for Quantum Approximate Optimization

## TL;DR

This paper explores multistart optimization techniques within the QAOA framework to enhance quantum algorithm performance on graph clustering problems, emphasizing the benefits of reusing parameters from similar instances.

## Contribution

It introduces a multistart approach for QAOA and demonstrates the advantage of reusing parameters across similar problems to improve optimization outcomes.

## Key findings

- Multistart methods improve QAOA performance on graph clustering.
- Reusing parameters from similar problems enhances classical optimizer efficiency.
- The approach addresses local optima issues in quantum-classical hybrid algorithms.

## Abstract

Hybrid quantum-classical algorithms such as the quantum approximate optimization algorithm (QAOA) are considered one of the most promising approaches for leveraging near-term quantum computers for practical applications. Such algorithms are often implemented in a variational form, combining classical optimization methods with a quantum machine to find parameters to maximize performance. The quality of the QAOA solution depends heavily on quality of the parameters produced by the classical optimizer. Moreover, the presence of multiple local optima in the space of parameters makes it harder for the classical optimizer. In this paper we study the use of a multistart optimization approach within a QAOA framework to improve the performance of quantum machines on important graph clustering problems. We also demonstrate that reusing the optimal parameters from similar problems can improve the performance of classical optimization methods, expanding on similar results for MAXCUT.