Enhanced Framework of Quantum Approximate Optimization Algorithm and Its Parameter Setting Strategy

TL;DR

This paper introduces an enhanced version of the quantum approximate optimization algorithm (QAOA) that offers increased computational power and flexibility, along with efficient parameter setting strategies, enabling faster convergence to optimal solutions in quantum optimization tasks.

Contribution

The paper proposes an improved QAOA framework with a new parameter setting strategy that reduces complexity and enhances performance compared to traditional QAOA.

Findings

Enhanced QAOA matches effectiveness of original but with greater flexibility

Proper parameters enable faster convergence to optimal solutions

Simulation results show high probability of finding solutions in fewer iterations

Abstract

An enhanced framework of quantum approximate optimization algorithm (QAOA) is introduced and the parameter setting strategies are analyzed. The enhanced QAOA is as effective as the QAOA but exhibits greater computing power and flexibility, and with proper parameters, it can arrive at the optimal solution faster. Moreover, based on the analysis of this framework, strategies are provided to select the parameter at a cost of $O(1)$. Simulations are conducted on randomly generated 3-satisfiability (3-SAT) of scale of 20 qubits and the optimal solution can be found with a high probability in iterations much less than $O(\sqrt{N})$