Quantum optimization with a novel Gibbs objective function and ansatz architecture search

Abstract

This work proposes modifications on two key components of the Quantum Approximate Optimization Algorithm (QAOA): a Gibbs objective function and an ansatz architecture search algorithm. The authors explore how these propositions improve upon ordinary QAOA when finding low-energy states by using numerical experiments on grid and complete graph Ising models.