Two-Step QAOA: Enhancing Quantum Optimization by Decomposing K-hot Constraints in QUBO Formulations

TL;DR

This paper introduces Two-Step QAOA, a method that decomposes k-hot constraint problems in QUBO form to improve quantum optimization efficiency and effectiveness, especially for complex societal issues.

Contribution

It presents a novel two-stage approach to transform soft constraints into hard constraints, enhancing QAOA performance on complex combinatorial problems.

Findings

Improved optimization efficiency for k-hot constrained problems

Enhanced initial condition generation for QAOA

Better handling of complex societal optimization problems

Abstract

The Quantum Approximate Optimization Algorithm (QAOA) has shown promise in solving combinatorial optimization problems by leveraging quantum computational power. We propose a simple approach, the Two-Step QAOA, which aims to improve the effectiveness of QAOA by decomposing problems with k-hot encoding QUBO (Quadratic Unconstrained Binary Optimization) formulations. By identifying and separating the problem into two stages, we transform soft constraints into hard constraints, simplifying the generation of initial conditions and enabling more efficient optimization. The method is particularly beneficial for tackling complex societal problems that often involve intricate constraint structures.