Improving Quantum Optimization with Warm Starts

Quantum Approximate Optimization Algorithm (QAOA) is a leading candidate for solving discrete optimization problems with potential quantum advantage. However, the practical performance of QAOA on current quantum hardware remains constrained by device limitations. A common strategy to address these challenges is to leverage insights from classical solvers to enhance quantum algorithms. In this work, we introduce a novel warm-starting approach for QAOA that integrates classical knowledge to improve solution quality. Hardware experiments and extensive simulations demonstrate the effectiveness of our method, underscoring the value of hybrid strategies in advancing quantum optimization.