AI-Augmented Parameter Initialization for QAOA

The recent interest in learning-augmented algorithms has motivated us to explore their applicability in quantum computing, specifically during the noisy intermediate-scale quantum (NISQ) era. This paper introduces a learning-augmented algorithm-like approach to enhance the Quantum Approximate Optimization Algorithm (QAOA) for max-cut problems, by improving initial parameter estimation. We trained a random forest regression model on the optimal solution parameters from various max-cut graphs, and then used this model to guess better initial parameters for previously unseen graphs. Experimental results demonstrate that our approach reduces the number of iterations of quantum computation required, thereby reducing noise and error. These findings indicate that integrating learning-augmented algorithm techniques can enhance the computational feasibility of solving QAOA problems in the NISQ era.