Hybrid Quantum-HPC Solutions for Max-Cut: Bridging Classical and Quantum Algorithms

TL;DR

This paper investigates the integration of QAOA into hybrid quantum-HPC systems for Max-Cut, analyzing performance, scalability, and communication overhead through theoretical modeling and simulations, highlighting potential advantages over classical algorithms.

Contribution

It introduces a theoretical framework and simulation-based evaluation of QAOA in hybrid systems, assessing scalability and performance for large Max-Cut instances.

Findings

QAOA shows promising solution accuracy on small instances.

Hybrid systems can potentially outperform classical algorithms at scale.

Scalability of QAOA depends on problem size and system resources.

Abstract

This research explores the integration of the Quantum Approximate Optimization Algorithm (QAOA) into Hybrid Quantum-HPC systems for solving the Max-Cut problem, comparing its performance with classical algorithms like brute-force search and greedy heuristics. We develop a theoretical model to analyze the time complexity, scalability, and communication overhead in hybrid systems. Using simulations, we evaluate QAOA's performance on small-scale Max-Cut instances, benchmarking its runtime, solution accuracy, and resource utilization. The study also investigates the scalability of QAOA with increasing problem size, offering insights into its potential advantages over classical methods for large-scale combinatorial optimization problems, with implications for future Quantum computing applications in HPC environments.