GPU-Accelerated Distributed QAOA on Large-scale HPC Ecosystems

TL;DR

This paper presents a GPU-accelerated, distributed implementation of DQAOA on HPC systems, significantly improving scalability and performance for large combinatorial optimization problems using advanced decomposition and parallelization techniques.

Contribution

It introduces a scalable, GPU-accelerated approach for DQAOA on HPC systems, enhancing performance and enabling larger problem instances through advanced decomposition and workload management.

Findings

Achieved up to 10x speedup over CPU-based simulations.

Demonstrated improved scalability for large problem instances.

Validated the effectiveness of advanced decomposition strategies.

Abstract

Quantum computing holds great potential to accelerate the process of solving complex combinatorial optimization problems. The Distributed Quantum Approximate Optimization Algorithm (DQAOA) addresses high-dimensional, dense problems using current quantum computing techniques and high-performance computing (HPC) systems. In this work, we improve the scalability and efficiency of DQAOA through advanced problem decomposition and parallel execution using message passing on the Frontier CPU/GPU supercomputer. Our approach ensures efficient quantum-classical workload management by distributing large problem instances across classical and quantum resources. Experimental results demonstrate that enhanced decomposition strategies and GPU-accelerated quantum simulations significantly improve DQAOA's performance, achieving up to 10x speedup over CPU-based simulations. This advancement enables better scalability for large problem instances, supporting the practical deployment of GPU systems for hybrid quantum-classical applications. We also highlight ongoing integration efforts using the Quantum Framework (QFw) to support future HPC-quantum computing systems.