Analyzing Machine Learning Performance in a Hybrid Quantum Computing and HPC Environment

TL;DR

This paper demonstrates the potential speedups of hybrid quantum machine learning workflows in HPC environments, showing significant performance improvements using quantum simulators on supercomputers compared to traditional systems.

Contribution

It provides the first performance evaluation of hybrid quantum-classical workflows on HPC systems, highlighting substantial speedups and motivating further scaling studies.

Findings

GPU acceleration yields up to 92% speedup over CPUs.

Hybrid workflows achieve up to 226% faster runtimes than non-HPC systems.

Quantum simulators benefit from HPC resources, enabling more efficient quantum machine learning.

Abstract

We explored the possible benefits of integrating quantum simulators in a "hybrid" quantum machine learning (QML) workflow that uses both classical and quantum computations in a high-performance computing (HPC) environment. Here, we used two Oak Ridge Leadership Computing Facility HPC systems, Andes (a commodity-type Linux cluster) and Frontier (an HPE Cray EX supercomputer), along with quantum computing simulators from PennyLane and IBMQ to evaluate a hybrid QML program -- using a "ground up" approach. Using 1 GPU on Frontier, we found ~56% and ~77% speedups when compared to using Frontier's CPU and a local, non-HPC system, respectively. Analyzing performance on a larger dataset using multiple threads, the Frontier GPUs performed ~92% and ~48% faster than the Andes and Frontier CPUs, respectively. More impressively, this is a ~226% speedup over a local, non-HPC system's runtime using the same simulator and number of threads. We hope that this proof of concept will motivate more intensive hybrid QC/HPC scaling studies in the future.