Analyzing Machine Learning Performance in a Hybrid Quantum Computing and HPC Environment
<|reference_start|>Analyzing Machine Learning Performance in a Hybrid Quantum Computing and HPC Environment: 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.<|reference_end|>
@article{bieberich2024analyzing,
    title={Analyzing Machine Learning Performance in a Hybrid Quantum Computing and
  HPC Environment},
    author={Samuel T. Bieberich, Michael A. Sandoval},
    journal={arXiv preprint arXiv:2407.07294},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.07294},
    primaryClass={cs.ET cs.LG quant-ph}
}