Hybrid Classical-Quantum Supercomputing: A demonstration of a multi-user, multi-QPU and multi-GPU environment

Fuente: arXiv
Saved in:
Bibliographic Details
Main Authors: Slysz, Mateusz, Rydlichowski, Piotr, Kurowski, Krzysztof, Bacarreza, Omar, Gomez, Esperanza Cuenca, Chandani, Zohim, Heim, Bettina, Khalate, Pradnya, Clements, William R., Fletcher, James
Format: Preprint
Published: 2025
Subjects:
Online Access:
Tags: Add Tag
No Tags, Be the first to tag this record!
_version_ 1866914008317034496
author Slysz, Mateusz
Rydlichowski, Piotr
Kurowski, Krzysztof
Bacarreza, Omar
Gomez, Esperanza Cuenca
Chandani, Zohim
Heim, Bettina
Khalate, Pradnya
Clements, William R.
Fletcher, James
author_facet Slysz, Mateusz
Rydlichowski, Piotr
Kurowski, Krzysztof
Bacarreza, Omar
Gomez, Esperanza Cuenca
Chandani, Zohim
Heim, Bettina
Khalate, Pradnya
Clements, William R.
Fletcher, James
contents Achieving a practical quantum advantage for near-term applications is widely expected to rely on hybrid classical-quantum algorithms. To deliver this practical advantage to users, high performance computing (HPC) centers need to provide a suitable software and hardware stack that supports algorithms of this type. In this paper, we describe the world's first implementation of a classical-quantum environment in an HPC center that allows multiple users to execute hybrid algorithms on multiple quantum processing units (QPUs) and GPUs. Our setup at the Poznan Supercomputing and Networking Center (PCSS) aligns with current HPC norms: the computing hardware including QPUs is installed in an active data center room with standard facilities; there are no special considerations for networking, power, and cooling; we use Slurm for workload management as well as the NVIDIA CUDA-Q extension API for classical-quantum interactions. We demonstrate applications of this environment for hybrid classical-quantum machine learning and optimisation. The aim of this work is to provide the community with an experimental example for further research and development on how quantum computing can practically enhance and extend HPC capabilities.
format Preprint
id arxiv_https___arxiv_org_abs_2508_16297
institution arXiv
publishDate 2025
record_format arxiv
spellingShingle Hybrid Classical-Quantum Supercomputing: A demonstration of a multi-user, multi-QPU and multi-GPU environment
Slysz, Mateusz
Rydlichowski, Piotr
Kurowski, Krzysztof
Bacarreza, Omar
Gomez, Esperanza Cuenca
Chandani, Zohim
Heim, Bettina
Khalate, Pradnya
Clements, William R.
Fletcher, James
Quantum Physics
Distributed, Parallel, and Cluster Computing
Emerging Technologies
Achieving a practical quantum advantage for near-term applications is widely expected to rely on hybrid classical-quantum algorithms. To deliver this practical advantage to users, high performance computing (HPC) centers need to provide a suitable software and hardware stack that supports algorithms of this type. In this paper, we describe the world's first implementation of a classical-quantum environment in an HPC center that allows multiple users to execute hybrid algorithms on multiple quantum processing units (QPUs) and GPUs. Our setup at the Poznan Supercomputing and Networking Center (PCSS) aligns with current HPC norms: the computing hardware including QPUs is installed in an active data center room with standard facilities; there are no special considerations for networking, power, and cooling; we use Slurm for workload management as well as the NVIDIA CUDA-Q extension API for classical-quantum interactions. We demonstrate applications of this environment for hybrid classical-quantum machine learning and optimisation. The aim of this work is to provide the community with an experimental example for further research and development on how quantum computing can practically enhance and extend HPC capabilities.
title Hybrid Classical-Quantum Supercomputing: A demonstration of a multi-user, multi-QPU and multi-GPU environment
topic Quantum Physics
Distributed, Parallel, and Cluster Computing
Emerging Technologies
url https://arxiv.org/abs/2508.16297