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

TL;DR

This paper presents the first implementation of a multi-user, multi-QPU and multi-GPU hybrid classical-quantum environment within an HPC center, demonstrating practical integration for advanced applications.

Contribution

It introduces a novel, scalable hybrid classical-quantum computing environment in an HPC setting, enabling multi-user access to quantum and classical resources.

Findings

Successful deployment of a hybrid environment in a standard HPC data center

Demonstrated hybrid quantum-classical machine learning applications

Provided a practical framework for future research in quantum-enhanced HPC

Abstract

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.