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

2025-08-22

AI Summary

This work addresses the critical challenge of efficiently integrating quantum computing resources into HPC centers to support near-term quantum advantage applications. Methodologically, it introduces and implements the first multi-user, multi-QPU–multi-GPU hybrid classical–quantum computing environment fully compatible with standard supercomputing infrastructure—requiring no modifications to network, power, or cooling systems. Leveraging the existing Slurm workload manager and NVIDIA CUDA-Q API, distributed QPUs are deployed within active data centers and seamlessly integrated into the standard HPC software and hardware stack. Key contributions include: (1) the first demonstration of concurrent, multi-user scheduling of heterogeneous quantum–classical resources in a production-scale supercomputing environment; (2) end-to-end validation of the hybrid architecture on quantum machine learning and combinatorial optimization workloads; and (3) a reusable, scalable technical paradigm for HPC–quantum integration, establishing an engineering foundation for practical quantum computing deployment.

Technology Category

• Machine Learning: Quantum Machine Learning
• Search and Optimization: Mixed Discrete/Continuous Search
• Planning, Routing, and Scheduling: Mixed Discrete/Continuous Planning

Application Category

• Economics, Online Markets and Human Computation: Research challenges in human and human-AI computation
• Systems and Infrastructure for Web, Mobile and WoT: Virtualization and resource management in Web systems and infrastructures
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web

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.

Problem

• Demonstrating a multi-user hybrid classical-quantum supercomputing environment
• Enabling practical quantum advantage through hybrid algorithms on HPC systems
• Integrating quantum processing units with standard HPC infrastructure and management

Innovation

• Multi-user hybrid classical-quantum supercomputing environment
• Integration with standard HPC infrastructure using Slurm
• NVIDIA CUDA-Q API for quantum-classical interactions