Quantum Integrated High-Performance Computing: Foundations, Architectural Elements and Future Directions

2026-04-17

AI Summary

This work addresses the critical challenge of efficiently integrating quantum processing units (QPUs) into existing high-performance computing (HPC) systems to enable unified scheduling and协同 execution of classical and quantum resources. The paper proposes a Quantum-integrated High-Performance Computing (QHPC) architecture—the first HPC framework explicitly designed for quantum-classical convergence—featuring a layered design that holistically manages CPUs, GPUs, FPGAs, and QPUs. Key innovations include a user request abstraction layer offering a Slurm-like unified interface, a quantum-aware scheduling algorithm, a hybrid workflow engine, and a hierarchical execution model supported by high-speed interconnects. This architecture provides scalable heterogeneous computing support for emerging applications such as quantum chemistry, materials discovery, combinatorial optimization, and climate modeling, thereby laying the foundation for a future quantum-classical HPC ecosystem.

Technology Category

• Machine Learning: Quantum Machine Learning
• Cognitive Modeling & Cognitive Systems: (Computational) Cognitive Architectures
• Humans and AI: Other Foundations of Human Computation & AI

Application Category

• Economics, Online Markets and Human Computation: Architectures and workflows that use LLMs for crowd work
• Systems and Infrastructure for Web, Mobile and WoT: Federated Web and WoT systems, including distributed, federated and edge-based data processing
• Graph Algorithms and Modeling for the Web: Algorithms and analysis for heterogeneous, signed, attributed, multi-relational, temporal, higher-order, and annotated Web-related graphs

Abstract

High-performance computing (HPC) has evolved over decades through multiple architectural transitions, from vector supercomputers to massively parallel CPU clusters and GPU-accelerated systems, continuously expanding the frontier of scientific discovery. With the emergence of quantum processing units (QPUs) as practical computational accelerators, a new opportunity arises to further extend this trajectory by integrating quantum and classical computing paradigms. This paper presents Quantum Integrated High-Performance Computing (QHPC), a visionary architectural framework that unifies CPUs, GPUs, FPGAs, and QPUs as first-class heterogeneous resources. We propose a layered system design comprising unified resource management, quantum-aware scheduling, hybrid workflow orchestration, middleware and programming abstraction, interconnect technologies, and a tiered execution model enabling seamless workload partitioning across classical and quantum backends. A central aspect of our vision is a strong user requests abstraction layer that exposes heterogeneous resources through a unified job submission interface, similar in spirit to existing schedulers such as Slurm, allowing users to describe workloads in a consistent template independent of underlying compute type or location. Drawing insights from prior accelerator integration eras, we outline how QHPC can support emerging workloads in quantum chemistry, materials discovery, combinatorial optimization, and climate modeling. We conclude by highlighting open challenges in building scalable, reliable, and programmable quantum-classical infrastructures that seamlessly connect global users to heterogeneous compute resources for future quantum-classical HPC ecosystems.

Problem

• Quantum Integrated High-Performance Computing
• quantum-classical integration
• heterogeneous computing
• quantum accelerators
• HPC infrastructure

Innovation

• Quantum Integrated HPC
• heterogeneous computing
• quantum-classical integration
• unified resource management
• quantum-aware scheduling