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

TL;DR

This paper introduces Quantum Integrated High-Performance Computing (QHPC), a framework unifying classical and quantum resources for advanced scientific workloads, with a layered system design and user-friendly resource management.

Contribution

It proposes a comprehensive architectural model for integrating quantum processing units into existing HPC systems, enabling seamless workload partitioning and resource management.

Findings

Layered system design for quantum-classical integration

Unified resource management and scheduling mechanisms

Application scenarios in quantum chemistry and climate modeling

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.