Reference Architecture of a Quantum-Centric Supercomputer

2026-03-11

AI Summary

This work addresses the inefficiency in developing hybrid quantum-classical algorithms caused by the current isolation of quantum processing units (QPUs) from classical high-performance computing (HPC) systems. To bridge this gap, the paper proposes a Quantum-Centric Supercomputing (QCSC) reference architecture that deeply integrates QPUs, GPUs, and CPUs through co-design across hardware, middleware, and application layers, enabling end-to-end workflows for domains such as quantum chemistry and materials science. The study further establishes, for the first time, a systematic three-stage evolutionary roadmap for QCSC: beginning with QPUs as specialized accelerators, progressing to heterogeneous cooperative scheduling, and ultimately achieving full-stack co-design. This architecture provides a standardized technical pathway for quantum-HPC convergence, substantially enhancing algorithmic exploration efficiency and system scalability.

Technology Category

• Machine Learning: Quantum Machine Learning
• Search and Optimization: Mixed Discrete/Continuous Search
• Cognitive Modeling & Cognitive Systems: (Computational) Cognitive Architectures

Application Category

• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems
• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments
• 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

Quantum computers have demonstrated utility in simulating quantum systems beyond brute-force classical approaches. As the community builds on these demonstrations to explore using quantum computing for applied research, algorithms and workflows have emerged that require leveraging both quantum computers and classical high-performance computing (HPC) systems to scale applications, especially in chemistry and materials, beyond what either system can simulate alone. Today, these disparate systems operate in isolation, forcing users to manually orchestrate workloads, coordinate job scheduling, and transfer data between systems -- a cumbersome process that hinders productivity and severely limits rapid algorithmic exploration. These challenges motivate the need for flexible and high-performance Quantum-Centric Supercomputing (QCSC) systems that integrate Quantum Processing Units (QPUs), Graphics Processing Units (GPUs), and Central Processing Units (CPUs) to accelerate discovery of such algorithms across applications. These systems will be co-designed across quantum and classical HPC infrastructure, middleware, and application layers to accelerate the adoption of quantum computing for solving critical computational problems. We envision QCSC evolution through three distinct phases: (1) quantum systems as specialized compute offload engines within existing HPC complexes; (2) heterogeneous quantum and classical HPC systems coupled through advanced middleware, enabling seamless execution of hybrid quantum-classical algorithms; and (3) fully co-designed heterogeneous quantum-HPC systems for hybrid computational workflows. This article presents a reference architecture and roadmap for these QCSC systems.

Problem

• Quantum-Centric Supercomputing
• hybrid quantum-classical algorithms
• quantum computing integration
• high-performance computing
• workflow orchestration

Innovation

• Quantum-Centric Supercomputing
• hybrid quantum-classical algorithms
• co-designed architecture
• quantum HPC integration
• reference architecture