Quantum computing is often treated as an experimental add-on: it is often loosely coupled to classical infrastructure through high-level interpreted languages, cloud-based orchestration and physical measurement hardware. However, future deployments in both high-performance computing (HPC) and embedded environments will demand tighter integration for lower latencies, stronger determinism, and architectural consistency. We propose a vertically integrated quantum systems architecture that treats quantum accelerators as peripheral system components. A central element is the Quantum Abstraction Layer (QAL), introduced at the kernel level of the operating system. This design enables real-time, low-latency, and high-throughput interaction between quantum and classical resources and opens the door for robust scheduling, resource management, and cross-stack optimisation. Kernel-space integration is not merely a technical curiosity—it is a necessity. For HPC, it enables scalable, heterogeneous workloads; for embedded systems, it provides a foundation for future quantum deployments far beyond scripting-based control interfaces. We present early-stage results including a virtual QPU model integrated into a QEMU-based prototype system. The architecture is validated through functional emulation and time-accurate FPGA simulations, enabling realistic evaluation of hybrid system performance and quantum advantage scenarios. Our work lays the groundwork for a system-level co-design methodology tailored for the next generation of quantum-classical computing.
Quantum computing is often treated as an experimental add-on: it is often loosely coupled to classical infrastructure through high-level interpreted languages, cloud-based orchestration and physical measurement hardware. However, future deployments in both high-performance computing (HPC) and embedded environments will demand tighter integration for lower latencies, stronger determinism, and architectural consistency. We propose a vertically integrated quantum systems architecture that treats quantum accelerators as peripheral system components. A central element is the Quantum Abstraction Layer (QAL), introduced at the kernel level of the operating system. This design enables real-time, low-latency, and high-throughput interaction between quantum and classical resources and opens the door for robust scheduling, resource management, and cross-stack optimisation. Kernel-space integration is not merely a technical curiosity—it is a necessity. For HPC, it enables scalable, heterogeneous workloads; for embedded systems, it provides a foundation for future quantum deployments far beyond scripting-based control interfaces. We present early-stage results including a virtual QPU model integrated into a QEMU-based prototype system. The architecture is validated through functional emulation and time-accurate FPGA simulations, enabling realistic evaluation of hybrid system performance and quantum advantage scenarios. Our work lays the groundwork for a system-level co-design methodology tailored for the next generation of quantum-classical computing.
Quantum computers are often treated as experimental add-ons that are loosely coupled to classical infrastructure through high-level interpreted languages and cloud-like orchestration. However, future deployments in both, high-performance computing (HPC) and embedded environments, will demand tighter integration for lower latencies, stronger determinism, and architectural consistency, as well as to implement error correction and other tasks that require tight quantum-classical interaction as generically as possible. We propose a vertically integrated quantum systems architecture that treats quantum accelerators and processing units as peripheral system components. A central element is the Quantum Abstraction Layer (QAL) at operating system kernel level. It aims at real-time, low-latency, and high-throughput interaction between quantum and classical resources, as well as robust low-level quantum operations scheduling and generic resource management. It can serve as blueprint for orchestration of lowlevel computational components “around” a QPU (and inside a quantum computer), and across different modalities. We present first results towards such an integrated architecture, including a virtual QPU model based on QEMU. The architecture is validated through functional emulation on three base architectures (x86_64, ARM64, and RISC-V), and timingaccurate FPGA-based simulations. This allows for a realistic evaluation of hybrid system performance and quantum advantage scenarios. Our work lays the ground for a system-level co-design methodology tailored for the next generation of quantum-classical computing.
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. Building on the emerging vision of Quantum Integrated High-Performance Computing (QHPC), this paper contributes a full-stack layered architecture that integrates QPUs as first-class accelerators within, not merely alongside, the classical HPC software and hardware stack, with tight, on-premise quantum-classical coupling as its defining characteristic. The architecture comprises of 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 this architecture 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.
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. Building on the emerging vision of Quantum Integrated High-Performance Computing (QHPC), this paper contributes a full-stack layered architecture that integrates QPUs as first-class accelerators within, not merely alongside, the classical HPC software and hardware stack, with tight, on-premise quantum–classical coupling as its defining characteristic. The architecture comprises of 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 this architecture 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.
We define some of the programming and system-level challenges facing the application of quantum processing to high-performance computing. Alongside barriers to physical integration, prominent differences in the execution of quantum and conventional programs challenges the intersection of these computational models. Following a brief overview of the state of the art, we discuss recent advances in programming and execution models for hybrid quantum-classical computing. We discuss a novel quantum-accelerator framework that uses specialized kernels to offload select workloads while integrating with existing computing infrastructure. We elaborate on the role of the host operating system to manage these unique accelerator resources, the prospects for deploying quantum modules, and the requirements placed on the language hierarchy connecting these different system components. We draw on recent advances in the modeling and simulation of quantum computing systems with the development of architectures for hybrid high-performance computing systems and the realization of software stacks for controlling quantum devices. Finally, we present simulation results that describe the expected system-level behavior of high-performance computing systems composed from compute nodes with quantum processing units. We describe performance for these hybrid systems in terms of time-to-solution, accuracy, and energy consumption, and we use simple application examples to estimate the performance advantage of quantum acceleration.
We define some of the programming and system-level challenges facing the application of quantum processing to high-performance computing. Alongside barriers to physical integration, prominent differences in the execution of quantum and conventional programs challenges the intersection of these computational models. Following a brief overview of the state of the art, we discuss recent advances in programming and execution models for hybrid quantum-classical computing. We discuss a novel quantum-accelerator framework that uses specialized kernels to offload select workloads while integrating with existing computing infrastructure. We elaborate on the role of the host operating system to manage these unique accelerator resources, the prospects for deploying quantum modules, and the requirements placed on the language hierarchy connecting these different system components. We draw on recent advances in the modeling and simulation of quantum computing systems with the development of architectures for hybrid high-performance computing systems and the realization of software stacks for controlling quantum devices. Finally, we present simulation results that describe the expected system-level behavior of high-performance computing systems composed from compute nodes with quantum processing units. We describe performance for these hybrid systems in terms of time-to-solution, accuracy, and energy consumption, and we use simple application examples to estimate the performance advantage of quantum acceleration.
The connection and eventual integration of High-Performance Computing (HPC) with Quantum Computing (QC) represents a transformative advancement in computational technology, promising significant enhancements in solving complex, previously intractable problems. This manuscript provides a comprehensive overview of the current state of HPC-QC interfacing, detailing architectural methodologies, software stack developments, middleware functionalities, and hardware integration strategies. It critically assesses existing hardware-level integration models, ranging from standalone and loosely-coupled architectures to tightly-integrated and on-node systems. The software ecosystem is analyzed, highlighting prominent frameworks such as Qiskit, PennyLane, CUDA-Q, and middleware solutions like Pilot-Quantum, essential for seamless hybrid computing environments. Furthermore, the manuscript discusses practical applications in optimization, machine learning, and many-body dynamics, where hybrid HPC-QC systems can offer substantial advantages. It also describes existing challenges, including hardware limitations (coherence, scalability, connectivity), software maturity, communication overhead, resource management complexities, and cost factors. Finally, future directions towards tighter hardware and software integration are discussed, emphasizing ongoing research developments and emerging trends that promise to expand the capabilities and accessibility of hybrid HPC-QC systems.