As we make strides toward the era of quantum utility with improved hardware and defined practical use cases, a primary challenge resides in the definition of robust implementation architectures that enable efficient utilization of quantum accelerators. To truly unlock the capabilities of Quantum Processing Units (QPUs), basic classical support must be upgraded to a high-performance, efficient infrastructure. On the application level, integration translates to defining coherent workflows that enable optimized implementations, while abstracting complexity for larger tasks. Accordingly, a common primitive in hybrid applications is the evaluation of a physical system's observable quantities. Leveraging quantum hardware for this task requires decomposing observable-derived operators into vast arrays of Pauli terms, necessitating a high volume of distinct circuit executions that currently suffer from suboptimal management. This work proposes a novel, scalable procedural architecture that establishes observable measurement as a first-class citizen of the High-Performance Computing - Quantum Computing (HPCQC) stack. We introduce a hierarchical runtime environment featuring a workload manager for resource-aware scheduling and local quantum and classical workers for low-latency, device-specific interactions. Such infrastructure enables a pipeline of advanced optimizations, including, but not restricted to, commutativity-based operator grouping, adaptive shot allocation for variance-aware load balancing, and stateful circuit caching to minimize compilation overhead. By automating the orchestration of measuring non-commuting terms across heterogeneous backends, we demonstrate a pathway to significantly reduce time-to-solution and improve accuracy. This procedure bridges the gap between theoretical quantum algorithms and practical, scalable HPC deployment.
As we make strides toward the era of quantum utility with improved hardware and defined practical use cases, a primary challenge resides in the definition of robust implementation architectures that enable efficient utilization of quantum accelerators. To truly unlock the capabilities of Quantum Processing Units (QPUs), basic classical support must be upgraded to a high-performance, efficient infrastructure. On the application level, integration translates to defining coherent workflows that enable optimized implementations, while abstracting complexity for larger tasks. Accordingly, a common primitive in hybrid applications is the evaluation of a physical system's observable quantities. Leveraging quantum hardware for this task requires decomposing observable-derived operators into vast arrays of Pauli terms, necessitating a high volume of distinct circuit executions that currently suffer from suboptimal management. This work proposes a novel, scalable procedural architecture that establishes observable measurement as a first-class citizen of the High-Performance Computing - Quantum Computing (HPCQC) stack. We introduce a hierarchical runtime environment featuring a workload manager for resource-aware scheduling and local quantum and classical workers for low-latency, device-specific interactions. Such infrastructure enables a pipeline of advanced optimizations, including, but not restricted to, commutativity-based operator grouping, adaptive shot allocation for variance-aware load balancing, and stateful circuit caching to minimize compilation overhead. By automating the orchestration of measuring non-commuting terms across heterogeneous backends, we demonstrate a pathway to significantly reduce time-to-solution and improve accuracy. This procedure bridges the gap between theoretical quantum algorithms and practical, scalable HPC deployment.
Quantum computers are making their way into High Performance Computing (HPC) centers as next-generation accelerators. Due to their physical implementation as mostly large appliances in separate racks, their number in typical data centers is significantly lower than the number of nodes offloading work to them, unlike the case with GPU accelerators. As a consequence, they form large-scale disaggregated infrastructures that pose a number of integration challenges due to their diverse implementation technologies and their need to be used as a shared resource for optimal utilization. Running hybrid High Performance Computing-Quantum Computing (HPCQC) applications in HPC environments, where the quantum portion is offloaded to the quantum processing units (QPUs), requires sophisticated resource management strategies to optimize resource utilization and performance. In this paper, we present one aspect of the Munich Quantum Software Stack (MQSS) - a Just-In-Time (JIT) compilation and execution software stack for quantum and hybrid quantum-HPC workloads - beneficial for integrating disaggregated quantum accelerators into traditional HPC workflows. MQSS is centered around a series of novel contributions aimed at optimizing the compilation process while managing the disaggregated resources ensuring efficient utilization of all available quantum resources. The key stages of our JIT compilation stack involve hardware-agnostic optimizations, quantum circuit cutting – when necessary – and a novel hardware platform selection process. Included here is description of the use of the MQSS’s Quantum Device Management Interface (QDMI), a unified interface between the software stack and the quantum accelerators, which allows connection to several, potentially different, quantum resources. Our methodology demonstrates notable advancements in HPCQC integration workflows, offering researchers a powerful framework for leveraging quantum computational capabilities. Overall, our work represents a significant step towards the practical integration of disaggregated quantum devices within the realm of supercomputing, unlocking new avenues for computational exploration and discovery.
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.
Hybrid quantum-classical applications pose significant resource management challenges due to heterogeneity and dynamism in both infrastructure and workloads. Quantum-HPC environments integrate quantum processing units (QPUs) with diverse classical resources (CPUs, GPUs), while applications span coupling patterns from tightly coupled execution to loosely coupled task parallelism with varying resource requirements. Traditional HPC schedulers lack visibility into application semantics and cannot respond to fluctuating resource availability at runtime. This paper presents a middleware-based approach for adaptive resource, workload, and task management in hybrid quantum-HPC systems. We make four contributions: (i) a conceptual four-layer middleware architecture that decomposes management across workflow, workload, task, and resource levels, enabling application-aware scheduling over heterogeneous quantum-HPC resources; (ii) a set of execution motifs capturing interaction and coupling characteristics of hybrid applications, realized as quantum mini-apps for systematic workload characterization; (iii) Pilot-Quantum, a middleware framework built on the pilot abstraction that enables late binding and dynamic resource allocation, adapting to resource and workload dynamics at runtime; and (iv) Q-Dreamer, a performance modeling toolkit providing reusable components for informed workload partitioning, including a circuit-cutting optimizer that analytically derives optimal partitioning strategies. Evaluation on heterogeneous HPC platforms (Perlmutter, NVIDIA DGX with H100/B200 GPUs) demonstrates efficient multi-backend orchestration across CPUs, GPUs, and QPUs for diverse execution motifs. Q-Dreamer predicts optimal circuit cutting configurations with up to 82% accuracy.
In this work, we demonstrate hybrid High Performance Computing-Quantum Computing (HPCQC) workflows on a production petascale system. The demonstration combines three components: the SuperMUC-NG supercomputer at the Leibniz Supercomputing Centre (LRZ), a 20-qubit superconducting quantum processor provided by IQM Quantum Computers (IQM), and Munich Quantum Valley (MQV)'s Munich Quantum Software Stack (MQSS).
Integrating quantum processors into High Performance Computing (HPC) systems requires a heterogeneous software stack capable of orchestrating classical and quantum resources within established supercomputing workflows. MQSS treats Quantum Processing Units (QPUs) as scheduler-managed accelerators and it performs resource coordination following a two-level scheduling scheme. Slurm performs system-level allocation by exposing QPUs as Generic RESources (GRES), while the MQSS Quantum Resource Manager & Compiler Infrastructure (QRM&CI) performs just-in-time compilation and subsequent dispatch of quantum circuits.
To integrate with existing HPC operations without modifying the scheduler core, MQSS introduces an open-source SLURM Plugin Suite based on Prolog/Epilog scripts and SPANK modules. Experimental results show that hybrid HPCQC workflows can be executed without significant latency overhead compared to conventional workloads.
The presented architecture provides a portable integration model for quantum accelerators on large-scale HPC systems and is directly applicable to next-generation Hewlett Packard Enterprise (HPE) Cray platforms, including LRZ's upcoming 'Blue Lion' supercomputer.
Robust execution environments are important for addressing key challenges in quantum computing, such as application development, portability, and reproducibility, and help unlock the development of modular quantum programs, driving forward hybrid quantum workflows. In this work, we show progress towards a basic, but portable, runtime environment for developing and executing hybrid quantum-classical programs running in High Performance Computing (HPC) environments enhanced with Quantum Processing Units (QPUs). The middleware includes a second layer of scheduling after the main HPC resource manager in order to improve the utilization of the QPU, and extra functionality for observability, monitoring, and admin access. This approach enables managing multiple programming Software Development Kits (SDKs) as first-class citizens in the environment by building on a recently proposed vendor-neutral Quantum Resource Management Interface (QRMI). Lastly, we discuss and show a solution for the monitoring and observability stack, completing our description of the hybrid system architecture.