International Conference for High Performance Computing, Networking, Storage and Analysis (SC)
A Software Platform to Support Disaggregated Quantum Accelerators
Ercüment Kaya, Jorge Echavarria, Muhammad Nufail Farooqi, Aleksandra Świerkowska, Patrick Hopf, Burak Mete, Lukas Burgholzer, Robert Wille, Laura Brandon Schulz, Martin Schulz
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.
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.
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.
As quantum computing (QC) technologies mature, their integration into established high-performance computing (HPC) infrastructures is becoming a central objective for next-generation computing systems. However, unlocking the potential of hybrid platforms for computationally demanding workloads remains challenging. The mismatch between quantum and classical programming models, the limited maturity of quantum software stacks, and the scarcity of quantum processing units (QPUs) above all, necessitate scheduling strategies that go beyond standard HPC mechanisms to manage such heterogeneous and constrained resources.
To address this issue, we investigate three distinct methodologies for HPC-QC resource scheduling: time-based multiplexing, dynamic resource management, and workflow decomposition. Experimental validation on production HPC clusters and real quantum hardware demonstrates the effectiveness of these approaches under different workload scenarios. Malleability and workflow strategies significantly optimize classical resource utilization, reducing consumption by up to 45.7% and 64% respectively, proving to be best fitted for hybrid jobs where quantum and classical workloads are evenly balanced. Conversely, time-multiplexing enhances QPU utilization and reduces execution time at the cluster level, making it the optimal strategy for the opposite context, which is characterized by high classical-quantum workload imbalances. These findings underscore the practical viability of tailored scheduling strategies for hybrid HPC-QC environments and highlight their complementarity in building efficient, scalable software stacks for next-generation quantum-accelerated facilities.
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.
Quantum computing is advancing rapidly, with notable progress in both hardware and algorithm development. However, to make quantum computing truly accessible and usable across disciplines, a comprehensive, efficient and unified software stack is essential. Such a software stack must be flexible enough to support diverse hardware platforms and evolving algorithms, while enabling programming models for a wide range of users from quantum specialists to users without deep quantum expertise. Additionally, a software stack must support dynamic resource management for efficient system utilization as well as seamless integration with classical High-Performance Computing (HPC) environments. The latter is key, as quantum systems are increasingly envisioned as accelerators within hybrid workflows, supporting various levels of integration from loosely to tightly coupled. Despite numerous discussions, conceptual proposals, and initial efforts in recent years, few tangible implementations of such full-featured software stacks currently exist.
To harness the power of quantum computing (QC) in the near future, tight and efficient integration of QC with high performance computing (HPC) infrastructure (both on the software (SW) and the hardware (HW) level) is crucial. This paper addresses the development of a unified quantum platform (UQP) and how it is being integrated into the HPC ecosystem. It builds on the concepts of hybrid high performance computing - quantum computing (HPCQC) workflows and a unified HPCQC toolchain, introduced in our previous work and makes the next needed step: it unifies the low-level interface between the existing classical HPC systems and the emerging quantum hardware technologies, including but not limited to machines based on superconducting qubits, neutral atoms or trapped ions. The UQP consists of three core components: a runtime library, an instruction set architecture (ISA) and a quantum control processor (QCP) micro-architecture. In particular, this work contributes a unified HPCQC runtime library that bridges the gap between programming systems built on quantum intermediate representation (QIR) standard with a novel, unified hybrid ISA. It then introduces the initial extension of an ISA and QCP micro-architecture to be platform and technology agnostic and enables it as an efficient execution platform. The UQP has been verified to ensure correctness. Further, our performance analysis shows that the execution time and memory requirements of the runtime library scale super-linearly with number of qubits, which is critical to support scalability efforts in OC hardware.