Quantum-centric supercomputing (QCSC) workflows often involve hybrid classical-quantum algorithms that are inherently probabilistic and executed on remote quantum hardware, making them difficult to interpret and limiting the ability to monitor runtime performance and behavior. The high cost of quantum circuit execution and large-scale high-performance computing (HPC) infrastructure further restricts the number of feasible trials, making comprehensive evaluation of execution results essential for iterative development. We propose an observability architecture tailored for QCSC workflows that decouples telemetry collection from workload execution, enabling persistent monitoring across system and algorithmic layers and retaining detailed execution data for reproducible and retrospective analysis, eliminating redundant runs. Applied to a representative workflow involving sample-based quantum diagonalization, our system reveals solver behavior across multiple iterations. This approach enhances transparency and reproducibility in QCSC environments, supporting infrastructure-aware algorithm design and systematic experimentation.
Quantum-centric supercomputing (QCSC) workflows often involve hybrid classical-quantum algorithms that are inherently probabilistic and executed on remote quantum hardware, making them difficult to interpret and limiting the ability to monitor runtime performance and behavior. The high cost of quantum circuit execution and large-scale high-performance computing (HPC) infrastructure further restricts the number of feasible trials, making comprehensive evaluation of execution results essential for iterative development. We propose an observability architecture tailored for QCSC workflows that decouples telemetry collection from workload execution, enabling persistent monitoring across system and algorithmic layers and retaining detailed execution data for reproducible and retrospective analysis, eliminating redundant runs. Applied to a representative workflow involving sample-based quantum diagonalization, our system reveals solver behavior across multiple iterations. This approach enhances transparency and reproducibility in QCSC environments, supporting infrastructure-aware algorithm design and systematic experimentation.
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.
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.
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.
The implementation of scalable processing workflows is essential to improve the access to and analysis of the vast amount of high-resolution and multi-source Remote Sensing (RS) data and to provide decision-makers with timely and valuable information. The Modular Supercomputing Architecture (MSA) systems that are operated by the Jülich Supercomputing Centre (JSC) are a concrete solution for data-intensive RS applications that rely on big data storage and processing capabilities. To meet the requirements of applications with more complex computational tasks, JSC plans to connect the High Performance Computing (HPC) systems of its MSA environment to different quantum computers via the Jülich UNified Infrastructure for Quantum computing (JUNIQ). The paper describes this unique computing environment and highlights its potential to address real RS application scenarios through high-performance and hybrid quantum-classical processing workflows.
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 entering an era where quantum processing units (QPUs) with tens to hundreds of qubits are increasingly integrated with high-performance computing (HPC) systems to support hybrid quantum-classical workloads. This paradigm, often referred to as Quantum-Centric SuperComputing (QCSC), introduces new operational, software, and infrastructure challenges. In this paper, we identify and characterize six distinct personas involved in QCSC environments. Particular attention is given to emerging roles that arise at the interface between quantum and HPC systems. We analyze the responsibilities and tools associated with each persona and examine their differing requirements for operational data. Consistent data representations and analytics frameworks will be required to support diverse users across the quantum-HPC ecosystem. By defining personas and their data needs, we provide a foundation for future discussions in this critical area.