High-Performance Computing (HPC) is today the main tool for large-scale scientific computing, but performance gains are increasingly limited by architectural and energy constraints. At the same time, quantum computing is an emerging paradigm, still under development, but already promising for accelerating certain computational kernels. A central challenge is therefore to understand how to efficiently integrate quantum processors within existing HPC infrastructures, rather than treating them as isolated accelerators. The objective of this thesis is to explore hybrid quantum-classical computing, both from the algorithmic perspective and in terms of programming models. We investigate how to coherently combine classical and quantum resources within hybrid workflows, leveraging the respective strengths of each paradigm. As a primary use case, we focus on solving linear systems of equations, a fundamental building block of scientific computing and HPC. First, we address the challenge of encoding classical data into quantum representations, which remains a major bottleneck for hybrid algorithms. We propose an improved preprocessing method for matrix encoding into unitaries using a Pauli decomposition, reducing the associated computational cost. Next, we introduce a hybrid solver based on the Quantum Singular Value Transformation (QSVT). Inspired by classical numerical linear algebra, we propose a mixed-precision iterative refinement approach, combining low-precision quantum solves with high-precision classical corrections. A detailed analysis of convergence, errors, and computational cost shows that hybridization can reduce the quantum resources required to reach a given precision.Finally, we address programming and implementation aspects through the design of a hybrid quantum-classical programming framework, Q-Pragma, aimed at facilitating the integration of quantum acceleration into existing HPC environments. Overall, this work contributes to the development of more practical and usable hybrid quantum-HPC codes, bridging the gap between theoretical algorithms and real-world high-performance applications.
High-Performance Computing (HPC) is today the main tool for large-scale scientific computing, but performance gains are increasingly limited by architectural and energy constraints. At the same time, quantum computing is an emerging paradigm, still under development, but already promising for accelerating certain computational kernels. A central challenge is therefore to understand how to efficiently integrate quantum processors within existing HPC infrastructures, rather than treating them as isolated accelerators. The objective of this thesis is to explore hybrid quantum-classical computing, both from the algorithmic perspective and in terms of programming models. We investigate how to coherently combine classical and quantum resources within hybrid workflows, leveraging the respective strengths of each paradigm. As a primary use case, we focus on solving linear systems of equations, a fundamental building block of scientific computing and HPC. First, we address the challenge of encoding classical data into quantum representations, which remains a major bottleneck for hybrid algorithms. We propose an improved preprocessing method for matrix encoding into unitaries using a Pauli decomposition, reducing the associated computational cost. Next, we introduce a hybrid solver based on the Quantum Singular Value Transformation (QSVT). Inspired by classical numerical linear algebra, we propose a mixed-precision iterative refinement approach, combining low-precision quantum solves with high-precision classical corrections. A detailed analysis of convergence, errors, and computational cost shows that hybridization can reduce the quantum resources required to reach a given precision.Finally, we address programming and implementation aspects through the design of a hybrid quantum-classical programming framework, Q-Pragma, aimed at facilitating the integration of quantum acceleration into existing HPC environments. Overall, this work contributes to the development of more practical and usable hybrid quantum-HPC codes, bridging the gap between theoretical algorithms and real-world high-performance applications.
Algorithmes hybrides quantiques-classiques pour le calcul haute performance Le calcul haute performance (HPC) constitue aujourd'hui l'outil principal du calcul scientifique à grande échelle, mais les gains de performance sont de plus en plus contraints par des limites architecturales et énergétiques. En parallèle, le calcul quantique est un paradigme émergent, encore en cours de développement, mais déjà prometteur pour l'accélération de certaines briques numériques. Un enjeu central consiste donc à comprendre comment intégrer efficacement des processeurs quantiques au sein des infrastructures HPC existantes, plutôt que de les considérer comme des accélérateurs isolés. L'objectif de cette thèse est d'explorer le calcul hybride quantique-classique, à la fois du point de vue algorithmique et des modèles de programmation. Nous étudions comment combiner de manière cohérente des ressources classiques et quantiques au sein de workflows hybrides, en exploitant les forces respectives de chaque paradigme. Comme cas d'usage principal, nous nous intéressons à la résolution de systèmes linéaires, un noyau fondamental du calcul scientifique et du HPC. Dans un premier temps, nous abordons la problématique de l'encodage de données classiques sous forme de représentations quantiques, qui constitue un goulot d'étranglement majeur des algorithmes hybrides. Nous proposons une amélioration du coût de pré-traitement pour l'encodage de matrice dans des unitaires, via décomposition dans la base de Pauli. Nous introduisons ensuite un solveur hybride reposant sur la Quantum Singular Value Transformation (QSVT). Inspirée de l'algèbre linéaire numérique classique, une approche de raffinement itératif en précision mixte est proposée, combinant des résolutions quantiques basse précision avec des corrections classiques haute précision. Une analyse détaillée de la convergence, des erreurs et des coûts de calcul est présentée, montrant que l'hybridation permet de réduire les ressources quantiques nécessaires pour atteindre une précision donnée. Enfin, nous abordons les aspects de programmation et d'implémentation à travers la conception d'un framework de programmation hybride quantique-classique, Q-Pragma, destiné à faciliter l'intégration de l'accélération quantique dans des environnements HPC existants. Ce travail contribue ainsi au développement de codes hybrides quantique-HPC plus réalistes et exploitables.
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 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.
Quantum computing (QC) introduces a novel mode of computation with the possibility of greater computational power that remains to be exploitedpresenting exciting opportunities for high-performance computing (HPC) applications. However, recent advancements in the field have made clear that QC does not supplant conventional HPC, but can rather be incorporated into current heterogeneous HPC infrastructures as an additional accelerator, thereby enabling the optimal utilization of both paradigms. The desire for such integration significantly affects the development of software for quantum computers, which in turn influences the necessary software infrastructure. To date, previous review articles have investigated various quantum programming tools (QPTs) (such as languages, libraries, frameworks) in their ability to program, compile, and execute quantum circuits. However, the integration effort with classical HPC frameworks or systems has not been addressed. This study aims to characterize existing QPTs from an HPC perspective, investigating if existing QPTs have the potential to be efficiently integrated with classical computing models and determining where work is still required. This work structures a set of criteria into an analysis blueprint that enables HPC scientists to assess whether a QPT is suitable for a high-performance computing quantum computing (HPCQC) environment.
Hybrid computing models, which integrate both classical and quantum systems, represent a promising frontier in enhancing computational power and efficiency. Classical computing systems, while highly efficient for a broad range of tasks, encounter significant limitations when addressing complex, large-scale problems such as optimization, cryptography, and molecular simulations. Quantum computing, on the other hand, offers exponential speedup for specific algorithms, particularly in areas like factorization, search, and simulation of quantum systems. However, quantum systems are not yet mature enough to replace classical systems entirely, due to issues like error rates, qubit coherence, and the need for cryogenic environments. As a result, hybrid models are emerging as the most viable solution, combining the strengths of both paradigms to tackle computationally intensive tasks more efficiently. This paper provides a comprehensive analysis of the development and application of hybrid computing models. It explores how these models leverage the parallel processing power of quantum systems while utilizing the robustness and data handling capabilities of classical systems. We discuss various architectures proposed for hybrid computing, focusing on their scalability, efficiency, and real-world applications across industries such as healthcare, finance, cryptography, and artificial intelligence. Additionally, we examine the challenges involved in integrating classical and quantum systems, including synchronization, resource management, and algorithm development. The paper concludes with future directions for hybrid computing, emphasizing the potential for these models to revolutionize fields that rely on high-performance computing by offering unprecedented computational capabilities. In summary, hybrid computing models stand at the intersection of classical and quantum technologies, providing a synergistic approach to solving some of the most challenging computational problems faced today.