One of the roadmap plans for quantum computers is an integration within HPC ecosystems assigning them a role of accelerators for a variety of computationally hard tasks. However, in the near term, quantum hardware will be in a constant state of change. Heading towards solving real-world problems, we advocate development of portable, architecture-agnostic hybrid quantum-classical frameworks and demonstrate one for the community detection problem evaluated using quantum annealing and gate-based universal quantum computation paradigms.
One of the roadmap plans for quantum computers is an integration within HPC ecosystems assigning them a role of accelerators for a variety of computationally hard tasks. However, in the near term, quantum hardware will be in a constant state of change. Heading towards solving real-world problems, we advocate development of portable, architecture-agnostic hybrid quantum-classical frameworks and demonstrate one for the community detection problem evaluated using quantum annealing and gate-based universal quantum computation paradigms.
Abstract Emerging quantum technologies represent a promising alternative for solving hard combinatorial problems in the post-Moore’s law era. For practical purposes, however, the current number of qubits limits the direct applicability to larger real-world instances in the near-term future. Therefore, a promising strategy to overcome this issue is represented by hybrid quantum classical algorithms which leverage classical as well as quantum devices. One prominent example of a hard computational problem is the community detection problem: a partition of a graph into distinct communities such that the ratio between intra-community and inter-community connectivity is maximized. In this paper, we explore the current potential of quantum annealing and gate-based quantum technologies to solve the community detection problem for an arbitrary number of communities. For this purpose, existing algorithms are (re-)implemented and new hybrid algorithms, that can be run on gate-model devices, are proposed. Their performance on standardized benchmark graphs has been evaluated and compared to the one of a state-of-the-art classical heuristic algorithm. Although no quantum speed-up has been achieved, the existing quantum annealing-based methods as well as the novel hybrid algorithms for gate-based quantum computers yield modularity values, which are similar to those of the classical heuristic. However, the modular architecture of the used algorithms allows for fast utilization of more powerful quantum technologies once they become available.Reproducibility:Our code and data are publicly available (Github in Quantum Modularization. https://github.com/jku-win se/quantum_modularization 2021).
Quantum computing combined with cloud infrastructure is a paradigm-shifting area in computational frameworks. In this paper, we explore the multidimensional aspects of combining quantum processing power into standard cloud computing infrastructures. We discuss upcoming quantum-cloud architecture designs, resource planning approaches, and security challenges of this emerging hybrid paradigm. Our analysis demonstrates significant promise for quantum acceleration within virtualized domains of computation while pinpointing large technical challenges that must be addressed before adoption becomes widespread. Theoretical evaluation of existing quantum cloud service models and experimental deployment scenarios, we outline a roadmap for successful quantum-classical integration in distributed computing systems. We also introduce a novel quantum resource abstraction framework for seamless integration with the classical virtualization infrastructure. The research finds that while enterprise-scale quantum cloud deployment is on the horizon, strategic use of quantum processing units for specialized applications offers near-term value for cloud service providers and their customers.
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.
After decades of being mainly confined to theoretical research, Quantum Computing is now becoming a useful tool for solving realistic problems. This work aims to experimentally explore the feasibility of using currently available quantum computers, based on the Quantum Annealing paradigm, to build a recommender system exploiting community detection. Community detection, by partitioning users and items into densely connected clusters, can boost the accuracy of non-personalized recommendation by assuming that users within each community share similar tastes. However, community detection is a computationally expensive process. The recent availability of Quantum Annealers as cloud-based devices, constitutes a new and promising direction to explore community detection, although effectively leveraging this new technology is a long-term path that still requires advancements in both hardware and algorithms. This work aims to begin this path by assessing the quality of community detection formulated as a Quadratic Unconstrained Binary Optimization problem on a real recommendation scenario. Results on several datasets show that the quantum solver is able to detect communities of comparable quality with respect to classical solvers, but with better speedup, and the non-personalized recommendation models built on top of these communities exhibit improved recommendation quality. The takeaway is that quantum computing, although in its early stages of maturity and applicability, shows promise in its ability to support new recommendation models and to bring improved scalability as technology evolves.
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 represents a foundational paradigm shift from traditional classical computational models, leveraging the intrinsic mechanics of quantum physics—specifically superposition, entanglement, and unitary interference—to execute parallel processing frameworks at an exponential operational scale. This state-of-the-art review systematically deconstructs the mathematical framework of quantum bit (qubit) state vectors within complex Hilbert spaces and details pioneering algorithms like Shor's and Grover's operations. Furthermore, we evaluate dominant contemporary hardware frameworks, specifically contrasting superconducting circuits against trapped-ion architectures across critical indices of coherence metrics, operational speeds, and thermal constraints. Finally, this review highlights systemic algorithmic barriers including phase-flip/bit-flip noise profiles, decoherence phenomena, and real-world deployment challenges across cryptographic infrastructure and biochemical modeling matrices, charting the roadmap from Noisy Intermediate-Scale Quantum (NISQ) systems toward scalable, fault-tolerant ecosystems.