Computing today requires a lot of processing power, an immersive environment, and excellent precision. In this paper, we propose a hybrid multiprocessor architecture with quantum integration for data-intensive applications like a metaverse. Regular non-computationally intensive applications are handled by the central processing unit (CPU) and video rendering, graphically intensive programs by the graphics processing unit (GPU), and enormous, parallel, and computationally intensive applications by the quantum processing unit (QPU). To achieve higher permanence computing, we provide combined CPU, GPU, and QPU architecture in this work. This paper provides a roadmap for truly high-performance computing for massive real-time systems. In addition, we present an onsite and cloud-based architecture for scalability. Also, we present multimodality security protection systems for internal and external communication based on the split computing paradigm.
Computing today requires a lot of processing power, an immersive environment, and excellent precision. In this paper, we propose a hybrid multiprocessor architecture with quantum integration for data-intensive applications like a metaverse. Regular non-computationally intensive applications are handled by the central processing unit (CPU) and video rendering, graphically intensive programs by the graphics processing unit (GPU), and enormous, parallel, and computationally intensive applications by the quantum processing unit (QPU). To achieve higher permanence computing, we provide combined CPU, GPU, and QPU architecture in this work. This paper provides a roadmap for truly high-performance computing for massive real-time systems. In addition, we present an onsite and cloud-based architecture for scalability. Also, we present multimodality security protection systems for internal and external communication based on the split computing paradigm.
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.
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.
The drastic growth of data generation by companies such as Google and Facebook, the widespread integration of IoT devices into everyday life, advancements in artificial intelligence, and the rise of social media platforms are driving a shift from model-driven to data-driven science. This surge in data created a need for massively parallel accelerators such as graphics processing units (GPU). High Performance Computing (HPC) are required to accommodate the growing complexity of data-driven technologies. HPC demands are anticipated to reach exascale levels within this decade. Since 2004, three waves of high-performance accelerators have driven advancements in HPC: the first wave introduced Central Processing Units (CPUs) and Field Programmable Gate Arrays (FPGAs), the second wave brought General Purpose Graphics Processing Units (GPGPUs), and the third wave focused on machine learning specific GPUs. Recent research reveals that quantum processing units (QPUs) have the potential to be the fourth wave of high-performance acceleration in the near future. However, as Quantum Computing is still in its early stages, its practical and useful quantum computation is yet to be developed. In this paper, a heterogeneous CPU-GPU-QPU (HCGQ) accelerator is proposed. The accelerator is based on an input size threshold, where the CPU assigns smaller input sizes workloads to the GPU, and larger input sizes to the QPU. This dynamic workload allocation is realized entirely through software-level integration. Using the searching algorithm and deep neural networks as benchmarks, it was observed that the HCGQ accelerator outperformed standalone GPUs and QPUs in terms of speed and energy efficiency.
The technology of Quantum Computing (QC) is continuously evolving, as researchers explore new technologies and the public gains access to quantum computers with an increasing number of qubits. In addition, the research community and industry are increasingly interested in the potential use, application, and contribution of QCs to large-scale problems in the real world as a result of this technological enhancement. QCs operations are based on quantum mechanics, and their special properties are mainly exploited to solve computationally intensive problems in polynomial time, problems that are commonly unsolvable, even by High-Performance Computing systems (HPCs) in a feasible time. However, since QCs cannot perform as general-purpose computing machines, alternative computational approaches aiming to boost further their enormous computing abilities are requested, and their combination as an additional computing resource to HPC systems is considered as one of the most promising ones. In the proposed hybrid HPCs, the Quantum Processing Units (QPUs), similar to GPUs and CPUs, target specific problems that, through Quantum Algorithms, can exploit quantum properties like quantum entanglement and superposition to achieve substantial performance gains from the HPC point of view. This interconnection between classical HPC systems and QCs towards the creation of Hybrid Quantum-Classical computing systems is neither straightforward nor standardized while crucial for unlocking the real potential of QCs and achieving real performance improvements. The interconnection between the classical and quantum systems can be performed in the hardware, software (system), or application layer. In this study, a concise overview of the existing architectures for the interconnection interface between HPCs and QCs is provided, focusing mainly on hardware approaches that enable effective hybrid quantum-classical operation.
Quantum computing (QC) offers a new computing paradigm that has the potential to provide significant speedups over classical computing. Each additional qubit doubles the size of the computational state space available to a quantum algorithm. Such exponentially expanding reach underlies QC's power, but at the same time puts demanding requirements on the quantum processing units (QPU) hardware. On the other hand, purely classical simulations of quantum circuits on either central processing unit (CPU) or graphics processing unit (GPU) scale poorly as they quickly become bottlenecked by runtime and memory. This paper introduces CutQC, a scalable hybrid computing approach that distributes a large quantum circuit onto quantum (QPU) and classical platforms (CPU or GPU) for co-processing. CutQC demonstrates evaluation of quantum circuits that are larger than the limit of QPU or classical simulation, and achieves much higher quantum circuit evaluation fidelity than the large NISQ devices achieve in real-system runs.