Hybrid quantum-classical workflows are expected to underpin practical quantum computing applications, yet the quantum and HPC communities lack a shared framework for reasoning about where and when their integration requirements matter most. Such a framework must separate two distinct levels of analysis: the application level, where communication overhead affects runtime performance, and the real-time level, where it determines feasibility. To address this, we introduce a runtime model that decomposes workflow execution into quantum compute, classical compute, and communication costs. At the application level, a communication-to-computation ratio from this decomposition quantifies whether a workflow is communication-bound or compute-bound; at the real-time level, a feasibility constraint determines whether timing requirements can be met at all, with the reaction time setting the logical clock speed of fault-tolerant computation once they are. Application of this model to representative workflows demonstrates that co-location of quantum processors with HPC infrastructure offers negligible performance benefit for compute-intensive applications today, while tight integration remains crucial for real-time tasks such as quantum error correction needed for large scale quantum computations. However, we discuss how even at the application level these assessments may shift with hardware evolution, illustrating how the model can identify specific crossover conditions, and how, under fault tolerance, the reaction time can set application-level performance.
Hybrid quantum-classical workflows are expected to underpin practical quantum computing applications, yet the quantum and HPC communities lack a shared framework for reasoning about where and when their integration requirements matter most. Such a framework must separate two distinct levels of analysis: the application level, where communication overhead affects runtime performance, and the real-time level, where it determines feasibility. To address this, we introduce a runtime model that decomposes workflow execution into quantum compute, classical compute, and communication costs. At the application level, a communication-to-computation ratio from this decomposition quantifies whether a workflow is communication-bound or compute-bound; at the real-time level, a feasibility constraint determines whether timing requirements can be met at all, with the reaction time setting the logical clock speed of fault-tolerant computation once they are. Application of this model to representative workflows demonstrates that co-location of quantum processors with HPC infrastructure offers negligible performance benefit for compute-intensive applications today, while tight integration remains crucial for real-time tasks such as quantum error correction needed for large scale quantum computations. However, we discuss how even at the application level these assessments may shift with hardware evolution, illustrating how the model can identify specific crossover conditions, and how, under fault tolerance, the reaction time can set application-level performance.
Hybrid quantum-classical computing has emerged as the dominant paradigm for near-term quantum applications, yet hybrid workflows are typically described by individual algorithms rather than their underlying execution behavior. We introduce the Quantum Execution Locality Framework (QELF), a qualitative framework for characterizing hybrid quantum-classical workflows according to recurring dataflow structures and quantum execution locality, the extent to which computation remains resident on the Quantum Processing Unit (QPU) before host intervention or classical synchronization. From a representative cross-section of applications, QELF identifies five recurring computational patterns with distinct locality characteristics and discusses their implications for communication overhead, workflow organization, and future hybrid computing architectures. By providing a common vocabulary for reasoning about hybrid workloads, QELF establishes a foundation for future quantitative validation and the co-design of algorithms, runtime systems, and hybrid computing architectures.
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.
The computational requirements of applications running in supercomputing centres are growing rapidly and diversifying over time. To meet these needs, developers are constantly developing new processors, domain-specific accelerators, and technologies. Quantum computing is one notable technology that has the potential to speed up certain computations and could be used to enhance High Performance Computing (HPC). However, integrating a quantum computer (QC) into HPC systems is a complex and challenging task, both at the physical and software level. Although various types of accelerators have been integrated into HPC in the past, the integration of QC into HPC presents a unique challenge as it operates on a new computing paradigm and is a scarce resource that needs to be shared among multiple jobs. Furthermore, HPCQC integration is a multidisciplinary field and integration can mean different things to different stakeholders. Our approach to HPCQC integration is from the perspective of an HPC centre where QC is integrated into the HPC environment as an accelerator. We identify parameters that are important for the integration, including network latency, HPCQC hybrid application characteristics and fine-grained scheduling. In addition, we develop a custom simulation model that can comprehensively simulate a hybrid classical-quantum compute system at selected level of detail. This simulation model is utilised to simulate the performance of a hybrid application by adjusting its features on a hybrid system with varying parameters. We discover that fine-grained scheduling is critical to performance, and network latency between QC and HPC has a significant impact on hybrid system performance.
Abstract The integration of cloud computing architectures with quantum computational models represents a significant paradigm shift in the field of large-scale data analysis. While quantum processors possess the theoretical capability to solve complex statistical and logistical problems exponentially faster than classical systems, deploying them within conventional cloud environments introduces severe performance bottlenecks. These challenges primarily revolve around network latency, quantum state decoherence, and the inadequacies of classical resource provisioning algorithms. In this paper, we explore the performance issues associated with hybrid quantum-cloud infrastructures and propose a structured decision-making and scheduling framework. By analyzing deployment constraints, cryptographic overheads, and hardware limitations, this study highlights the critical pathways and ethical considerations necessary for standardizing Quantum-as-a-Service (QaaS) in modern enterprise datacenters. Keywords: Quantum processors, Quantum-as-a-Service(QaaS), makespan 1.Introduction The integration of cloud computing and quantum computation represents a crucial frontier for the future of complex data analysis. Cloud computing has revolutionized the modern information technology landscape by transitioning computational resources from localized, purchased products to highly scalable, on-demand services delivered over the internet. Simultaneously, quantum computing offers unprecedented computational advantages through fundamental physical phenomena like entanglement and superposition, enabling the resolution of highly complex logistical, chemical, and statistical problems. However, merging these two distinct technological domains into a unified framework introduces severe performance bottlenecks related to dynamic resource provisioning, network communication latency, and task scheduling. The primary problem lies in the efficient execution of large-scale data analysis pipelines that make both classical datacenters and quantum processing units (QPUs). Existing approaches are demonstrably insufficient for managing this hybrid computing environment for two main reasons. First, conventional cloud load balancing and resource allocation algorithms are designed solely for deterministic classical virtual machines, fundamentally failing to account for the unique coherence times, error rates, and state preparation constraints of quantum hardware. Fig 1.1 shows Cloud balancing. Second,current cryptographic protocols designed for delegated quantum computing , while excellent at ensuring the blindness and security of user data, impose substantial communication and entangling overheads that severely degrade end-to-end performance in real-world datacenter along with Circuit diagram in Fig.1.2. Figure 1.1 Cloud Balancing with web servers To address these critical performance issues, this paper introduces a novel architectural approach designed specifically for hybrid quantum-cloud systems. We aim to bridge the gap between theoretical quantum potential and practical cloud deployment. Specifically, our work makes the following contributions: We propose a structured, hybrid decision-making and scheduling framework that dynamically partitions and routes data analysis tasks between classical cloud servers and quantum nodes. We formulate a comprehensive, hypothetical evaluation methodology to simulate the makespan and resource utilization of this hybrid system, identifying key latency bottlenecks in delegated quantum operations. Fig 1.2. Quantum Circuit diagram 2.Classical Cloud Computing The first category of related work focuses on resource management and provisioning in classical cloud computing. Traditional methods in this domain emphasize minimizing the computational makespan of tasks through selective heuristics, such as min-min and max-min scheduling algorithms, which allocate virtual machines based on fluctuating task demands. Furthermore, comprehensive load balancing schemes have been developed to ensure efficient resource utilization in accordance with strict Service Level Agreements (SLAs), allowing resources to be provisioned on a pay-as-you-go basis. While these classical models demonstrate robust strengths in minimizing computational wait times and maximizing server efficiency, their fundamental weakness is their inability to schedule probabilistic and highly sensitive quantum operations. In contrast to these purely classical frameworks, our proposed work directly integrates quantum-specific hardware constraints, such as state preparation delays and decoherence windows, into the provisioning logic. The second category addresses broader enterprise cloud adoption and organizational decision-making frameworks. Research in this area highlights the necessity for generic, multi-criteria decision frameworks that evaluate cloud infrastructures beyond mere cost considerations, factoring in specific enterprise risks, benefits, and IT support metrics. Additionally, numerous studies have developed strategic roadmaps for Small and Medium Enterprises (SMEs) to navigate complex interoperability, knowledge, and security challenges during cloud migration. Although these frameworks provide excellent strategic guidance for adopting standard classical services, they lack operational mechanisms for managing the performance issues introduced by bleeding-edge quantum co-processors. Our work builds upon these established enterprise decision-making structures by providing a functional, low-level deployment pipeline tailored for hybrid operations. The third category explores the theoretical foundations of delegated quantum computing and interdisciplinary quantum applications. Delegated quantum computing allows clients with extremely low computational capabilities to securely outsource demanding tasks to a powerful quantum server, often utilizing measurement-based models and complex prepare-and-send communication paradigms to preserve data blindness. Concurrently, quantum computing frameworks have been utilized to explore deep interdisciplinary problems—ranging from physics paradoxes to advanced chemical modeling—demonstrating vast analytical potential. The primary weakness of this category is that much of the academic research remains focused on abstract theoretical protocols or isolated quantum circuits, rather than addressing the physical infrastructure pitfalls and massive scale required for cloud integration. This paper bridges that gap by embedding delegated quantum paradigms within a concrete, performance-oriented enterprise cloud architecture. 3.Hybrid Quantum-Cloud To tackle the performance issues inherent in hybrid data analysis, we propose a modular Hybrid Quantum-Cloud Provisioning Framework. The system follows a structured, numbered pipeline designed to seamlessly route computational tasks across heterogeneous hardware. First, classical user requests are ingested by a central cloud load balancer that analyzes the computational complexity and algorithmic requirements of the incoming dataset. Second, the system employs a multi-criteria decision engine to partition the workflow; classical data preprocessing is routed to standard virtual machines, while complex optimization components are packaged as isolated quantum circuits. Third, the framework utilizes a delegated quantum computing protocol to securely transmit the quantum state from the classical node to the QPU, applying a prepare-and-send methodology to preserve data blindness during execution. Finally, the resulting quantum measurements are returned to the classical cloud for post-processing and aggregation before the final analysis is delivered to the end user. The core design choice in this approach is the adaptation of selective scheduling heuristics to manage the delicate interaction between classical datacenters and quantum nodes. Traditional task allocation algorithms are augmented to treat QPUs as highly specialized, time-shared resources with exceptionally strict execution windows dictated by qubit decoherence times. Furthermore, we utilize an ad hoc resource harvesting concept for the classical preprocessing stages, allowing sporadic, non-exclusive classical resources to rapidly feed structured data into the highly controlled quantum environment. This strict division of labor ensures that the expensive, high-maintenance quantum hardware is never left idle waiting for classical data transformations, thereby optimizing the overall makespan and mitigating critical performance bottlenecks. To validate the efficacy of this proposed framework, we outline a comprehensive hypothetical evaluation plan. We propose extending a standard cloud simulation environment, such as CloudSim, by integrating a custom quantum execution module that mathematically models qubit gate latencies, state preparation times, and network communication overheads. The hypothetical evaluation will benchmark our hybrid scheduling algorithm against strict First-Come-First-Serve (FCFS) provisioning policies across varying data analysis workload intensities. By tracking the total task makespan and server utilization rates, we anticipate demonstrating that intelligent algorithmic partitioning significantly reduces the performance degradation typically associated with delegated quantum computing overheads. 4.Practical implications The deployment of hybrid quantum-cloud systems carries significant practical implications for the future of enterprise IT infrastructure. From a deployment perspective, organizations will no longer need to invest heavily in fragile, independent quantum hardware; instead, they will consume computation through standardized cloud providers. This shift mandates a reevaluation of existing academic cloud research, pushing industry professionals to move beyond small-scale toy deployments and embrace complex, data-center-level abstractions that in
As computational demands in scientific applications continue to rise, hybrid high-performance computing (HPC) systems integrating classical and quantum computers (HPC-QC) are emerging as a promising approach to tackling complex computational challenges. One critical area of application is Hamiltonian simulation, a fundamental task in quantum physics and other large-scale scientific domains. This paper investigates strategies for quantum-classical integration to enhance Hamiltonian simulation within hybrid supercomputing environments. By analyzing computational primitives in HPC allocations dedicated to these tasks, we identify key components in Hamiltonian simulation workflows that stand to benefit from quantum acceleration. To this end, we systematically break down the Hamiltonian simulation process into discrete computational phases, highlighting specific primitives that could be effectively offloaded to quantum processors for improved efficiency. Our empirical findings provide insights into system integration, potential offloading techniques, and the challenges of achieving seamless quantum-classical interoperability. We assess the feasibility of quantum-ready primitives within HPC workflows and discuss key barriers such as synchronization, data transfer latency, and algorithmic adaptability. These results contribute to the ongoing development of optimized hybrid solutions, advancing the role of quantum-enhanced computing in scientific research.
The Noisy Intermediate-Scale Quantum (NISQ) era, defined by qubit counts in the hundreds and gate error rates that preclude long coherence times, imposes severe constraints on purely quantum computation. The central problem is that achieving practical quantum advantage for real-world tasks must overcome these hardware limitations, yet fault-tolerant quantum computers with full error correction remain years away. To address this, we present a systematic survey of hybrid classical-quantum computing models, in which classical high-performance resources orchestrate the workflow, perform iterative optimization, and apply error mitigation to noisy quantum processing units. Our review encompasses variational quantum algorithms, such as the variational quantum eigensolver and quantum approximate optimization algorithm; classical optimizers tailored for noisy quantum landscapes; error mitigation techniques including zero-noise extrapolation and probabilistic error cancellation; and circuit knitting that partitions large circuits across small QPUs. We find that these hybrid architectures have already demonstrated domain-specific utility in quantum chemistry for molecular ground-state estimation, in logistics and finance for combinatorial optimization, and in machine learning for generative and recommendation models. Notably, they often surpass purely classical or purely quantum approaches on carefully chosen benchmarks. The evidence indicates that hybrid classical-quantum systems are not a temporary stopgap but a durable paradigm. Their role will persist into the early fault-tolerant era, where small, logical qubits will similarly require classical coordination. Consequently, the field urgently demands the establishment of standardized hybrid performance benchmarks, the maturation of robust quantum-classical middleware capable of efficient transpiration and execution management, and holistic co-design frameworks that seamlessly integrate hardware constraints, software stack layers, and application-specific requirements from the outset.