Quantum processing units (QPUs) are currently exclusively available from cloud vendors. However, with recent advancements, hosting QPUs will soon be possible everywhere. Existing work has yet to draw from research in edge computing to explore systems exploiting mobile QPUs, or how hybrid applications can benefit from distributed heterogeneous resources. Hence, this work presents an architecture for Quantum Computing in the edge-cloud continuum. We discuss the necessity, challenges, and solution approaches for extending existing work on classical edge computing to integrate QPUs. We describe how warm-starting allows defining workflows that exploit the hierarchical resources spread across the continuum. Then, we introduce a distributed inference engine with hybrid classical-quantum neural networks (QNNs) to aid system designers in accommodating applications with complex requirements that incur the highest degree of heterogeneity. We propose solutions focusing on classical layer partitioning and quantum circuit cutting to demonstrate the potential of utilizing classical and quantum computation across the continuum. To evaluate the importance and feasibility of our vision, we provide a proof of concept that exemplifies how extending a classical partition method to integrate quantum circuits can improve the solution quality. Specifically, we implement a split neural network with optional hybrid QNN predictors. Our results show that extending classical methods with QNNs is viable and promising for future work.
Quantum processing units (QPUs) are currently exclusively available from cloud vendors. However, with recent advancements, hosting QPUs will soon be possible everywhere. Existing work has yet to draw from research in edge computing to explore systems exploiting mobile QPUs, or how hybrid applications can benefit from distributed heterogeneous resources. Hence, this work presents an architecture for Quantum Computing in the edge-cloud continuum. We discuss the necessity, challenges, and solution approaches for extending existing work on classical edge computing to integrate QPUs. We describe how warm-starting allows defining workflows that exploit the hierarchical resources spread across the continuum. Then, we introduce a distributed inference engine with hybrid classical-quantum neural networks (QNNs) to aid system designers in accommodating applications with complex requirements that incur the highest degree of heterogeneity. We propose solutions focusing on classical layer partitioning and quantum circuit cutting to demonstrate the potential of utilizing classical and quantum computation across the continuum. To evaluate the importance and feasibility of our vision, we provide a proof of concept that exemplifies how extending a classical partition method to integrate quantum circuits can improve the solution quality. Specifically, we implement a split neural network with optional hybrid QNN predictors. Our results show that extending classical methods with QNNs is viable and promising for future work.
The rapid growth of data generated by Internet of Things (IoT) devices necessitates the development of advanced computational frameworks that can efficiently handle real-time data processing. Traditional cloud and edge computing systems, while effective, face significant challenges, including high latency, limited computational resources, and data transfer bottlenecks. To address these issues, this study introduces a hybrid approach that integrates quantum computing into the edge-cloud continuum. The proposed method utilizes Quantum- Neural Networks (QNNs) in conjunction with local edge processing, leveraging quantum autoencoders for feature compression, and incorporating fully decentralized edge computing with quantum processors to enhance data processing capabilities. Additionally, a hybrid model combining QNNs with classical machine learning techniques is implemented at the edge for improved classification accuracy. The proposed system's performance is evaluated through extensive experiments across multiple datasets, comparing the results of quantum-based models with traditional classical and hybrid approaches. The results show a significant improvement in classification accuracy, reduced feature compression time, and enhanced processing efficiency, particularly in resource-constrained environments. The results underscore the potential of quantum machine learning in real-time, large-scale applications, marking an important step toward the practical integration of quantum computing in distributed computing systems.
This research investigates the limitations of current quantum hardware in fulfilling the computational demands of quantum neural networks. It introduces an optimized circuit computed method leveraging bit splitting within the framework of distributed quantum computing. The research exemplifies this approach by decomposing a 25-qubit variational quantum circuit into several smaller 5-qubit circuits, thereby enabling distributed quantum computations. A hybrid quantum-classical neural network architecture employing this methodology was implemented and evaluated on selected computer vision datasets. The results were promising, affirming the viability of the approach. The successful implementation of this architecture suggests that future distributed quantum computing systems will likely consist of scalable, heterogeneous quantum systems interconnected through an extensive quantum network. This configuration is anticipated to significantly enhance computational power and versatility, expanding the scope of quantum computing applications in real-world scenarios. This advancement not only validates the feasibility of distributed quantum architectures but also underscores their potential to revolutionize various technological domains through enhanced computational capabilities.
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 Machine Learning (QML) represents a transformative intersection of quantum computing and artificial intelligence, promising exponential speed-ups and new capabilities for data-driven tasks. However, the practical realization of QML is hindered by the current limitations of quantum hardware, including scalability constraints, qubit decoherence, and high error rates. Hybrid quantum-classical architectures have emerged as a pragmatic approach, leveraging classical resources alongside quantum processors to mitigate these hardware challenges. A promising direction for advancing QML is the integration of hybrid architectures into cloud computing platforms and mobile-edge environments. This approach enables scalable access to quantum resources, facilitates real-time data processing, and brings quantum-enhanced intelligence closer to end-users and devices. This article reviews the state-of-the-art in QML, focusing on how hybrid models and quantum algorithms are addressing the dual challenges of hardware scalability and architectural integration within cloud and mobile-edge computing contexts. Recent advances are analyzed, benchmark results are highlighted, and future directions for deploying robust, scalable QML systems in distributed computing environments are discussed.
Hybrid quantum-classical workflows combine quantum processing units (QPUs) with classical hardware to address computational tasks that are challenging or infeasible for conventional systems alone. Coordinating these heterogeneous resources at scale demands robust orchestration, reproducibility, and observability. Even in the presence of fault-tolerant quantum devices, quantum computing will continue to operate within a broader hybrid ecosystem, where classical infrastructure plays a central role in task scheduling, data movement, error mitigation, and large-scale workflow coordination.
In this work, we present a cloud-native framework for managing hybrid quantum-HPC pipelines using Kubernetes, Argo Workflows, and Kueue. Our system unifies CPUs, GPUs, and QPUs under a single orchestration layer, enabling multi-stage workflows with dynamic, resource-aware scheduling. We demonstrate the framework with a proof-of-concept implementation of distributed quantum circuit cutting, showcasing execution across heterogeneous nodes and integration of classical and quantum tasks. This approach highlights the potential for scalable, reproducible, and flexible hybrid quantum-classical computing in cloud-native environments.
We discuss a Quantum-Enhanced Computing Continuum, a heterogeneous, hybrid architecture that integrates quantum processing units (QPUs) within an Edge-Cloud-HPC fabric. Promote sustainability by shifting from performance to "energy-aware integration.' The architecture has three layers: a Physical Layer with shared fiber-optic infrastructure, a Control and Orchestration Layer managed by the user, and an Application Layer with an Adaptive Quantum Classical Fusion (AQCF) framework. Tighter system integration, like moving from cloud coupling to cryogenic logic, reduces energy waste and "thermal footprints.' The aim is a Green Performance Advantage: energy per problem solved in the era of Advanced Computing.