This article addresses the problem of constructing a distributed task management model in a hybrid quantum-classical computing environment. The relevance of this topic stems from the need for efficient distribution of computing resources in the face of limited availability of quantum devices and the high complexity of applied problems requiring quantum acceleration. A three-tier architecture of the hybrid system is proposed, with responsibilities divided between the task, control, and hardware infrastructure levels. Dispatching and adaptive scheduling principles are described, taking into account the specific features of variational quantum-classical algorithms, the stochastic nature of quantum computing, and the capacity limitations of quantum resources. A methodology for formalizing hybrid workflows, selecting optimality criteria, and synthesizing control rules is developed based on queuing theory, Markov processes, and multi-agent models.
This article addresses the problem of constructing a distributed task management model in a hybrid quantum-classical computing environment. The relevance of this topic stems from the need for efficient distribution of computing resources in the face of limited availability of quantum devices and the high complexity of applied problems requiring quantum acceleration. A three-tier architecture of the hybrid system is proposed, with responsibilities divided between the task, control, and hardware infrastructure levels. Dispatching and adaptive scheduling principles are described, taking into account the specific features of variational quantum-classical algorithms, the stochastic nature of quantum computing, and the capacity limitations of quantum resources. A methodology for formalizing hybrid workflows, selecting optimality criteria, and synthesizing control rules is developed based on queuing theory, Markov processes, and multi-agent models.
Abstract Integrating quantum network optimization with traditional cloud computing presents a groundbreaking approach to enhancing computational efficiency and resource management. This study investigates the quantifiable impact of quantum-classical hybrid systems in cloud environments, focusing on resource utilization enhancement and task scheduling optimization. Using a MATLAB-based simulation environment interfaced with Variational Quantum Eigensolver (VQE) simulations, this research implements a three-layer architecture across 50 nodes, combining quantum optimization algorithms with classical scheduling techniques through a GPS queue model. The hybrid quantum-classical framework achieves remarkable improvements, including a 180% increase in task throughput, 70% reduction in response time, 41.5% improvement in resource utilization, and 27.1% enhancement in energy efficiency while maintaining 85% efficiency under peak load conditions and demonstrating 99.5% success rate for short-term tasks and 97.8% for long-running operations. Despite hardware limitations of existing quantum processors and scalability constraints, the system establishes a foundation for future developments, including advanced error correction mechanisms, expanded system scalability beyond 50 nodes, and enhanced resource utilization. These findings establish quantum-enhanced cloud computing as a viable solution for next-generation cloud infrastructure, particularly in managing complex workloads and optimizing resource allocation.
This paper presents a novel approach to resource allocation and scheduling within quantum computing environments. Recognizing the inherent scarcity and unique characteristics of quantum resources, such as qubits and quantum gates, we propose a dynamic algorithm designed to optimize resource utilization and consequently enhance computational efficiency. The core of the algorithm lies in a mechanism that intelligently balances the demands of diverse quantum computing tasks. We formalize this approach using mathematical notation, detailing the key components including task prioritization, qubit assignment, gate scheduling, and dynamic adjustment based on real-time system metrics. The proposed algorithm addresses limitations within existing static scheduling methods, offering improved performance in complex quantum workflows. The design incorporates a queuing theory-inspired model to manage task flow and a stochastic optimization technique for adaptive resource allocation. Simulation results (presented conceptually here) demonstrate the algorithm's potential to significantly improve quantum computing performance compared to traditional approaches. Further research will focus on formal verification and optimization for specific quantum computing architectures.
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.
Inspired by the ideas of quantum physics, quantum computing is a modern computational paradigm. It is one of the rapidly growing areas in computer science, mathematics, and physics. It has high speed and efficiency by making use of phenomena such as superposition, entanglement, and interference, allowing new possibilities in areas like cryptography, machine learning, and complex system simulations. Even though it has great potential, quantum computing also suffers from resource management and task scheduling issues, system design due to the differences in the foundational basis between classical and quantum computing. This study reviews the architecture and workflow of QOS (quantum operating system), emphasizing the integration of quantum and classical resources. In this study, elements such as quantum resource management, automatic qubit calibration, quantum program compilation, and hybrid task scheduling are investigated. The present study also looks at innovative architectures such as quantum FPGAs, x86-based quantum systems, and distributed quantum systems, which provide advanced quantum computations while being compatible with classical systems. The findings of this paper reveal how QOS improves fidelity, resource utilization, and task scheduling. We have also explored key quantum algorithms such as Grover’s and Shor’s algorithms, highlighting their potential in NP-hard problems[1], quantum machine learning, and optimization challenges. This research addresses the transformative power of QOS in solving real problems in computation, providing robust solutions in the management and programming of quantum resources. This research advances the development of future quantum computing systems by evaluating existing designs and suggesting improvements in task scheduling, qubit accuracy, and quantum algorithm performance. The findings in this paper seek to contribute to the advancement of understanding of QOS and inspire innovation in several fields.
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.
The integration of quantum computing technologies with established classical infrastructure represents a transformative advancement in computational capabilities. This manuscript addresses architectural frameworks enabling seamless orchestration between quantum and classical environments through container-based methodologies. Kubernetes extensions provide standardized interfaces for managing hybrid workloads while ensuring reproducibility across experimental iterations. Custom resource definitions facilitate quantum job specifications with appropriate hardware requirements, while specialized operators handle complex scheduling demands inherent to quantum processing. Implementation architectures demonstrate enhanced resource utilization through dynamic allocation mechanisms that accommodate both paradigms simultaneously. Practical evaluations reveal substantial improvements in workflow automation when orchestration platforms coordinate quantum simulators alongside cloud-based quantum processing units. The extensibility of container-based solutions proves particularly valuable for enterprises navigating evolving quantum hardware landscapes. The technological foundation described establishes a robust framework for quantum-classical integration while addressing security and deployment considerations. Findings indicate strategic advantages in adopting containerized approaches for quantum workflow management, particularly regarding experimental reproducibility and resource optimization across heterogeneous computing environments.