Summary Quantum computing resources are predominantly accessible through cloud services, with a potential future shift to edge networks. This paradigm and the increasing global interest in quantum computing have amplified the need for efficient, adaptable resource management strategies and service models for quantum systems. However, many limitations in the quantum resources' quantity, quality, availability, and cost pose significant challenges for conducting research in practical environments. To address these challenges, we proposed iQuantum, a holistic and lightweight discrete‐event simulation toolkit uniquely tailored to model hybrid quantum computing environments. We also present a detailed system model for prototyping and problem formulation in quantum resource management. Through rigorous empirical validation and evaluations using large‐scale quantum workload datasets, we demonstrate the flexibility and applicability of our toolkit in various use cases. iQuantum provides a versatile environment for designing and evaluating quantum resource management policies such as quantum task scheduling, backend selection, hybrid task offloading, and orchestration in the quantum cloud‐edge continuum. Our work endeavors to create substantial contributions to quantum computing modeling and simulation, empowering the creation of future resource management strategies and quantum computing's broader applications.
Summary Quantum computing resources are predominantly accessible through cloud services, with a potential future shift to edge networks. This paradigm and the increasing global interest in quantum computing have amplified the need for efficient, adaptable resource management strategies and service models for quantum systems. However, many limitations in the quantum resources' quantity, quality, availability, and cost pose significant challenges for conducting research in practical environments. To address these challenges, we proposed iQuantum, a holistic and lightweight discrete‐event simulation toolkit uniquely tailored to model hybrid quantum computing environments. We also present a detailed system model for prototyping and problem formulation in quantum resource management. Through rigorous empirical validation and evaluations using large‐scale quantum workload datasets, we demonstrate the flexibility and applicability of our toolkit in various use cases. iQuantum provides a versatile environment for designing and evaluating quantum resource management policies such as quantum task scheduling, backend selection, hybrid task offloading, and orchestration in the quantum cloud‐edge continuum. Our work endeavors to create substantial contributions to quantum computing modeling and simulation, empowering the creation of future resource management strategies and quantum computing's broader applications.
Today’s quantum computers are primarily accessible through the cloud and are expected to be deployed in edge networks in the near future. With the rapid advancement and proliferation of quantum computing research worldwide, there has been a considerable increase in demand for using cloud-based quantum computation resources. This demand has highlighted the need for designing efficient and adaptable resource management strategies and service models for quantum computing. However, the limited quantity, quality, and accessibility of quantum resources pose significant challenges to practical research in quantum software and systems. To address these challenges, we propose iQuantum, a first-of-its-kind simulation toolkit that can model quantum computing environments for prototyping and evaluating system design and scheduling algorithms. This paper presents the quantum computing system model, architectural design, proof-of-concept implementation, potential use cases, and future development of iQuantum. Our proposed iQuantum simulator is anticipated to boost research in quantum software and systems, particularly in the creation and evaluation of policies and algorithms for resource management, job scheduling, and hybrid quantum-classical task orchestration in quantum computing environments integrating edge and cloud resources.
The convergence of quantum computing and high-performance computing (HPC) is giving rise to hybrid cloud platforms that coordinate workflows across classical and quantum resources. These heterogeneous environments introduce new challenges for workload management, including device variability, noise-prone quantum processors, and the need for synchronized orchestration across distributed systems. This survey reviews state-of-the-art techniques for scheduling, resource allocation, and workflow management in classical HPC, quantum clouds, and emerging hybrid infrastructures. A taxonomy is proposed to categorize workload management approaches by scheduling strategy, allocation model, orchestration mechanism, and performance metric. We present HybridCloudSim, a novel simulation framework designed to model hybrid quantum–HPC cloud environments and optimize resource usage under realistic constraints. HybridCloudSim is a conceptual foundation and a practical tool for advancing workload management in hybrid quantum–HPC systems.
Current quantum computing service providers operate in a cloud-based model, hosting expensive quantum computers in data centers for remote access. However, in the Noisy Intermediate-Scale Quantum (NISQ) era, quantum hardware is limited in terms of qubit count and connectivity. To address scalability challenges, future quantum cloud infrastructures will likely adopt a modular architecture where Quantum Processing Units (QPUs) are interconnected via quantum networks using various hardware technologies. In such systems, challenges like resource management, scheduling, and placement become critical, yet there is currently a gap in simulation tools capable of modeling quantum computers connected via quantum networks. To address this, we propose QuCloudSim, a quantum cloud simulator that adopts a modular design to model various hardware components within a quantum cloud environment. It employs discrete event simulation to replicate real-world scenarios with concurrent user requests, and our evaluation of different scheduling and placement methods demonstrates that QuCloudSim effectively models modular quantum cloud systems while providing valuable insights for developing scalable quantum cloud infrastructures.
Quantum programming is today largely discussed at the level of circuits and application programminginterfaces: a developer constructs a circuit, submits it to a simulator or a cloud-hosted device, and readsback measurement results. As quantum computing moves toward shared, heterogeneous, andincreasingly hybrid quantum-classical environments, a complementary systems-level question becomesimportant — how should the lifecycle of a quantum computation be managed once it leaves the hands ofthe programmer, particularly when many such computations compete for limited execution resources?This paper presents QuantumOS, a research-oriented software platform that treats a quantum circuit notmerely as an object to be executed, but as a managed workload: a quantum job that is created, queued,scheduled according to a selectable policy, assigned to a computational resource by a resource manager,optionally transformed by a circuit-processing stage, executed on a local Quantum Virtual Machine(QVM), and finally recorded and surfaced through a monitoring dashboard. QuantumOS is explicitly aresearch prototype and does not control physical quantum hardware; its current execution backend is aclassical, local circuit simulator. We describe the system's layered architecture, its job-lifecycle andscheduling abstractions, and its execution workflow, and we report an experimental evaluation of theQVM's performance and correctness characteristics. Measured results show that QVM execution timeremains low for small circuits and rises sharply beyond approximately eight to nine simulated qubits,consistent with the expected cost of classical state-vector simulation, and that execution time growsroughly in proportion to the number of measurement shots requested. A correctness evaluation of fourcanonical circuits — a single-qubit Hadamard gate, a Pauli-X gate, a two-qubit Bell state, and a three-qubitGHZ state, each sampled at 10,000 shots — shows observed outcome probabilities within approximatelyhalf a percentage point of their theoretical values, consistent with ordinary statistical sampling variationrather than simulation error. We discuss how the broader scheduling and resource-managementarchitecture supports the paper's central systems claim even where full quantitative benchmarking ofevery subsystem remains for future work, and we outline the educational value of a locally runnableplatform that makes both quantum-circuit behavior and workload-management concepts directlyobservable. We conclude by identifying the prototype's current limitations and the concrete next stepsrequired to mature QuantumOS into a more completely benchmarked research platform.
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.
Quantum computing resources are increasingly being incorporated into high-performance computing (HPC) environments as co-processors for hybrid workloads. To support this paradigm, quantum devices must be treated as schedulable first-class resources within existing HPC infrastructure. This enables consistent workload management, unified resource visibility, and support for hybrid quantum-classical job execution models. This paper presents a reference architecture and implementation for the integration of quantum computing resources, both on-premises and cloud-hosted into HPC centers via standard workload managers. We introduce a Slurm plugin designed to abstract and control quantum backends, enabling seamless resource scheduling, minimizing queue duplication, and supporting job co-scheduling with classical compute nodes. The architecture supports heterogeneous quantum resources and can be extended to any workload (and container) management systems.