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.
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.
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.
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.
The growing demand for large-scale quantum computers is pushing research on Distributed Quantum Computing (DQC). Recent experimental efforts have demonstrated some of the building blocks for such a design. DQC systems are clusters of quantum processing units (QPUs) connected by means of quantum network infrastructures. Their extension ranges from the single box to the geographical scale. Furthermore, they can be integrated with classical High Performance Computing systems. Simulation modeling of DQC architectures provides a safe way to test and explore different what-if scenarios. Many simulation tools have been developed to support the research community in designing and evaluating quantum computer and quantum network technologies, including hardware, protocols, and applications. However, a framework for DQC simulation putting equal emphasis on computational and networking aspects has never been proposed, so far. In this paper, a design framework for DQC simulation is presented, whose core component is an Execution Manager that schedules DQC jobs for running on networked quantum computers. Two metrics are proposed for evaluating the impact of the job scheduling algorithms with respect to QPU utilization and quantum network utilization, beyond the traditional concept of makespan. The discussion is supported by a DQC job scheduling example, where two different strategies are compared in terms of the proposed metrics.
Quantum cloud platforms, which rely on Noisy Intermediate-Scale Quantum (NISQ) devices, face significant challenges in efficiently managing quantum programs. This paper proposes a QPU Scheduling and Resource Allocation (QSRA) approach to address these challenges. QSRA enhances qubit utilization and reduces turnaround time by adapting CPU scheduling techniques to Quantum Processing Units (QPUs). It incorporates a subroutine for qubit allocation that takes into account qubit quality and connectivity, while also merging multiple quantum programs to further optimize qubit usage. Our evaluation of QSRA against existing methods demonstrates its effectiveness in improving both qubit utilization and turnaround time.
Cloud computing can be revolutionized by quantum computing which will offer the world more computational power than has ever been seen to solve complex issues.Quantum computing coupled with cloud computing enables the remote access to quantum resources, thus greatly minimizing the cost, technical, and operational difficulties of having quantum hardware owned and maintained in the field.The integration makes large-scale data processing, cryptography, and optimization tasks as well as new applications in artificial intelligence efficient in terms of their computation.This work is a review of the existing approaches, system, and systems to quantum cloud computing, the main algorithms, software applications, implementation plans, and real-life examples.We find that quantum cloud computing provides significant enhancements in computational speed and parallelism, scalability, as well as provides the capability to process data securely and to execute quantum circuits remotely.However, there are still a few obstacles such as stability of qubits, error correction, noise reduction, and effective resource utilization, which restrict the practical use of quantum cloud services.The findings indicate that, irrespective of these challenges, quantum computing with the use of cloud computing platforms offers meaningful potentials to scientific discovery, business, and an AI-based innovation.The paper wraps up by noting that further research should be done to enhance the reliability of quantum hardware, optimize quantum algorithms, and design quantum cloud computing security systems, enabling quantum cloud computing to be adopted more broadly as a more transformative model of computation and ensuring that quantum cloud computing can grow sustainably.
Quantum computing combined with cloud infrastructure is a paradigm-shifting area in computational frameworks. In this paper, we explore the multidimensional aspects of combining quantum processing power into standard cloud computing infrastructures. We discuss upcoming quantum-cloud architecture designs, resource planning approaches, and security challenges of this emerging hybrid paradigm. Our analysis demonstrates significant promise for quantum acceleration within virtualized domains of computation while pinpointing large technical challenges that must be addressed before adoption becomes widespread. Theoretical evaluation of existing quantum cloud service models and experimental deployment scenarios, we outline a roadmap for successful quantum-classical integration in distributed computing systems. We also introduce a novel quantum resource abstraction framework for seamless integration with the classical virtualization infrastructure. The research finds that while enterprise-scale quantum cloud deployment is on the horizon, strategic use of quantum processing units for specialized applications offers near-term value for cloud service providers and their customers.