As the popularity of quantum computing continues to grow, quantum machine access over the cloud is critical to both academic and industry researchers across the globe. And as cloud quantum computing demands increase exponentially, the analysis of resource consumption and execution characteristics are key to efficient management of jobs and resources at both the vendor-end as well as the client-end. While the analysis of resource consumption and management are popular in the classical HPC domain, it is severely lacking for more nascent technology like quantum computing. This paper is a first-of-its-kind academic study, analyzing various trends in job execution and resources consumption / utilization on quantum cloud systems. We focus on IBM Quantum systems and analyze characteristics over a two year period, encompassing over 6000 jobs which contain over 600,000 quantum circuit executions and correspond to almost 10 billion “shots” or trials over 20+ quantum machines. Specifically, we analyze trends focused on, but not limited to, execution times on quantum machines, queuing/waiting times in the cloud, circuit compilation times, machine utilization, as well as the impact of job and machine characteristics on all of these trends. Our analysis identifies several similarities and differences with classical HPC cloud systems. Based on our insights, we make recommendations and contributions to improve the management of resources and jobs on future quantum cloud systems.
As the popularity of quantum computing continues to grow, quantum machine access over the cloud is critical to both academic and industry researchers across the globe. And as cloud quantum computing demands increase exponentially, the analysis of resource consumption and execution characteristics are key to efficient management of jobs and resources at both the vendor-end as well as the client-end. While the analysis of resource consumption and management are popular in the classical HPC domain, it is severely lacking for more nascent technology like quantum computing. This paper is a first-of-its-kind academic study, analyzing various trends in job execution and resources consumption / utilization on quantum cloud systems. We focus on IBM Quantum systems and analyze characteristics over a two year period, encompassing over 6000 jobs which contain over 600,000 quantum circuit executions and correspond to almost 10 billion “shots” or trials over 20+ quantum machines. Specifically, we analyze trends focused on, but not limited to, execution times on quantum machines, queuing/waiting times in the cloud, circuit compilation times, machine utilization, as well as the impact of job and machine characteristics on all of these trends. Our analysis identifies several similarities and differences with classical HPC cloud systems. Based on our insights, we make recommendations and contributions to improve the management of resources and jobs on future quantum cloud systems.
As the popularity of quantum computing continues to grow, efficient quantum machine access over the cloud is critical to both academic and industry researchers across the globe. And as cloud quantum computing demands increase exponentially, the analysis of resource consumption and execution characteristics are key to efficient management of jobs and resources at both the vendor-end as well as the client-end. While the analysis and optimization of job / resource consumption and management are popular in the classical HPC domain, it is severely lacking for more nascent technology like quantum computing.This paper proposes optimized adaptive job scheduling to the quantum cloud taking note of primary characteristics such as queuing times and fidelity trends across machines, as well as other characteristics such as quality of service guarantees and machine calibration constraints. Key components of the proposal include a) a prediction model which predicts fidelity trends across machine based on compiled circuit features such as circuit depth and different forms of errors, as well as b) queuing time prediction for each machine based on execution time estimations.Overall, this proposal is evaluated on simulated IBM machines across a diverse set of quantum applications and system loading scenarios, and is able to reduce wait times by over 3x and improve fidelity by over 40% on specific usecases, when compared to traditional job schedulers.
Quantum computers face challenges due to hardware constraints, noise errors, and heterogeneity, and face fundamental design tradeoffs between key performance metrics such as \textit{quantum fidelity} and system utilization. This substantially complicates managing quantum resources to scale the size and number of quantum algorithms that can be executed reliably in a given time. We introduce QOS, a cloud operating system for managing quantum resources while mitigating their inherent limitations and balancing the design tradeoffs of quantum computing. QOS exposes a hardware-agnostic API for transparent quantum job execution, mitigates hardware errors, and systematically multi-programs and schedules the jobs across space and time to achieve high quantum fidelity in a resource-efficient manner. To achieve this, it leverages two key insights: First, to maximize utilization and minimize fidelity loss, some jobs are more compatible than others for multi-programming on the same quantum computer. Second, sacrificing minimal fidelity can significantly reduce job waiting times. We evaluate QOS on real quantum devices hosted by IBM, using 7000 real quantum runs of more than 70.000 benchmark instances. We show that the QOS achieves 2.6--456.5× higher fidelity, increases resource utilization by up to 9.6×, and reduces waiting times by up to 5× while sacrificing only 1--3\% fidelity, on average, compared to the baselines.
Quantum Computing (QC) has evolved from a few custom quantum computers, which were only accessible to their creators, to an array of commercial quantum computers that can be accessed on the cloud by anyone. Accessing these cloud quantum computers requires a complex chain of tools that facilitate connecting, programming, simulating algorithms, estimating resources, submitting quantum computing jobs, retrieving results, and more. Some steps in the chain are hardware dependent and subject to change as both hardware and software tools, such as available gate sets and optimizing compilers, evolve. Understanding the trade-offs inherent in this process is essential for evaluating the power and utility of quantum computers. ARLIS has been systematically investigating these environments to understand these complexities. The work presented here is a detailed summary of three months of using such quantum programming environments. We show metadata obtained from these environments, including the connection metrics to the different services, the execution of algorithms, the testing of the effects of varying the number of qubits, comparisons to simulations, execution times, and cost. Our objective is to provide concrete data and insights for those who are exploring the potential of quantum computing. It is not our objective to present any new algorithms or optimize performance on any particular machine or cloud platform; rather, this work is focused on providing a consistent view of a single algorithm executed using out-of-the-box settings and tools across machines, cloud platforms, and time. We present insights only available from these carefully curated data.
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
The convergence of artificial intelligence (AI), quantum computing, and cloud infrastructure represents a paradigm shift in how intelligent systems are designed, trained, and deployed. As AI models continue to demand exponential computational resources, classical systems are increasingly reaching their performance limits in domains such as high-dimensional optimization, complex pattern recognition, and probabilistic modeling. Quantum computing, with its foundational principles of superposition, entanglement, and quantum parallelism, offers a theoretical edge in solving problems that are intractable for conventional hardware. However, the practical deployment of quantum algorithms—particularly those relevant to machine learning (ML) has been hindered by the limited scalability and accessibility of quantum hardware. To bridge this gap, cloud-based quantum computing platforms have emerged, providing remote, programmable access to quantum devices and hybrid runtimes. In this context, the present study offers a comprehensive review of cloud-delivered quantum AI systems, examining core architectures, hybrid quantum-classical algorithms, platform capabilities, and real-world use cases. The paper synthesizes recent advances and performance insights from systems such as Amazon Braket, IBM Quantum, and Azure Quantum, while critically analyzing algorithmic trends like variational quantum circuits and quantum kernel methods. It further explores challenges including circuit depth limitations, latency in hybrid models, and standardization barriers across toolchains. By consolidating current research and implementation patterns, this review aims to inform the design and future trajectory of scalable, cloud-native quantum AI applications.
Quantum computing has emerged as a revolutionary approach to solving complex problems that demand powerful computational capabilities. With various cloud quantum computing services available, users are presented with different architectures and performances. In this study, we conduct research on select cloud quantum computing services to evaluate their performances and compare them. Using two different methods, visual programming and Qiskit, we test these services and analyze the impact of factors such as the number of qubits per backend and shots per run on execution time. Our findings provide users with insights to decide which cloud quantum computing services offer better performance or faster execution times based on their specific requirements.