This paper introduces a lightweight predictive resource allocation framework for Quantum Operating Systems in cloud quantum computing. Current platforms often run user-submitted circuits serially on Quantum Processing Units (QPUs), leaving many qubits idle even when smaller circuits could execute in parallel on disjoint partitions. The proposed approach performs static analysis on the transpiled circuit at submission time, extracting three key metrics: qubit count (n), circuit depth (d), and total gate count (g). These metrics enable accurate estimation of each job’s resource needs and predicted execution time without runtime profiling. The framework combines two scheduling strategies: Resource-Aware FCFS (RA-FCFS) with crosstalk-aware bin-packing, which packs compatible circuits into idle qubit fragments while enforcing a minimum separation constraint (δ_sep = 2 edges on heavy-hex topologies) to control ZZ crosstalk and preserve fidelity. Quantum Highest Response Ratio Next (Q-HRRN), a priority policy that prevents starvation of long/deep circuits while maintaining fast response for short jobs. Discrete-event simulations on 27-qubit to 127-qubit heavy-hex devices using VQE, QAOA, and QFT workloads show significant gains: qubit utilization rises from 31–38% (serial baseline) to 79–87%, and average wait times decrease by 1.8× to 3.4×. The crosstalk guard limits additional error-budget to only 3.1%. This low-overhead solution requires no hardware changes and offers a practical path to improve efficiency in NISQ-era quantum cloud services.
This paper introduces a lightweight predictive resource allocation framework for Quantum Operating Systems in cloud quantum computing. Current platforms often run user-submitted circuits serially on Quantum Processing Units (QPUs), leaving many qubits idle even when smaller circuits could execute in parallel on disjoint partitions. The proposed approach performs static analysis on the transpiled circuit at submission time, extracting three key metrics: qubit count (n), circuit depth (d), and total gate count (g). These metrics enable accurate estimation of each job’s resource needs and predicted execution time without runtime profiling. The framework combines two scheduling strategies: Resource-Aware FCFS (RA-FCFS) with crosstalk-aware bin-packing, which packs compatible circuits into idle qubit fragments while enforcing a minimum separation constraint (δ_sep = 2 edges on heavy-hex topologies) to control ZZ crosstalk and preserve fidelity. Quantum Highest Response Ratio Next (Q-HRRN), a priority policy that prevents starvation of long/deep circuits while maintaining fast response for short jobs. Discrete-event simulations on 27-qubit to 127-qubit heavy-hex devices using VQE, QAOA, and QFT workloads show significant gains: qubit utilization rises from 31–38% (serial baseline) to 79–87%, and average wait times decrease by 1.8× to 3.4×. The crosstalk guard limits additional error-budget to only 3.1%. This low-overhead solution requires no hardware changes and offers a practical path to improve efficiency in NISQ-era quantum cloud services.
Access to cloud quantum hardware is expensive, queue-constrained, and subject to calibration variability -- making efficient utilisation of every quantum shot and circuit execution critical for research groups with finite quantum access budgets. This paper proposes the Quantum Hardware Resource Optimisation (QHRO) framework, a systematic methodology for maximising the scientific output per quantum hardware credit across five resource dimensions: shot budget allocation, circuit batching and job scheduling, hardware platform selection, calibration-aware execution timing, and quantum- classical co-processing balance. QHRO is evaluated on a six-month IBM Quantum Network access log covering 2,840 quantum job submissions from this journal's algorithm ecosystem. QHRO's shot allocation strategy (adaptive Bayesian shot budgeting) reduces total shots by 38.4% at equal result quality. Circuit batching reduces queue wait time by 48.4% through intelligent job packing. Calibration-aware scheduling reduces effective error rate by 18.4% by submitting jobs during low-noise calibration windows. QHRO provides a complete resource management system reducing total quantum hardware cost by 42.4% for the evaluated workload, with a practical implementation guide for research groups.
Quantum Processing Units are rapidly scaling, with current systems exceeding 100 qubits and targeting one billion operations in the near future. However, Quantum Computing as a Service platforms are facing growing pressure due to high user demand and long execution queues. This is primarily due to resource underutilization and the current execution model, which typically allows only one job per queue slot. Additionally, high costs are associated with the execution of each job. To address these limitations, this paper proposes a multi-batch quantum circuit scheduling approach aimed at increasing hardware utilization and reducing execution queues. To achieve this, different circuits are packed into various batches to be executed within a single job, where the results obtained in each batch are measured in distinct classical registers. These features allow for the reuse of previously utilized qubits in previous batches, enabling them to be used again for execution without the need to submit a new job. The approach has been validated on the 133-qubit IBM Torino processor through large-scale experiments with multiple circuits and controlled noise isolation tests. By reducing repeated queue interactions and job submission overhead, the proposed method increases execution throughput, allowing for numerous circuit executions within a single job. Under the evaluated conditions, this approach achieves significant execution cost reductions compared to traditional circuit submissions. Although the method incurs an overall absolute noise penalty, it allows packing numerous circuits into a single iteration, resulting in substantial financial savings. Furthermore, the noise level remains stable from the first to the last batch, showing no progressive degradation over time.
Rapid advancements in cloud based platforms providing access to quantum computing capabilities have opened up several challenges for efficient usage of these highly delicate and costly devices. Although most of the current systems use a priority based access protocol, they are unable to fully support reliable, efficient, and scalable execution of larger-scale applications. To overcome this limitation, we propose a comprehensive solution for efficient allocation of quantum programs to appropriate quantum devices, considering all the relevant cost metrics into account including, fidelity, execution time and communication overhead. We also formulate use-cases for distributed quantum workflow and propose modified graph based algorithms to solve for allocation of such use-cases, assuming a hybrid classical-quantum network. Since hardware advancements in large standalone devices is an ongoing process, it is critical to investigate such distributed workflows to maximize the best utilization of current NISQ devices. Our empirical study shows that the proposed techniques perform better than state-of-the-art methods for almost all evaluation parameters, with average improvements of approximately 5% in execution time, 30% in communication overhead, 40% in wait time and 2% in fidelity, providing better solutions to efficient allocation strategies.
Present-day quantum systems face critical bottlenecks, including limited qubit counts, brief coherence intervals, and high susceptibility to errors—all of which obstruct the execution of large and complex circuits. The advancement of quantum algorithms has outpaced the capabilities of existing quantum hardware, making it difficult to scale computations effectively. Additionally, inconsistencies in hardware performance and pervasive quantum noise undermine system stability and computational accuracy. To optimize quantum workloads under these constraints, strategic approaches to task scheduling and resource coordination are essential. One of the persistent challenges in this domain is how to efficiently divide and execute large circuits across multiple quantum processors (QPUs), especially in error-prone environments. In response, we introduce a simulation-based tool that supports distributed scheduling and concurrent execution of quantum jobs on networked QPUs connected via real-time classical channels. The tool models circuit decomposition for workloads that surpass individual QPU limits, allowing for parallel execution through inter-processor communication. Using this simulation environment, we compare four distinct scheduling techniques—among them, a model informed by reinforcement learning. These strategies are evaluated across multiple metrics, including runtime efficiency, fidelity preservation, and communication costs. Our analysis underscores the trade-offs inherent in each approach and highlights how parallelized, noise-aware scheduling can meaningfully improve computational throughput in distributed quantum infrastructures.
The rapid expansion of quantum cloud services has led to long job queues due to single-tenant execution models that underutilize hardware resources. Quantum multi-programming (QMP) mitigates this by executing multiple circuits in parallel on a single device, but existing methods target superconducting systems with limited connectivity, high crosstalk, and lower gate fidelity. Trapped-ion architectures, with all-to-all connectivity, long coherence times, and high-fidelity mid-circuit measurement properties, presents itself as a more suitable platform for scalable QMP. We present CircPack, a hardware-aware circuit packing framework designed for modular trapped-ion devices based on the Quantum Charge-Coupled Device (QCCD) architecture. CircPack formulates static circuit scheduling as a two-dimensional packing problem with hardware-specific shuttling constraints. Compared to superconducting-based QMP approaches, CircPack achieves up to 70.72% better fidelity, 62.67% higher utilization, and 32.80% improved layer reduction. This framework is also capable of scalable, balanced scheduling across a cluster of independent QCCD modules, highlighting trapped-ion systems' potential in improving the throughput of quantum cloud computing in the near future.
Current quantum computing platforms often exhibit suboptimal hardware utilization, with most circuits engaging only a fraction of the available qubits. This inefficiency inflates execution costs, extends queueing times, and hampers scalability, particularly in shared, cloud-based environments. This paper introduces a service-oriented architecture for the parallel execution of quantum circuits that jointly schedules and executes logically independent circuits within a single task. The service supports multiple configurable scheduling policies, based on criteria such as circuit depth, number of qubits, and number of shots, and ensures logical separation of circuits with accurate result demultiplexing. Extensive validation on real quantum hardware, including IBM and AWS processors, demonstrates significant improvements: task submission reductions of up to 95%, cost savings of up to 93%, and fidelity levels exceeding 97% even under high utilization. This solution offers a reusable, extensible approach that enhances resource efficiency and performance in Quantum Computing as a Service platforms.