Quantum computing has gained considerable attention, especially after the arrival of the Noisy Intermediate-Scale Quantum (NISQ) era. Quantum processors and cloud services have been made worldwide increasingly available. Unfortunately, jobs on existing quantum processors are often executed in series, and the workload could be heavy to the processor. Typically, one has to wait for hours or even longer to obtain the result of a single quantum job on public quantum cloud due to long queue time. In fact, as the scale grows, the qubit utilization rate of the serial execution mode will further diminish, causing the waste of quantum resources. In this paper, to our best knowledge for the first time, the Quantum Job Scheduling Problem (QJSP) is formulated and introduced, and we accordingly aim to improve the utility efficiency of quantum resources. Specifically, a noise-aware quantum job scheduler (NAQJS) concerning the circuit width, number of measurement shots, and submission time of quantum jobs is proposed to reduce the execution latency. We conduct extensive experiments on a simulated Qiskit noise model, as well as on the Xiaohong (from QuantumCTek) superconducting quantum processor. Numerical results show the effectiveness in both the QPU time and turnaround time.
Quantum computing has gained considerable attention, especially after the arrival of the Noisy Intermediate-Scale Quantum (NISQ) era. Quantum processors and cloud services have been made worldwide increasingly available. Unfortunately, jobs on existing quantum processors are often executed in series, and the workload could be heavy to the processor. Typically, one has to wait for hours or even longer to obtain the result of a single quantum job on public quantum cloud due to long queue time. In fact, as the scale grows, the qubit utilization rate of the serial execution mode will further diminish, causing the waste of quantum resources. In this paper, to our best knowledge for the first time, the Quantum Job Scheduling Problem (QJSP) is formulated and introduced, and we accordingly aim to improve the utility efficiency of quantum resources. Specifically, a noise-aware quantum job scheduler (NAQJS) concerning the circuit width, number of measurement shots, and submission time of quantum jobs is proposed to reduce the execution latency. We conduct extensive experiments on a simulated Qiskit noise model, as well as on the Xiaohong (from QuantumCTek) superconducting quantum processor. Numerical results show the effectiveness in both the QPU time and turnaround time.
Abstract With the progression into the quantum utility era, computing is shifting toward quantum-centric architectures, where multiple quantum processors collaborate with classical computing resources. Platforms such as IBM Quantum and Amazon Braket exemplify this trend, enabling access to diverse quantum backends. However, efficient resource management remains a challenge, as quantum processors are highly susceptible to noise, which significantly impacts computation fidelity. Additionally, the heterogeneous noise characteristics across different processors add further complexity to scheduling and resource allocation. Existing scheduling strategies typically focus on mapping and scheduling jobs to these heterogeneous backends, which leads to some jobs suffering extremely low fidelity. Targeting quantum optimization jobs (e.g., VQC, VQE, QAOA) — among the most promising quantum applications in the NISQ era — we hypothesize that executing the later stages of a job on a high-fidelity quantum processor can significantly improve overall fidelity. To verify this, we use VQE as a case study and develop a Genetic Algorithm-based scheduling framework that incorporates job splitting to optimize fidelity and throughput. Experimental results demonstrate that our approach consistently maintains high fidelity across all jobs while significantly enhancing system throughput. Furthermore, the proposed algorithm exhibits excellent scalability in handling an increasing number of quantum processors and larger workloads, making it a robust and practical solution for emerging quantum computing platforms. To further substantiate its effectiveness, we conduct experiments on a real quantum processor, IBM Strasbourg, which confirm that job splitting improves fidelity and reduces the number of iterations required for convergence.
The current execution model of quantum computers, based on serialized task processing, leads to inefficient resource utilization and increased waiting times. This inefficiency persists even as the demand for quantum computing grows. To address this, an automated scheduling approach is proposed through the AutoScheduler library, which replicates quantum circuits to fully occupy the available qubits in a quantum processor. This strategy reduces the number of required shots per execution while optimizing hardware usage. Experimental validation on IBM Quantum and Amazon Braket platforms demonstrates that autoscheduling achieves substantial reductions in execution time and cost, with minimal impact on result fidelity despite inherent quantum noise. These findings suggest that circuit autoscheduling is a viable method for improving the scalability and efficiency of quantum computing in the NISQ era.
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.
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.
Emerging quantum networks enable distributed quantum computing (DQC) by interconnecting modest, noisy quantum processors. However, practical deployments face tight memory budgets, probabilistic entanglement generation, and decoherence, all while serving multiple users. In realistic settings -- cloud execution, iterative/interactive workloads and concurrent jobs -- circuits do not arrive in a single batch but as a stream. This creates a fundamentally online problem: the system must react to stochastic arrivals, balance latency and throughput, and remain stable under varying loads without foresight of future jobs.
We study Online Scheduling and Distribution of Quantum Circuits (OS-DQC) and formalize the online scheduling problem for quantum networks: circuits arrive over time and must be executed without knowledge of future arrivals. At each event time (arrival or completion), the system decides which waiting circuits to launch and how to place their qubits, while accounting for congestion from in-flight circuits and constraints due to finite memories and decoherence. The objective is to minimize the mean completion time and makespan under these physical and resource constraints.
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.