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.
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.
Quantum computing holds significant potential across various fields such as cybersecurity, finance, and health-care, among many others. However, we currently face multiple limitations such as the low availability of computers, the underutilization of available qubits, and the ever-increasing number of users, which results in prolonged waiting times. This, combined with the cost of executions, limits access to quantum computing. To address these challenges, we propose a scheduling process to optimize task execution on quantum computers. The proposed scheduling process unifies multiple circuits to enhance the efficiency of resource utilization in quantum machines. This approach allows for better use of available qubits and reduces the associated execution costs. We validate the proposed method employing various quantum algorithms by systematically examining the scheduling process through a comparative case study between two quantum service providers: IBM Quantum and Amazon Braket. This validation has shown that the scheduling process does not generate a significant impact on the results, ensuring similar results to single circuit executions with a fraction of the cost.
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.
Context: Quantum computing is rapidly evolving, offering new opportunities for solving problems in optimization, cryptography, and simulation. However, the limited availability of quantum resources makes efficient utilization of quantum hardware a current challenge. Today’s paradigms often lead to under-utilization of qubits, increased costs, and execution delays, especially in the NISQ era. Objective: This work aims to improve the utilization of quantum hardware by introducing an execution model that integrates multiprogramming at circuit level with quantum shot-wise distribution in a single policy-driven pipeline. Methods: An architecture has been implemented that combines circuit scheduling and shot distribution techniques to aggregate multiple circuits and distribute their shots across heterogeneous QPUs. The approach was empirically validated on actual IBM Quantum devices using a diverse set of reference circuits. Results: The proposal achieved a reduction in cost of 95% and a reduction in tasks 92%. Moreover, the fidelity analysis of the results showed an increase in noise, with an average increase of approximately 20% using different statistical distances. Conclusions: This research provides a usable and extensible solution to increase the efficiency, cost effectiveness, and resilience of quantum workload execution in heterogeneous and dynamic cloud environments. These results obtained suggest that users should weigh the implications of fidelity versus cost (and time) savings based on the application requirements and their goals.
Progress in the realm of quantum technologies is paving the way for a multitude of potential applications across different sectors. However, the reduced number of available quantum computers, their technical limitations and the high demand for their use are posing some problems for developers and researchers. Mainly, users trying to execute quantum circuits on these devices are usually facing long waiting times in the tasks queues. In this context, this work propose a technique to reduce waiting times and optimize quantum computers usage by scheduling circuits from different users into combined circuits that are executed at the same time. To validate this proposal, different widely known quantum algorithms have been selected and executed in combined circuits. The obtained results are then compared with the results of executing the same algorithms in an isolated way. This allowed us to measure the impact of the use of the scheduler. Among the obtained results, it has been possible to verify that the noise suffered by executing a combination of circuits through the proposed scheduler does not critically affect the outcomes.
Currently, quantum processors offered by quantum service providers are significantly underutilized, resulting in high execution costs and limited scalability. Various approaches have explored parallelization and circuit scheduling techniques to improve resource utilization. While these methods can significantly reduce both cost and execution times, they often have an impact on the results' noise. Addressing this challenge, this emerging study proposes a technique called Quantum Island Mapping, which enables the execution of multiple quantum circuits by separating them into “islands” within the qubit topology of a processor. The goal is to schedule multiple circuits into a single task to reduce execution costs and queuing, while maintaining a controlled separation of qubits between circuits to minimize interferences. To achieve this, separation distance parameters are defined, specifying the minimum number of qubits between different islands. This allows for configurations ranging from zero separation (maximum processor utilization) to maximum separation (single-circuit execution). The technique has been validated through experiments on a real quantum computer using well-known quantum algorithms, achieving optimal results.
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.