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.
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 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.
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.
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.
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.
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.
The integration of distributed energy resources into power networks is accelerating. The resulting variability narrows operating margins, so a disturbance can cascade into a wide-area blackout. Controlled islanding arrests that propagation by splitting a compromised grid into self-sustaining islands that keep coherent generators together. Exact classical solutions become intractable as the bus count and island number grow. Gate-based quantum optimization provides a different route through this combinatorial space, although its reach is limited when one circuit carries every bus assignment, since qubit count and depth then follow grid size. In this study, a round-synchronous distributed quantum computing framework is developed for coherent controlled islanding under a fixed per-circuit qubit budget. Every round derives all regional subproblems from one frozen grid-wide snapshot, dispatches them to independent quantum backends at the same time, and merges the returned candidates classically into one globally evaluated update. Circuits executed in parallel therefore keep a constant size as the grid grows, and a round costs the slowest region rather than the sum of all of them. Benchmarking spans IEEE systems from 9 to 300 buses on simulation and on quantum processors of different architectures. The framework attains optimal and operationally feasible partitions on every platform under noise, even where compilation cost differs by nearly an order of magnitude. Bounding width in this way places grids beyond the reach of monolithic circuits within range of present devices and establishes a multi-backend baseline for quantum computing in large-scale power-system optimization.