Quantum cloud platforms, which rely on Noisy Intermediate-Scale Quantum (NISQ) devices, face significant challenges in efficiently managing quantum programs. This paper proposes a QPU Scheduling and Resource Allocation (QSRA) approach to address these challenges. QSRA enhances qubit utilization and reduces turnaround time by adapting CPU scheduling techniques to Quantum Processing Units (QPUs). It incorporates a subroutine for qubit allocation that takes into account qubit quality and connectivity, while also merging multiple quantum programs to further optimize qubit usage. Our evaluation of QSRA against existing methods demonstrates its effectiveness in improving both qubit utilization and turnaround time.
Quantum cloud platforms, which rely on Noisy Intermediate-Scale Quantum (NISQ) devices, face significant challenges in efficiently managing quantum programs. This paper proposes a QPU Scheduling and Resource Allocation (QSRA) approach to address these challenges. QSRA enhances qubit utilization and reduces turnaround time by adapting CPU scheduling techniques to Quantum Processing Units (QPUs). It incorporates a subroutine for qubit allocation that takes into account qubit quality and connectivity, while also merging multiple quantum programs to further optimize qubit usage. Our evaluation of QSRA against existing methods demonstrates its effectiveness in improving both qubit utilization 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.
The development of quantum computing relies heavily on the effective execution of quantum algorithms. However, current Noisy Intermediate-Scale Quantum (NISQ) computers are plagued by limitations such as short coherence times and significant error rates, posing substantial challenges to scheduling and execution. This paper introduces a novel dynamic resource-aware scheduling algorithm designed to mitigate these challenges. The core of the algorithm lies in its ability to adapt to the fluctuating noise levels and coherence times of the quantum hardware, intelligently prioritizing tasks based on their estimated computational cost and error probability. We demonstrate that this adaptive approach significantly improves the potential for successful quantum algorithm execution on NISQ devices, offering a pathway towards more efficient and reliable quantum computation. The algorithm's effectiveness is evaluated through theoretical analysis and conceptual modeling, focusing on the optimization of resource allocation and task prioritization within the constraints of NISQ technology. This research contributes to the growing field of quantum resource management and scheduling, providing a framework for tackling the complexities inherent in operating quantum computers.
Distributed quantum computing (DQC) has emerged as a promising approach to overcome the scalability limitations of monolithic quantum processors in terms of computational capability. However, realising the full potential of DQC requires effective resource management and circuit scheduling. This involves efficiently assigning each circuit to a subset of quantum processing units (QPUs), based on factors such as their computational power and connectivity. In heterogeneous DQC networks with arbitrary connectivity topologies and non-identical QPUs, this becomes a complex challenge. This paper addresses resource management and circuit scheduling in such settings, with a focus on computing resource allocation in a quantum data centre. We propose circuit scheduling algorithms based on Mixed-Integer Linear Programming (MILP). Our MILP model accounts for errors arising from inter-QPU communication. In particular, the proposed schemes consider key factors, including network topology, QPU capacities, and quantum circuit structure, to make efficient scheduling and allocation decisions. Simulation results demonstrate that our proposed algorithms significantly improve circuit execution time and scheduling efficiency (measured by makespan and throughput), while also reducing inter-QPU communication overhead, compared to baseline strategies. This work provides valuable insights into resource management strategies for scalable and heterogeneous DQC systems.
As quantum computing (QC) technologies mature, their integration into established high-performance computing (HPC) infrastructures is becoming a central objective for next-generation computing systems. However, unlocking the potential of hybrid platforms for computationally demanding workloads remains challenging. The mismatch between quantum and classical programming models, the limited maturity of quantum software stacks, and the scarcity of quantum processing units (QPUs) above all, necessitate scheduling strategies that go beyond standard HPC mechanisms to manage such heterogeneous and constrained resources.
To address this issue, we investigate three distinct methodologies for HPC-QC resource scheduling: time-based multiplexing, dynamic resource management, and workflow decomposition. Experimental validation on production HPC clusters and real quantum hardware demonstrates the effectiveness of these approaches under different workload scenarios. Malleability and workflow strategies significantly optimize classical resource utilization, reducing consumption by up to 45.7% and 64% respectively, proving to be best fitted for hybrid jobs where quantum and classical workloads are evenly balanced. Conversely, time-multiplexing enhances QPU utilization and reduces execution time at the cluster level, making it the optimal strategy for the opposite context, which is characterized by high classical-quantum workload imbalances. These findings underscore the practical viability of tailored scheduling strategies for hybrid HPC-QC environments and highlight their complementarity in building efficient, scalable software stacks for next-generation quantum-accelerated facilities.
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.
This paper presents a novel approach to resource allocation and scheduling within quantum computing environments. Recognizing the inherent scarcity and unique characteristics of quantum resources, such as qubits and quantum gates, we propose a dynamic algorithm designed to optimize resource utilization and consequently enhance computational efficiency. The core of the algorithm lies in a mechanism that intelligently balances the demands of diverse quantum computing tasks. We formalize this approach using mathematical notation, detailing the key components including task prioritization, qubit assignment, gate scheduling, and dynamic adjustment based on real-time system metrics. The proposed algorithm addresses limitations within existing static scheduling methods, offering improved performance in complex quantum workflows. The design incorporates a queuing theory-inspired model to manage task flow and a stochastic optimization technique for adaptive resource allocation. Simulation results (presented conceptually here) demonstrate the algorithm's potential to significantly improve quantum computing performance compared to traditional approaches. Further research will focus on formal verification and optimization for specific quantum computing architectures.