An ML-Based Hybrid Task Scheduler for Classical–Quantum Computing Environments Using Real Graph-Derived Workloads
Peter Nimbe, Nicodemus Songose Awarayi, Vivian Akoto-Adjepong, Faiza Umar Bawah, Patrick Kwabena Mensah, Obed Appiah, Christopher Bombie Ninfaakang, Mighty Abra Ayidzoe, Augustine Takyi, Bernard Andoh, Daniel Asamoah Apraku, Frank Opoku Kwarteng, Farida Osman
Abstract Hybrid classical-quantum computing creates a scheduling challenge. Tasks need assignment over time and across different resources, each with unique execution, setup, and queue traits. This paper introduces a machine-learning-based scheduler for placing tasks between classical CPU and quantum processing resources in a simulated environment. We use the Wiki-Vote network from the Stanford Network Analysis Project as a workload source. We build a benchmark of connected Max-Cut task instances by sampling subgraphs of different sizes. Each task is profiled under both classical and quantum execution modes. The resulting dataset combines task features, runtime behaviour, and queue-state conditions. We evaluate multiple learning models for predicting task placement. Their scheduling decisions are compared to classical-only, quantum-only, and rule-based hybrid baselines. Results show that our ML-based scheduler outperforms all others. It achieves the lowest average completion time, smallest makespan, highest throughput, and best decision accuracy. Feature analysis reveals that queue pressure, system regime, and task priority are key factors in placement decisions. Graph and circuit complexity also enhance predictive power. These findings suggest that data-driven scheduling can boost resource efficiency in hybrid classical-quantum environments. This approach offers a practical path for intelligent task placement in emerging heterogeneous systems.
Abstract Hybrid classical-quantum computing creates a scheduling challenge. Tasks need assignment over time and across different resources, each with unique execution, setup, and queue traits. This paper introduces a machine-learning-based scheduler for placing tasks between classical CPU and quantum processing resources in a simulated environment. We use the Wiki-Vote network from the Stanford Network Analysis Project as a workload source. We build a benchmark of connected Max-Cut task instances by sampling subgraphs of different sizes. Each task is profiled under both classical and quantum execution modes. The resulting dataset combines task features, runtime behaviour, and queue-state conditions. We evaluate multiple learning models for predicting task placement. Their scheduling decisions are compared to classical-only, quantum-only, and rule-based hybrid baselines. Results show that our ML-based scheduler outperforms all others. It achieves the lowest average completion time, smallest makespan, highest throughput, and best decision accuracy. Feature analysis reveals that queue pressure, system regime, and task priority are key factors in placement decisions. Graph and circuit complexity also enhance predictive power. These findings suggest that data-driven scheduling can boost resource efficiency in hybrid classical-quantum environments. This approach offers a practical path for intelligent task placement in emerging heterogeneous systems.
Abstract This research presents a comprehensive hybrid quantum classical machine learning framework designed to overcome the computational limitations of classical algorithms when processing high dimensional data. The study integrates parameterized quantum circuits within classical neural network architectures and demonstrates their application across optimization tasks and supervised learning problems. The framework was evaluated on combinatorial optimization benchmarks including Max Cut and Job Shop Scheduling problems alongside classification tasks using quantum annealing based feature selection. Results demonstrate that hybrid quantum classical models achieve faster convergence rates and improved accuracy in high dimensional spaces compared to classical counterparts with the quantum algorithm for Max Cut optimization outperforming complete solution searches on larger problem instances. The findings provide machine learning practitioners and quantum computing researchers with validated hybrid architectures for near term quantum device deployment.
We present Qurator, a quantum-classical task scheduler that jointly optimizes queue time and circuit fidelity across heterogeneous architectures. Qurator models hybrid workloads as dynamic DAGs with explicit quantum semantics, including entanglement dependencies, synchronization barriers, no-cloning constraints, and circuit cutting and merging decisions. Fidelity is estimated through a unified logarithmic success score that reconciles incompatible calibration data from IBM, IonQ, IQM, Rigetti, AQT, Pasqal and other providers into a canonical set of gate error, readout fidelity, and decoherence terms. We evaluate Qurator on a simulator driven by four months of real queue data using circuits from the Munich Quantum Toolkit benchmark suite. Across load conditions from 5 to 35,000 quantum tasks with up to 120,000 classical tasks, Qurator stays within 1% of the highest-fidelity baseline at low load, achieves 30-75% queue time reduction at high load, all at a fidelity cost bounded by a user-specified target while improving on list and fair schedulers as well as state of the art schedulers on both metrics.
The rapid advancement of cloud computing has enabled large-scale deployment of machine learning applications; however, the emergence of Quantum Machine Learning (QML) introduces new challenges for cloud workload management. QML workloads exhibit unique execution patterns, hybrid quantum–classical behavior, and distinct resource requirements that are not effectively handled by existing cloud workload prediction and scheduling mechanisms. This paper presents a comprehensive review of recent research on cloud workload optimization, scheduling, and characterization, highlighting their limitations in supporting QML workloads. Based on the identified research gaps, a system model and methodology for QML workload prediction and categorization in cloud computing environments are discussed. The proposed approach emphasizes early workload analysis, feature-based prediction, and intelligent categorization to enable proactive scheduling and efficient resource utilization. Furthermore, key challenges related to quantum hardware limitations, hybrid integration complexity, and prediction reliability are analyzed. This study provides foundational insights for designing scalable and intelligent cloud platforms capable of supporting next-generation Quantum Machine Learning applications
Quantum Machine Learning (QML) has emerged as a transformative paradigm that integrates the computational advantages of quantum computing with the adaptive intelligence of machine learning to solve complex optimization problems in smart computing systems. Traditional optimization techniques often struggle with high-dimensional search spaces, combinatorial complexity, computational overhead, and scalability limitations in applications such as intelligent transportation, healthcare analytics, smart manufacturing, cybersecurity, cloud computing, and Internet of Things (IoT) infrastructures. QML approaches exploit quantum superposition, entanglement, and quantum parallelism to accelerate optimization tasks and enhance predictive performance beyond classical machine learning capabilities. This research proposes a hybrid Quantum Machine Learning optimization framework that combines Variational Quantum Circuits (VQCs), Quantum Neural Networks (QNNs), and classical deep learning models to address resource allocation, task scheduling, energy optimization, and intelligent decision-making in smart computing environments. The proposed framework integrates quantum-enhanced feature extraction, adaptive optimization layers, and reinforcement-based learning mechanisms to improve convergence speed and computational efficiency. Experimental analysis is performed using benchmark optimization datasets and simulated quantum environments to evaluate accuracy, execution time, energy efficiency, and scalability. Comparative results demonstrate that the proposed QML-based framework achieves superior optimization accuracy, reduced computational latency, and improved resource utilization when compared with traditional machine learning and classical metaheuristic optimization methods. Furthermore, the study highlights the practical significance of hybrid quantum-classical architectures in next-generation intelligent systems where large-scale optimization and real-time decision support are critical. The findings suggest that QML can significantly contribute to the advancement of sustainable, adaptive, and high-performance smart computing ecosystems while opening new directions for future quantum-aware intelligent infrastructures.
Hybrid quantum-classical applications pose significant resource management challenges due to heterogeneity and dynamism in both infrastructure and workloads. Quantum-HPC environments integrate quantum processing units (QPUs) with diverse classical resources (CPUs, GPUs), while applications span coupling patterns from tightly coupled execution to loosely coupled task parallelism with varying resource requirements. Traditional HPC schedulers lack visibility into application semantics and cannot respond to fluctuating resource availability at runtime. This paper presents a middleware-based approach for adaptive resource, workload, and task management in hybrid quantum-HPC systems. We make four contributions: (i) a conceptual four-layer middleware architecture that decomposes management across workflow, workload, task, and resource levels, enabling application-aware scheduling over heterogeneous quantum-HPC resources; (ii) a set of execution motifs capturing interaction and coupling characteristics of hybrid applications, realized as quantum mini-apps for systematic workload characterization; (iii) Pilot-Quantum, a middleware framework built on the pilot abstraction that enables late binding and dynamic resource allocation, adapting to resource and workload dynamics at runtime; and (iv) Q-Dreamer, a performance modeling toolkit providing reusable components for informed workload partitioning, including a circuit-cutting optimizer that analytically derives optimal partitioning strategies. Evaluation on heterogeneous HPC platforms (Perlmutter, NVIDIA DGX with H100/B200 GPUs) demonstrates efficient multi-backend orchestration across CPUs, GPUs, and QPUs for diverse execution motifs. Q-Dreamer predicts optimal circuit cutting configurations with up to 82% accuracy.
Abstract Integrating quantum network optimization with traditional cloud computing presents a groundbreaking approach to enhancing computational efficiency and resource management. This study investigates the quantifiable impact of quantum-classical hybrid systems in cloud environments, focusing on resource utilization enhancement and task scheduling optimization. Using a MATLAB-based simulation environment interfaced with Variational Quantum Eigensolver (VQE) simulations, this research implements a three-layer architecture across 50 nodes, combining quantum optimization algorithms with classical scheduling techniques through a GPS queue model. The hybrid quantum-classical framework achieves remarkable improvements, including a 180% increase in task throughput, 70% reduction in response time, 41.5% improvement in resource utilization, and 27.1% enhancement in energy efficiency while maintaining 85% efficiency under peak load conditions and demonstrating 99.5% success rate for short-term tasks and 97.8% for long-running operations. Despite hardware limitations of existing quantum processors and scalability constraints, the system establishes a foundation for future developments, including advanced error correction mechanisms, expanded system scalability beyond 50 nodes, and enhanced resource utilization. These findings establish quantum-enhanced cloud computing as a viable solution for next-generation cloud infrastructure, particularly in managing complex workloads and optimizing resource allocation.