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CompSci & AI Advances

Hybrid Quantum–Classical Optimization for Cloud Resource Allocation: A Scalable Framework for Energy–Efficient Computing

K. Kalpana, S. Kavitha, S. Chinnapparaj, V. V. Teresa, C. Sincija, Guangda Liu

Published Mar 1, 2025• 3 citations
Publisher

Abstract

Cloud computing has transformed modern digital infrastructure by enabling on-demand access to scalable computational resources. However, the increasing complexity of dynamic workloads and the need for efficient resource allocation present persistent challenges in maintaining performance, cost efficiency, and energy sustainability. This paper introduces a Quantum-Driven Optimization (QDO) framework, a hybrid quantum-classical approach designed to enhance cloud resource allocation by integrating quantum computing techniques with classical optimization methods. The proposed framework leverages Quantum Annealing (QA) and the Variational Quantum Eigensolver (VQE) to optimize resource distribution, minimizing energy consumption and operational costs while maximizing throughput and utilization efficiency. Experimental evaluations demonstrate that the QDO framework achieves a 27% improvement in resource utilization, a 34% reduction in operational costs, and a 21% enhancement in task completion time compared to traditional heuristic-based approaches. Additionally, the hybrid model reduces Service Level Agreement (SLA) violations by 18%, ensuring robust Quality of Service (QoS) for cloud users. The framework employs classical algorithms for preprocessing and decision-making while delegating complex optimization tasks to quantum solvers, ensuring scalability across diverse cloud environments. This study highlights the transformative potential of hybrid quantum-classical computing in addressing cloud resource allocation challenges. The results indicate significant improvements in energy efficiency, cost-effectiveness, and system responsiveness, making the QDO framework a viable solution for next-generation cloud infrastructures. Future research directions include extending the framework to multi-cloud architectures and investigating advanced quantum algorithms for further optimization gains.

DOI
10.69626/cai.2025.0001

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Cloud computing has fundamentally transformed digital infrastructure by providing on-demand access to scalable computational resources. Yet the inherent unpredictability of workloads alongside surging demands for efficient resource distribution continues to present substantial operational hurdles. This investigation introduces a Quantum-Driven Optimization framework that synergistically combines quantum and classical computational paradigms to improve resource allocation efficacy within cloud ecosystems. The proposed methodology employs Quantum Annealing alongside Variational Quantum Eigensolver algorithms to optimize resource distribution through simultaneous minimization of energy expenditure and operational overhead while maximizing throughput and utilization rates. Empirical validation demonstrates that our framework achieves measurable improvements over conventional heuristic methodologies, specifically showing enhanced resource utilization, reduced operational costs, and accelerated task completion times. The hybrid architecture effectively distributes computational burden by delegating preprocessing and decision-making functions to classical systems while quantum processors tackle inherently complex optimization problems. The solution demonstrates scalability and adaptability across diverse cloud deployment scenarios while exhibiting reduced Service Level Agreement violations, thereby ensuring superior Quality of Service for end-users. This research contributes meaningful insights regarding the practical implementation of hybrid quantum-classical approaches for addressing contemporary resource allocation challenges within cloud computing infrastructures.
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.
Task scheduling with the cloud computing domain constitutes a significant challenge, especially when the objective is to improve energy efficiency without detracting from performance metrics. Traditional optimization methodologies, including Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and Ant Colony Optimization (ACO), frequently encounter difficulties related to scalability and convergence velocity in expansive, dynamic cloud ecosystems. This manuscript introduces a quantum-assisted hybrid paradigm that amalgamates the Quantum Approximate Optimization Algorithm (QAOA) with the Variational Quantum Eigensolver (VQE) to facilitate energyefficient task scheduling within cloud computing platforms. The QAOA is utilized to investigate near-optimal mappings of tasks to resources by addressing combinatorial scheduling dilemmas, whereas the VQE refines the energy consumption model via variational circuit optimization techniques. Compared to classical metaheuristic approaches, the proposed hybrid framework exhibits enhanced efficiency in minimizing energy consumption by 23.6 %, decreasing the makespan by 17.4 %, and increasing resource utilization by 17.7 %. Empirical findings derived from a quantum simulator also indicate a 36.6 % acceleration in convergence relative to GA and a 10.3% improvement in solution accuracy when juxtaposed with PSO. These findings substantiate the premise that quantum-assisted hybrid optimization presents a viable path towards the realization of sustainable and highperformance cloud computing infrastructures.
Quantum-inspired optimization emerges as a transformative paradigm for cloud infrastructure management, addressing the increasing complexity and multi-dimensional challenges faced by modern distributed systems. This article introduces a comprehensive framework that applies quantum computational principles to classical infrastructure, enabling more efficient navigation of complex solution landscapes without requiring quantum hardware. The framework targets critical operational areas including workload distribution, auto-scaling, resource allocation, and fault tolerance enhancement. By leveraging quantum-inspired algorithmic approaches such as quantum annealing simulations and hybrid methods, the solution demonstrates significant advantages over conventional optimization techniques, particularly as infrastructure complexity increases. The implementation incorporates seamless integration with existing cloud platforms through non-invasive APIs and abstraction layers. Experimental results reveal improved resource utilization, enhanced reliability during failure scenarios, substantial cost reductions, and favorable scalability characteristics. The quantum-inspired approach offers a practical pathway toward addressing the exponentially growing complexity of cloud infrastructure optimization while providing immediate benefits using classical computing resources and establishing foundations for future integration with quantum computing services.
The exponential growth of data-driven systems has outpaced the computational capacity of conventional cloud infrastructures, demanding new paradigms of intelligence and optimization. This research introduces a quantum-enabled cloud intelligence framework that integrates post-classical optimization methods with data-centric artificial intelligence pipelines. The study investigates how quantum-inspired and hybrid quantum–classical models can enhance large-scale optimization, feature extraction, and decision processes within distributed cloud environments. By coupling quantum approximate optimization algorithms (QAOA) and variational quantum circuits (VQC) with classical data analytics layers, the framework achieves reduced computational latency, improved convergence for high-dimensional problems, and enhanced interpretability of AI-driven outcomes. A prototype quantum-cloud orchestration layer is developed to dynamically allocate quantum resources through cloud APIs, enabling adaptive workload balancing across heterogeneous architectures. Empirical results from simulation and hybrid execution demonstrate significant performance gains over classical baselines in terms of both efficiency and solution quality. Beyond performance metrics, the study emphasizes ethical and sustainable computing principles, ensuring that quantum acceleration aligns with transparent, explainable, and accountable AI operations. The outcomes position quantum-enabled cloud intelligence as a foundational technology for the next generation of data-centric optimization and cognitive automation.
Quantum computing emerges as a revolutionary force in distributed systems optimization, fundamentally transforming resource allocation and system management paradigms. The integration of quantum algorithms with classical infrastructure introduces unprecedented capabilities in addressing complex optimization challenges in microservice architectures. Through quantum-enhanced protocols and hybrid quantum-classical systems, distributed computing achieves remarkable improvements in efficiency, scalability, and performance. The combination of Quantum Approximate Optimization Algorithm (QAOA) and Variational Quantum Eigensolver (VQE) with traditional computing frameworks enables superior resource management, enhanced decision-making capabilities, and optimized service mesh configurations. This convergence of quantum and classical computing paradigms paves the way for next-generation distributed systems that can handle increasingly complex optimization challenges while maintaining operational efficiency.