Task scheduling is a critical problem within the modern underlying computing infrastructure, where resource overload causes a direct impact on the cost and time performance metrics, leading to increased latency, excessive financial cost, and overall poor throughput. Traditional scheduling algorithms are normally fixed and lack the strong exploration and exploitation processes, hence limiting their effectiveness when faced with large problems in scheduling, which are NP-hard. The current work investigates Quantum Computing (QC)-algorithms including Grover, the Variational Quantum Eigensolver (VQE) and Quantum Annealing (QA). This research work includes an extensive experiment conducted in the WorkflowSim simulator with heterogeneous task-scheduling performed with diverse Virtual Machine (VM) configurations. The performance-metrics considered for their comparison include cost and time, along with Relative Performance Index (RPI). The experimental results indicate that QC-algorithm QA achieves higher cost-efficiency, with an average cost reduction of 4 % compared to Grover and 25% compared to VQE, with a corresponding average reduction in time of about 14 % compared to Grover. VQE provides competitive behavior with particular VM configurations but provides poor stability behavior with a wider range of parameters. Statistical evaluations using five regression provides the Grover algorithm to have a superior predictive stability and stronger goodness-of-fit concerning cost and time compared to QA and VQE. Future research will be directed towards the design of hybrid quantum-classical scheduling systems and inclusion of additional performance indicators (including energy efficiency and throughput) and testing the mentioned systems on real quantum equipment to evaluate scalability and real-world feasibility in computational environments.
Task scheduling is a critical problem within the modern underlying computing infrastructure, where resource overload causes a direct impact on the cost and time performance metrics, leading to increased latency, excessive financial cost, and overall poor throughput. Traditional scheduling algorithms are normally fixed and lack the strong exploration and exploitation processes, hence limiting their effectiveness when faced with large problems in scheduling, which are NP-hard. The current work investigates Quantum Computing (QC)-algorithms including Grover, the Variational Quantum Eigensolver (VQE) and Quantum Annealing (QA). This research work includes an extensive experiment conducted in the WorkflowSim simulator with heterogeneous task-scheduling performed with diverse Virtual Machine (VM) configurations. The performance-metrics considered for their comparison include cost and time, along with Relative Performance Index (RPI). The experimental results indicate that QC-algorithm QA achieves higher cost-efficiency, with an average cost reduction of 4 % compared to Grover and 25% compared to VQE, with a corresponding average reduction in time of about 14 % compared to Grover. VQE provides competitive behavior with particular VM configurations but provides poor stability behavior with a wider range of parameters. Statistical evaluations using five regression provides the Grover algorithm to have a superior predictive stability and stronger goodness-of-fit concerning cost and time compared to QA and VQE. Future research will be directed towards the design of hybrid quantum-classical scheduling systems and inclusion of additional performance indicators (including energy efficiency and throughput) and testing the mentioned systems on real quantum equipment to evaluate scalability and real-world feasibility in computational environments.
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.
In this work, we study the application of quantum computing to complex engineering simulations and decisions. Using these quantum algorithms like Quantum Approximate Optimization Algorithm (QAOA), Variational Quantum Eigensolver (VQE), Quantum Support Vector Machine (QSVM) and Grover’s Algorithm, we will optimize computational efficiency, accuracy and diversity of solutions in engineering. It was performed with a comparative analysis with classical algorithms based on performance metrics such as accuracy of solution, execution time, and optimality of the decision. Analysis of experimental results showed QAOA produces a 92.3% solution accuracy, higher than that (88.6%) of classical simulated annealing and (85.4%) genetic algorithms. Similarly, VQE achieved 27% reduction in execution time versus the classical equivalent and QSVM achieved 94.1% classification accuracy on engineering pattern recognition tasks. Grover’s Algorithm also resulted in a 3.7x faster search efficiency in decision oriented simulation. Finally, the study shows that quantum inspired models can perform efficiently on large scale and highly complex engineering problems. These findings provide a major leap in realizing the capability to harness quantum technologies for real time, scalable decision making in structural analysis, lifecycle management and intelligent system design. Such results make way for future developments of hybrid quantum classical computing frameworks that could be deployed in industry.
Quantum computing has emerged as a transformative paradigm, leveraging principles of quantum mechanics to tackle computationally intractable problems. This manuscript explores the potential of quantum computing for solving complex engineering problems—specifically optimization, simulation, and data analysis tasks—using technologies and methodologies available up to 2022. We examine both gate-based and quantum annealing approaches, outline experimental frameworks, and compare performance against classical counterparts. Results from simulated benchmark problems demonstrate promising speedups in select cases, though practical adoption is constrained by hardware limitations such as qubit coherence times and noise. The study concludes by highlighting avenues for integrating hybrid quantum-classical workflows in engineering, along with a discussion of current constraints and future directions. Over the past decade, research efforts have focused on understanding how quantum phenomena like superposition and entanglement can be harnessed to realize computational advantages for specific problem classes. In particular, combinatorial optimization problems—ubiquitous in engineering design and logistics—offer fertile ground for quantum algorithms such as the Quantum Approximate Optimization Algorithm (QAOA) and quantum annealing. Additionally, quantum simulation techniques, including the Variational Quantum Eigensolver (VQE), promise to accelerate the modeling of physical systems at the molecular and materials level, potentially informing the design of next-generation structural and functional materials. Beyond raw algorithmic performance, this paper carefully assesses the end‑to‑end workflow, accounting for overheads in problem encoding, error mitigation, and classical post‑processing. We also present a detailed analysis of resource utilization—quantified by qubit counts, circuit depths, and annealing schedules—highlighting trade‑offs between solution quality and execution time. Ultimately, our findings underscore that, while pure quantum speedups remain rare in 2022, hybrid approaches harnessing both quantum and classical resources can yield practical benefits for small‑to‑medium scale engineering tasks. This expanded evaluation provides a roadmap for researchers and practitioners interested in applying quantum computing to real‑world engineering challenges.
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.
Task scheduling is one of the crucial agendas in cloud computing technology. The system consists of various tasks namely termed cloudlets which need to be fed among various processing units for its execution. However, the algorithm that has been used to schedule tasks among various processing units should provide the optimum solution to a given problem. This research realizes the power of quantum computation specifically quantum annealing to provide the optimum solution to schedule the tasks among various processing units under the given constraints. Quantum Annealing is used for combinatorial optimization problems and as task scheduling is also one of such problems, it can be done in the quantum environment for optimal results. The Hybrid Solver provided by the D-Wave Quantum Computer has been used to solve the given problem.Leveraging the D-Wave Quantum Annealer, we analyze various scheduling scenarios and evaluate the performance in terms of makespan and throughput. Our findings reveal substantial improvements in scheduling efficiency compared to Partical Swarm Optimization scheduling. Specifically, we observed a notable reduction in makespan by up to 70% across diverse workload scenarios. Moreover, there was not much difference in throughput provided by both algorithms. The task scheduling among various processing units using quantum annealing has provided significant differences in makespan. Thus, using an annealing technique to schedule the tasks in cloud computing would significantly enhance in load balancing and efficient use of resources.
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.