Quantum computing, as an emerging computational paradigm, has garnered significant attention due to its potential advantages in combinatorial optimization and quantum chemistry problems. A hybrid quantum-classical model that integrates VQE and QAOA for efficient task assignment and path optimization is suggested. However, constrained by hardware noise, qubit limitations and parameter optimization bottlenecks, these algorithms still face challenges such as convergence instability, gradient vanishing and low ground state probability in large-scale problems. This paper proposes a hybrid optimization framework that integrates improved VQE and QAOA, introducing adaptive parameter initialization and multi-level parameter scheduling mechanisms into VQE. In contrast, QAOA combines an improved classical optimizer and parameter perturbation strategy, thereby enhancing the algorithm’s global convergence capability in the search space. Experiments on combined optimization examples with 6, 9, 10 and 12 qubits demonstrate that this method can significantly improve ground state probability and convergence stability under limited hardware conditions. The results validate the effectiveness of the proposed framework in complex scheduling and resource allocation tasks, showcasing its potential applications in the practical implementation of quantum optimization.
Quantum computing, as an emerging computational paradigm, has garnered significant attention due to its potential advantages in combinatorial optimization and quantum chemistry problems. A hybrid quantum-classical model that integrates VQE and QAOA for efficient task assignment and path optimization is suggested. However, constrained by hardware noise, qubit limitations and parameter optimization bottlenecks, these algorithms still face challenges such as convergence instability, gradient vanishing and low ground state probability in large-scale problems. This paper proposes a hybrid optimization framework that integrates improved VQE and QAOA, introducing adaptive parameter initialization and multi-level parameter scheduling mechanisms into VQE. In contrast, QAOA combines an improved classical optimizer and parameter perturbation strategy, thereby enhancing the algorithm’s global convergence capability in the search space. Experiments on combined optimization examples with 6, 9, 10 and 12 qubits demonstrate that this method can significantly improve ground state probability and convergence stability under limited hardware conditions. The results validate the effectiveness of the proposed framework in complex scheduling and resource allocation tasks, showcasing its potential applications in the practical implementation of quantum optimization.
Quantum computing and artificial intelligence (AI) are positioned as groundbreaking tools capable of tackling complexities inherent to modern computation posed by high-dimensional optimization problems.This research presents a quantum-enhanced optimization framework that leverages the QAOA (Quantum Approximate Optimization Algorithm) and VQE (Variational Quantum Eigen solver) within a hybrid quantum-classical paradigm.Machine learning models, including reinforcement learning and NN (neural networks), are integrated to optimize the quantum circuit parameters, enhance convergence, and improve scalability for complex problem spaces.Experimental evaluations demonstrate that this approach outperforms traditional optimization techniques in terms of efficiency and solution accuracy, particularly for large-scale multi-objective tasks.The framework is applied to key domains such as smart energy systems, logistics, and scientific modeling, illustrating its versatility and alignment with sustainable development goals.This study also explores the limitations of current quantum hardware and discusses future pathways for advancing hybrid quantum AI systems, offering significant contributions to next generation computational methodologies and intelligent problem solving.
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 simulation offers a novel computational paradigm for addressing the increasing complexity of modern manufacturing systems. As production lines evolve into highly interconnected, data-rich environments, conventional simulation and optimization techniques often struggle to capture nonlinear interactions, dynamic bottlenecks, and stochastic fluctuations in real time. This study proposes a hybrid framework that integrates quantum-inspired state encoding, variational quantum optimization, and classical discrete-event simulation to model and predict bottleneck behavior with enhanced efficiency. By leveraging quantum subroutines—particularly Variational Quantum Eigensolvers (VQEs) and Quantum Approximate Optimization Algorithms (QAOA)—the framework accelerates combinatorial scheduling searches and dynamic resource allocation tasks. Classical processors, in turn, handle high-fidelity system modeling and domain-specific constraints. Preliminary experimental simulations demonstrate that the hybrid approach can identify emergent bottlenecks earlier than purely classical methods and improve process throughput by up to 18% in benchmark scenarios. These results highlight the transformative potential of hybrid quantum–classical systems for next-generation smart factories, enabling more resilient, adaptive, and energy-efficient manufacturing operations.
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 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.
Quantum computation injection into robot automation opens up new avenues for boosting multi-robot complicated task scheduling and real-time adaptive control. Conventional optimization methods barely keep up with the challenges of scalability and efficiency in dynamic environments, thus becoming one of the bottlenecks for successful robotic coordination. This paper puts forward two leading quantum algorithms, Quantum Approximate Optimization Algorithm (QAOA) and Variational Quantum Eigen solver (VQE), as a remedy to these shortcomings. Through the use of quantum parallelism and hybrid quantum-classical architectures, the intended solution is to elevate task assignment effectiveness and interactive response capacity in multi-robotic systems. Among other things, the proposed work aims at setting up a quantum inspired optimization model to schedule a task, an adaptive and real-time decision-support control framework, and the experimental demonstration of the algorithm's capability to handle complexity and high volume in robotic environments. Computer-based simulations alongside actual experiments, as a matter of fact, will be able to outperform regular optimizations, hence proving the transformative potentials of quantum algorithms on robotic automation.