Abstract Optimization problems are critical across various domains, yet existing quantum algorithms, despite their great potential, struggle with scalability and accuracy due to excessive reliance on entanglement. To address these limitations, we propose a variational quantum optimization algorithm (VQOA), which employs an ansatz based solely on quantum superposition, where single-qubit rotation gates function analogously to neurons in classical deep neural networks. This ansatz, which can be regarded as quantum neural networks, significantly reduces circuit complexity, enhances noise robustness, mitigates Barren Plateau issues, and enables efficient partitioning for highly complex, large-scale optimization. Furthermore, we introduce distributed VQOA (DVQOA), which integrates high-performance computing with quantum computing to achieve superior performance. These features enable a significant acceleration of material optimization tasks (e.g., metamaterial design), achieving more than 50 × speedup compared to state-of-the-art optimization algorithms. Beyond material design, DVQOA efficiently solves quantum chemistry problems and N -ary ( N ≥2) optimization problems involving higher-order interactions, outperforming classical deep neural networks. These advantages establish DVQOA as a highly promising and versatile solver for real-world problems, demonstrating the practical benefits of the quantum-classical approach.
Abstract Optimization problems are critical across various domains, yet existing quantum algorithms, despite their great potential, struggle with scalability and accuracy due to excessive reliance on entanglement. To address these limitations, we propose a variational quantum optimization algorithm (VQOA), which employs an ansatz based solely on quantum superposition, where single-qubit rotation gates function analogously to neurons in classical deep neural networks. This ansatz, which can be regarded as quantum neural networks, significantly reduces circuit complexity, enhances noise robustness, mitigates Barren Plateau issues, and enables efficient partitioning for highly complex, large-scale optimization. Furthermore, we introduce distributed VQOA (DVQOA), which integrates high-performance computing with quantum computing to achieve superior performance. These features enable a significant acceleration of material optimization tasks (e.g., metamaterial design), achieving more than 50 × speedup compared to state-of-the-art optimization algorithms. Beyond material design, DVQOA efficiently solves quantum chemistry problems and N -ary ( N ≥2) optimization problems involving higher-order interactions, outperforming classical deep neural networks. These advantages establish DVQOA as a highly promising and versatile solver for real-world problems, demonstrating the practical benefits of the quantum-classical approach.
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.
Abstract Quantum approximate optimization algorithm (QAOA) has shown promise in solving combinatorial optimization problems by providing quantum speedup on near-term gate-based quantum computing systems. However, QAOA faces challenges for high-dimensional problems due to the large number of qubits required and the complexity of deep circuits, limiting its scalability for real-world applications. In this study, we present a distributed QAOA (DQAOA), which leverages distributed computing strategies to decompose a large computational workload into smaller tasks that require fewer qubits and shallower circuits than are necessary to solve the original problem. These sub-problems are processed using a combination of high-performance and quantum computing resources. The global solution is iteratively updated by aggregating sub-solutions, allowing convergence toward the optimal solution. We demonstrate that DQAOA can handle considerably large-scale optimization problems (e.g., 1000-bit problem), achieving a high solution quality and short time-to-solution, outperforming existing strategies. Furthermore, we realize DQAOA on a quantum-centric supercomputing architecture, paving the way for practical applications of gate-based quantum computers in real-world optimization tasks. To extend DQAOA’s applicability to materials science, we further develop an active learning algorithm integrated with our DQAOA (AL-DQAOA), which involves machine learning, DQAOA, and active data production in an iterative loop. We successfully optimize photonic structures using AL-DQAOA, indicating that solving real-world optimization problems using gate-based quantum computing is feasible. We expect the proposed DQAOA to be applicable to a wide range of optimization problems and AL-DQAOA to find broader applications in material design.
Abstract Quantum Machine Learning (QML) represents a transformative paradigm that leverages quantum mechanical principles to enhance computational optimization in complex systems beyond the capabilities of classical machine learning algorithms. This paper investigates the optimization advantages of QML through a comprehensive analysis of quantum neural networks, quantum support vector machines, and hybrid quantum-classical architectures applied to complex system optimization problems. Our methodology employs variational quantum circuits with optimized feature encoding strategies and compares performance against classical baselines across multiple complexity scales. Experimental results demonstrate that QML algorithms achieve superior accuracy (95.1% for hybrid approaches vs 89.5% for classical neural networks), faster convergence rates (10x improvement in optimization iterations), and exponential scalability advantages for large-scale problems (31.7x speedup for problems with 5000 + parameters). The findings reveal that quantum algorithms excel in exploring high-dimensional solution spaces through superposition and entanglement, enabling more efficient navigation of complex optimization landscapes. Key contributions include a novel hybrid quantum-classical optimization framework, systematic performance benchmarking across problem complexities, and identification of quantum advantage thresholds for practical deployment. Results indicate that QML provides significant computational benefits for complex system optimization, particularly in scenarios involving large parameter spaces, non-convex optimization landscapes, and real-time processing requirements.
Optimization problems across domains such as logistics, finance, and artificial intelligence often involve complex and NP-hard formulations that are computationally challenging for classical algorithms due to scalability and efficiency limitations. The study aims to systematically investigate the role of Quantum Machine Learning (QML) in addressing complex optimization problems and to analyze its advantages over traditional optimization techniques. A comprehensive survey and comparative analysis of key QML algorithms, including Quantum Approximate Optimization Algorithm (QAOA), Variational Quantum Eigensolver (VQE), Quantum Neural Networks (QNNs), and Quantum Support Vector Machines (QSVMs), is conducted by examining their working principles, optimization capabilities, and real-world applications. The findings indicate that QML algorithms demonstrate significant potential in exploring large solutions spaces efficiently, achieving faster convergence, and providing improved optimization performance compared to classical approaches, although challenges such as quantum noise, scalability, and hardware limitations remain. The novelty of this study lies in providing a unified and critical comparative framework that integrates multiple QML optimization algorithms, highlights their practical feasibility, and identifies key research gaps hindering their real-world deployment. The implications of this research provide valuable insights for researchers and practitioners in selecting appropriate QML techniques and emphasize the need for advancements in hybrid quantum -classical systems, algorithms design, and quantum hardware to enable practical large-scale optimization.
Many real-world problems are naturally formulated as higher-order optimization (HUBO) tasks involving dense, multi-variable interactions, which are challenging to solve with classical methods. Quantum optimization offers a promising route, but hardware constraints and limitations to quadratic formulations have hampered their practicality. Here, we develop a distributed quantum optimization framework (DQOF) for dense, large-scale HUBO problems. DQOF assigns quantum circuits a central role in directly capturing higher-order interactions, while high-performance computing orchestrates large-scale parallelism and coordination. A clustering strategy enables wide quantum circuits without increasing depth, allowing efficient execution on near-term quantum hardware. We demonstrate high-quality solutions for HUBOs up to 500 variables within 170 seconds, significantly outperforming conventional approaches in solution quality and scalability. Applied to optical metamaterial design, DQOF efficiently discovers high-performance structures and shows that higher-order interactions are important for practical optimization problems. These results establish DQOF as a practical and scalable computational paradigm for large-scale scientific 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.