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Global Science Journal of Information Technology and Computer Science

Quantum-Inspired Meta-Heuristic Algorithm for Large-Scale Graph Neural Network Training in Distributed Cloud-Edge Environments

Eka Prasetya Adhu Sugara, Nurul Azwanti, Ivy Derla

Published Jun 30, 2025• 1 citation
Publisher

Abstract

This paper explores the application of quantum-inspired optimization algorithms in the training of large-scale Graph Neural Networks (GNNs) within distributed cloud-edge environments. GNNs have gained significant attention due to their ability to model complex relationships in graph-structured data, yet their training presents challenges such as high computational demand, inefficient resource allocation, and slow convergence, especially for large datasets. Traditional meta-heuristic algorithms, while useful, often face scalability and performance issues when applied to such large-scale tasks. To address these challenges, we propose a quantum-inspired meta-heuristic algorithm that leverages quantum principles, such as superposition and entanglement, to enhance optimization processes. The algorithm was integrated into a hybrid cloud-edge system, where computational tasks are dynamically distributed between edge nodes and the cloud, optimizing resource utilization and reducing latency. Our experimental results demonstrate significant improvements in training speed, resource efficiency, and convergence rate when compared to traditional optimization methods such as Genetic Algorithms and Simulated Annealing. The quantum-inspired algorithm not only accelerates the training process but also reduces memory usage, making it well-suited for large-scale GNN applications. Furthermore, the system's scalability was enhanced by the hybrid cloud-edge architecture, which balances computational load and enables real-time data processing. The findings suggest that quantum-inspired optimization algorithms can significantly improve the training of GNNs in distributed systems, opening new avenues for real-time applications in areas such as social network analysis, anomaly detection, and recommendation systems. Future work will focus on refining these algorithms to handle even larger datasets and more complex GNN architectures, with potential integration into edge devices for enhanced real-time decision-making.

DOI
10.70062/globalscience.v1i2.171

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Quantum-Enhanced Graph Neural Networks (QEN) presents a novel approach to graph neural network (GNN) learning, leveraging the principles of quantum entanglement to enhance both the training process and the network's expressive capabilities. This research investigates a method for utilizing entanglement to accelerate training and improve the network's ability to represent complex relationships within graph data. We introduce a new architecture incorporating quantum entanglement-enhanced layer activations, exploring its potential to overcome limitations inherent in classical GNN training. The core mechanism focuses on utilizing entanglement to reduce computational complexity and improve the speed of learning. This work demonstrates promising results through theoretical analysis and preliminary experimental validation, suggesting a significant improvement in both training speed and model performance.
Rapid advances in graph neural networks have greatly enhanced the ability of AI systems to learn from structural and relational data. However, the performance of these networks is highly sensitive to their hyperparameters and structural design, which is often optimized through computationally expensive trial-and-error or gradient-based methods that tend to achieve local optimal values. To address these challenges, this chapter proposes a novel hybrid framework, quantum-inspired child-drawing development Optimization (QCDDO), to optimize GNN training and hyperparameter tuning that leverages quantum-inspired analogous heuristic optimization. Inspired by quantum mechanical principles—such as superposition, entanglement, and quantum tunneling—these analogous heuristic strategies provide efficient exploration capabilities in high-dimensional, non-convex search spaces. We compare the framework with quantum-inspired particle swarm optimization (QPSO) and quantum-inspired gray wolf optimization (QGWO). Comparative results demonstrate that the quantum-inspired child-drawing development optimization (QCDDO) approach outperforms conventional child-drawing development optimization algorithms and quantum-inspired particle swarm optimization (QPSO) and quantum-inspired gray wolf optimization (QGWO) in convergence speed, solution quality, and diversity. This chapter concludes by reviewing future directions for integrating quantum-inspired strategies with graph neural networks for large-scale and real-time applications.
This paper investigates the application of quantum machine learning to graph neural networks (GNNs), specifically focusing on the design and evaluation of a novel quantum-enhanced GNN architecture. We propose a quantum-inspired neural network architecture that leverages the inherent quantum properties of entangled states and superposition to improve the representation capacity and computational efficiency of GNNs. The core mechanism centers around simulating graph structures using quantum algorithms and incorporating quantum machine learning techniques to enhance the network's ability to capture complex relationships within the data. This work introduces a framework for leveraging quantum mechanics to boost the performance of GNNs, potentially offering a significant advancement over traditional approaches. The paper presents initial results demonstrating the efficacy of this quantum-enhanced GNN architecture in a benchmark dataset, highlighting the potential for improved representation and faster training times.
Graph Neural Networks (GNNs) are powerful machine learning models that excel at analyzing structured data represented as graphs, demonstrating remarkable performance in applications like social network analysis and recommendation systems. However, classical GNNs face scalability challenges when dealing with large-scale graphs. This paper proposes frameworks for implementing GNNs on quantum computers to potentially address the challenges. We devise quantum algorithms corresponding to the three fundamental types of classical GNNs: Graph Convolutional Networks, Graph Attention Networks, and Message-Passing GNNs. A complexity analysis of our quantum implementation of the Simplified Graph Convolutional (SGC) Network shows potential quantum advantages over its classical counterpart, with significant improvements in time and space complexities. Our complexities can have trade-offs between the two: when optimizing for minimal circuit depth, our quantum SGC achieves logarithmic time complexity in the input sizes (albeit at the cost of linear space complexity). When optimizing for minimal qubit usage, the quantum SGC exhibits space complexity logarithmic in the input sizes, offering an exponential reduction compared to classical SGCs, while still maintaining better time complexity. These results suggest our Quantum GNN frameworks could efficiently process large-scale graphs. This work paves the way for implementing more advanced Graph Neural Network models on quantum computers, opening new possibilities in quantum machine learning for analyzing graph-structured data.
The convergence of quantum computing and artificial intelligence (AI) has introduced innovative opportunities to accelerate deep learning, particularly within decentralized cloud architectures. This study develops an adaptive quantum AI model leveraging hybrid quantum-classical algorithms to optimize deep learning processes such as training, inference, and resource allocation. The proposed model integrates Variational Quantum Circuits (VQCs) and Quantum Approximate Optimization Algorithms (QAOAs), which enable efficient handling of high-dimensional data and complex optimization tasks inherent in distributed environments. By addressing challenges like latency, energy efficiency, and computational overhead, the quantum AI model demonstrates significant performance gains in decentralized cloud systems.Experimental evaluations on benchmark datasets reveal a 40% reduction in training time, a 30% improvement in resource efficiency, and a 20% increase in prediction accuracy compared to classical deep learning frameworks. This study highlights the transformative potential of quantum computing in AI-driven decentralized cloud architectures, offering insights into its application for computationally intensive tasks across industries such as healthcare, finance, and logistics. Future work will focus on refining quantum hardware compatibility, developing quantum error correction methods, and exploring federated learning applications to expand the scope of quantum AI in privacy-preserving and distributed systems.