Quantum-Enhanced Graph Neural Networks

Jincheng Zhang

Published Sep 1, 2026• 0 citations

Abstract

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.

DOI: 10.5281/zenodo.22219629

Related papers

基于量子机器学习的图神经网络

Jincheng Zhang

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.

Aug 31, 20260 citations

Quantum Graph Neural Network Adaptive Learning

Jincheng Zhang

Quantum Graph Neural Network Adaptive Learning is a novel framework for training graph neural networks (GNNs) through an adaptive learning process. Traditional GNN models rely heavily on classical computer computations, limiting their ability to effectively handle complex datasets. This paper introduces a quantum-enhanced GNN architecture that leverages the unique properties of quantum graphs to enable self-adaptative learning, resulting in improved model performance across diverse datasets. The core mechanism involves the utilization of quantum phenomena to optimize the training process, thereby overcoming the limitations of classical approaches. This research demonstrates the efficacy of this approach through rigorous analysis and empirical validation, showcasing its potential to significantly enhance the capabilities of GNN models.

Sep 6, 20260 citations

基于量子纠缠的自适应图神经网络

Jincheng Zhang

This paper introduces a novel neural network architecture, termed a Quantum Entanglement-Aware Graph Neural Network (QEA-GNN), which leverages the principles of quantum entanglement to enhance the training process of graph neural networks. The QEA-GNN utilizes entanglement to dynamically adjust the network's parameters, leading to improved model performance and faster convergence. We explore the potential of this approach by demonstrating its effectiveness in a series of benchmark datasets. The core mechanism focuses on establishing entangled representations between nodes, allowing the network to learn more robust and informative features. The paper presents a detailed analysis of the algorithm's design, implementation, and performance evaluation. We conclude that the QEA-GNN offers a promising avenue for advancing graph neural network training through the incorporation of quantum entanglement.

Sep 1, 20260 citations

基于量子自适应的图神经网络

Jincheng Zhang

This paper introduces a novel approach to graph neural networks (GNNs) – the 'Quantum Adaptive Graph Neural Network' – leveraging quantum self-adaptation to enhance both expressiveness and computational efficiency. Traditional GNNs often struggle with the inherent complexities of graph data and require substantial computational resources. We propose a new architecture that employs quantum mechanics to simulate graph structures, allowing the network to learn more robust representations. This approach, dubbed Quantum Adaptive Graph Neural Network (QAGNN), utilizes quantum machine learning algorithms to improve the network's ability to capture intricate relationships within the graph. The paper details the core mechanism, its mathematical foundations, and preliminary experimental results demonstrating significant improvements in various graph classification tasks compared to traditional GNNs. We present a comprehensive analysis of the QAGNN's performance and discuss potential future research directions.

Aug 31, 20260 citations

Title: Topological Quantum Graph Neural Networks (TQNN)

Jincheng Zhang

This paper introduces a novel type of Graph Neural Network (GNN) called Topological Quantum Graph Neural Networks (TQNN). TQNN leverages the inherent topological structure of graphs and the strengths of Graph Neural Networks (GNNs) to enhance model recognition and prediction capabilities. We propose a new architecture that utilizes a modified graph representation and a quantum-inspired transformation to improve the network's ability to capture complex relationships and patterns within data. The core mechanism involves incorporating a quantum-inspired transformation into the graph representation, which enables the network to represent and process information in a fundamentally different way, leading to significant improvements in performance across various tasks. This work presents a new approach to GNNs that addresses limitations of existing methods, offering a promising pathway for enhanced machine learning performance.

Sep 4, 20260 citations

基于量子纠缠的神经网络架构

Jincheng Zhang

This paper investigates the potential of leveraging quantum entanglement to enhance neural network performance and efficiency. We propose a novel neural network architecture incorporating quantum entanglement to facilitate a more efficient and robust information transfer between neurons. The core mechanism centers on exploiting the unique properties of entangled states to minimize computational complexity and improve learning speed. The research explores the feasibility of implementing this architecture within a simulated environment, demonstrating preliminary results suggesting a potential improvement in training speed and accuracy compared to conventional neural networks. The paper concludes with a discussion of future research directions, including exploring the scalability of the quantum entanglement approach and investigating its applicability to diverse neural network tasks.

Aug 29, 20260 citations