多模态信息融合的量子神经网络

Jincheng Zhang

Published Sep 5, 2026• 0 citations

Abstract

This paper introduces a novel quantum neural network architecture, termed "Multi-Modal Information Fusion Quantum Neural Network" (MMIFQN), designed to enhance the representation learning capabilities of complex data sequences. The core principle of MMIFQN lies in the strategic integration of multiple information sources – including visual, textual, and audio data – through a quantum neural network. This integration leverages quantum state manipulation, specifically superposition and entanglement, to facilitate a deeper and more nuanced understanding of the underlying data patterns. The proposed architecture demonstrates significant improvements in accuracy and robustness compared to traditional neural network models when dealing with intricate datasets. This work presents a paradigm shift in how quantum computing can be applied to data representation and processing, potentially unlocking new capabilities in areas such as pattern recognition and anomaly detection.

DOI: 10.5281/zenodo.22332122

Related papers

基于量子神经网络的非线性模式识别

Jincheng Zhang

This paper explores the application of quantum neural networks (QNNs) to enhance pattern recognition capabilities, particularly in the analysis of complex datasets. Traditional neural networks often struggle with non-linear data patterns due to limitations in their ability to capture intricate relationships. Quantum neural networks, leveraging quantum mechanical principles, offer a potential paradigm shift in pattern recognition by introducing inherent quantum properties that facilitate novel feature extraction and pattern understanding. We propose a novel QNN architecture designed to automatically learn hidden patterns and relationships within data, aiming to surpass the capabilities of conventional neural networks. This work investigates the potential of quantum-enhanced neural networks for identifying complex, non-linear patterns, demonstrating its efficacy through simulation and preliminary experimentation. The core mechanism involves utilizing quantum entanglement and superposition to represent and process data in a fundamentally different manner, ultimately leading to improved pattern recognition accuracy.

Sep 2, 20260 citations

基于量子神经网络的非线性学习

Jincheng Zhang

Quantum Neural Networks (QNNs) offer a fundamentally different approach to neural network learning compared to classical networks, leveraging the unique properties of quantum mechanics. This paper investigates the potential of QNNs to achieve more efficient and powerful non-linear learning, particularly when handling high-dimensional data and complex patterns. We propose a novel algorithm that utilizes quantum entanglement and superposition to enhance the learning process and feature extraction capabilities. This work explores the potential of these quantum properties to overcome limitations inherent in current neural network architectures, specifically in areas requiring sophisticated pattern recognition. The core of our approach focuses on designing quantum units within the QNN architecture to facilitate enhanced learning and memory. The paper details the proposed algorithm, its theoretical underpinnings, and preliminary experimental results demonstrating its effectiveness in a simulated environment. We conclude with a discussion of future research directions, emphasizing the transformative potential of integrating quantum principles into neural network learning.

Sep 7, 20260 citations

Title: Quantum-Enhanced Neural Network Architecture

Jincheng Zhang

Quantum-enhanced neural networks (QNNs) represent a promising area of research aiming to surpass classical neural networks in complex pattern recognition tasks. This paper explores a novel QNN architecture utilizing quantum properties like superposition and entanglement to improve learning capabilities. We present a detailed design of this architecture, outlining the key quantum gates and their integration into the network's structure. The core claim is that incorporating these quantum elements will significantly enhance the network's ability to model intricate relationships within data, leading to improved performance in tasks requiring complex pattern detection.

Sep 5, 20260 citations

##基于量子神经网络的特征提取与分类

Jincheng Zhang

This paper explores the potential of Quantum Neural Networks (QNNs) for advanced feature extraction and classification tasks. Classical neural networks often struggle with complex datasets and computational bottlenecks. QNNs, leveraging principles of quantum mechanics like entanglement and superposition, offer a fundamentally different approach. This research investigates the construction and application of a QNN architecture designed to exploit these quantum properties. We present a model utilizing quantum gates and qubits to perform parallel computations and generate more effective feature representations compared to traditional neural networks. The core claim of this work is the demonstration of QNNs surpassing the performance of classical networks in complex classification problems. We detail the theoretical framework, model design, and discuss the potential impact of QNNs on various applications, highlighting their ability to unlock new levels of accuracy and efficiency in data analysis. The key mechanisms driving this potential are the inherent parallelism and superior feature representation capabilities of QNNs.

Aug 31, 20260 citations

##基于量子神经网络的模式识别

Jincheng Zhang

This paper investigates the application of quantum neural networks (QNNs) for pattern recognition. Traditional neural networks face limitations in processing complex data due to their reliance on classical computing principles. QNNs leverage quantum mechanics phenomena, specifically superposition and entanglement, to potentially overcome these limitations. This research proposes a framework for constructing a QNN-based pattern recognition system. The core claim is to enhance pattern recognition accuracy and efficiency by utilizing these quantum properties. We detail the conversion process from classical neural networks to QNNs, highlighting the computational advantages offered by quantum computing. This work contributes to the nascent field of quantum machine learning, exploring the potential of QNNs to revolutionize pattern recognition tasks. The theoretical foundation presented here forms a basis for future research and development in this area.

Aug 29, 20260 citations

基于自关联的量子神经网络

Jincheng Zhang

This paper presents a novel approach to quantum neural networks (QNNs) leveraging the inherent properties of self-associative quantum systems. Traditional quantum neural networks often struggle with computational complexity and require extensive error correction, limiting their practical applicability. This research proposes a new QNN architecture that utilizes self-associative quantum states to dramatically reduce the number of parameters and enhance the network's ability to learn complex patterns. We demonstrate the superiority of this approach through theoretical analysis and preliminary experimental results on simplified quantum circuits, showcasing significant improvements in both training speed and model capacity compared to existing QNNs. The core mechanism involves dynamically adjusting the entanglement structure within the self-associative system to optimize information flow and propagation, leading to enhanced gradient propagation and reduced computational burden. Further, we explore the potential of this architecture for tackling specific machine learning tasks, including pattern recognition and classification.

Sep 7, 20260 citations