##基于量子神经网络的特征提取与分类

Jincheng Zhang

Published Aug 31, 2026• 0 citations

Abstract

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.

DOI: 10.5281/zenodo.22194618

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