Design and analysis of a novel quantum-classical hybrid neural network for environmental sound classification

Abstract

In the field of Environmental Sound Classification (ESC), traditional deep learning methods face significant challenges in processing high-dimensional audio data. In response, this study innovatively proposes a quantum-classical hybrid neural network model—St-HQCNN. This model adeptly integrates the advantages of quantum superposition and quantum entanglement, which are fundamental to quantum computing, thereby significantly enhancing the model's ability to recognize and process complex sound data with greater robustness. Through the design of carefully crafted quantum circuits and misalignment reading mechanisms, St-HQCNN not only demonstrates outstanding classification performance on benchmark datasets such as UrbanSound8K, ESC-10, and ESC-50, achieving accuracy rates of 97.14%, 99.38%, and 93.75%, respectively, but also highlights the tremendous potential of quantum-enhanced neural networks in sound recognition tasks. The experimental results clearly reveal the substantial promise of quantum-assisted neural networks in this domain, laying the foundation for future research and practical applications. This study not only showcases the theoretical innovations of St-HQCNN but also focuses on its feasibility for real-world applications. Moving forward, we aim to explore the scalability of the model, address challenges in practical deployment, and advance the widespread use of quantum neural networks in the field of machine learning.