Recognition of time-frequency signals based on quantum-classical hybrid residual networks
Abstract
Quantum machine learning, positioned at the confluence of machine learning and quantum information science, offers transformative paradigms that enhance computational capabilities and broaden application domains. In this study, we propose a quantum-classical hybrid residual neural network (QCH-ResNet) tailored for multi-task recognition of time-frequency signals in radar applications. The QCH-ResNet integrates parameterized quantum circuits with classical residual neural networks, enabling accurate classification and detection even under high-noise conditions. Experimental results reveal that QCH-ResNet outperforms traditional residual neural networks in classification accuracy and noise resilience, highlighting the potential of quantum-enhanced signal processing. This work not only expands the scope of quantum computing applications but also provides a novel framework for analyzing complex time-frequency signals.
