Quantum Classical Neural Network

Quantum-classical hybrid neural networks aim to leverage the potential advantages of quantum computing for enhanced machine learning performance, particularly in areas where classical methods struggle. Current research focuses on developing and evaluating various hybrid architectures, often integrating variational quantum circuits with classical convolutional or spiking neural networks, and exploring different training algorithms like evolution strategies. These efforts are driven by the potential for improved accuracy, robustness, and efficiency in diverse applications, including medical image analysis, power flow analysis, and drug discovery, although the practical advantages remain a subject of ongoing investigation.