Residual Entanglement: How ResQuNNs Fix Gradient Flow in Quantum Neural Networks
Residual Entanglement: How ResQuNNs Fix Gradient Flow in Quantum Neural Networks In classical deep learning, residual connections revolutionized the training of deep networks. Now, a similar breakthrough is happening in quantum machine learning. The paper “ResQuNNs: Towards Enabling Deep Learning in Quantum Convolution Neural Networks” introduces a method to overcome a fundamental bottleneck in Quantum Convolutional Neural Networks (QuNNs): the inability to train multiple quantum layers due to broken gradient flow. ...