How does the layerwise learning technique address the vanishing gradient problem in QNNs?
The vanishing gradient problem is a significant challenge in training deep neural networks, including Quantum Neural Networks (QNNs). This issue arises when gradients used for updating network parameters diminish exponentially as they are backpropagated through the layers, leading to minimal updates in earlier layers and hindering effective learning. The layerwise learning technique has been proposed
What are the main types of unitary gates used in QNNs, and how do they function within the quantum circuit?
Quantum Neural Networks (QNNs) are an emerging area in the intersection of quantum computing and artificial intelligence, leveraging the principles of quantum mechanics to enhance machine learning algorithms. A fundamental component of QNNs is the unitary gate, which plays a important role in manipulating quantum bits (qubits) within a quantum circuit. Understanding the main types
- Published in Artificial Intelligence, EITC/AI/TFQML TensorFlow Quantum Machine Learning, Overview of TensorFlow Quantum, Layer-wise learning for quantum neural networks, Examination review