Quantum Neural Tangent Kernel

Quantum Neural Tangent Kernels (QNTKs) are mathematical tools used to analyze the training dynamics and performance of quantum neural networks (QNNs), particularly in the limit of infinitely wide networks. Current research focuses on understanding the relationship between QNTKs and key properties of QNNs like expressibility and trainability, exploring generalizations such as Quantum Path Kernels to capture hierarchical learning, and developing methods like symmetric pruning to improve QNN performance. This research aims to provide theoretical frameworks for designing more efficient and effective QNN architectures for applications in quantum machine learning and quantum simulation, ultimately advancing our understanding of both quantum computation and machine learning.