Towards practical quantum neural network diagnostics with neural tangent kernels

Predicting the performance of a Quantum Neural Network (QNN) on a given dataset before its training can save critical resources. However, obtaining such a priori information is, in general, very challenging. The Quantum Neural Tangent Kernel (QNTK) has recently emerged as a tool to mathematically describe the behavior of QNNs. Here, we provide practical guidelines for using the QNTK to diagnose the performance of a given QNN model. In particular, we link QNTK spectral features to the training dynamics, and we show how a first-order kernel approximation can predict the expected inference performance. By analyzing the interplay between the number of model parameters and of training samples, we identify a mismatch at the interpolation threshold: while the QNTK exhibits a double descent in generalization error, we observe that QNNs do not show this behavior in practice. This suggests that QNTK diagnostics are mostly reliable for overparametrized models well above the interpolation regime. Extensive numerical simulations across different architectures and datasets validate our approach. Our results demonstrate that QNTK diagnostics yields useful information about QNN behavior for sufficiently deep circuits, and enable detecting and addressing potential shortcomings in model design.