Nicholas Ezzell

Out-of-distribution generalization for learning quantum dynamics

2022 · arXiv (Cornell University)

Generalization bounds are a critical tool to assess the training data requirements of Quantum Machine Learning (QML). Recent work has established guarantees for in-distribution generalization of quantum neural networks (QNNs), where training and testing data …