Joe Gibbs

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 …

Invariant extended Kalman filter for tracking the bishop frame using radar measurements

2023 · IET conference proceedings.

This paper proposes a target model for radar state estimation based on the Bishop or Parallel Transport frame, extending upon previous work using the Frenet-Serret frame to describe target motion. The Bishop frame is embedded …