Entangled Datasets for Quantum Machine Learning. - Louis Schatzki; Andrew Arrasmith; Patrick J. Coles; Marco Cerezo (CoRR, 2021)

Representation Theory for Geometric Quantum Machine Learning. - Michael Ragone; Paolo Braccia; Quynh T. Nguyen; Louis Schatzki; Patrick J. Coles; Frederic Sauvage; Martin Larocca; Marco Cerezo (CoRR, 2022)

Theory for Equivariant Quantum Neural Networks. - Quynh T. Nguyen; Louis Schatzki; Paolo Braccia; Michael Ragone; Patrick J. Coles; Frederic Sauvage; Martin Larocca; Marco Cerezo (CoRR, 2022)

Theoretical Guarantees for Permutation-Equivariant Quantum Neural Networks. - Louis Schatzki; Martin Larocca; Frederic Sauvage; Marco Cerezo (CoRR, 2022)

On the Role of Entanglement and Statistics in Learning. - Srinivasan Arunachalam; Vojtech Havlícek; Louis Schatzki (NeurIPS, 2023)

Distributed inner product estimation with limited quantum communication. - Srinivasan Arunachalam; Louis Schatzki (CoRR, 2024)

Generalized Inner Product Estimation with Limited Quantum Communication. - Srinivasan Arunachalam; Louis Schatzki (STACS, 2025)