Towards quantum advantage for topological data analysis. - Casper Gyurik; Chris Cade; Vedran Dunjko (CoRR, 2020)

Parametrized Quantum Policies for Reinforcement Learning. - Sofiène Jerbi; Casper Gyurik; Simon C. Marshall; Hans J. Briegel; Vedran Dunjko (NeurIPS, 2021)

Variational quantum policies for reinforcement learning. - Sofiène Jerbi; Casper Gyurik; Simon C. Marshall; Hans J. Briegel; Vedran Dunjko (CoRR, 2021)

High Dimensional Quantum Learning With Small Quantum Computers. - Simon C. Marshall; Casper Gyurik; Vedran Dunjko (CoRR, 2022)

On establishing learning separations between classical and quantum machine learning with classical data. - Casper Gyurik; Vedran Dunjko (CoRR, 2022)

Towards quantum advantage via topological data analysis. - Casper Gyurik; Chris Cade; Vedran Dunjko (Quantum, 2022)

Shadows of quantum machine learning. - Sofiène Jerbi; Casper Gyurik; Simon C. Marshall; Riccardo Molteni; Vedran Dunjko (CoRR, 2023)

Exponential separations between classical and quantum learners. - Casper Gyurik; Vedran Dunjko (CoRR, 2023)

Structural risk minimization for quantum linear classifiers. - Casper Gyurik; Dyon van Vreumingen; Vedran Dunjko (Quantum, 2023)

High Dimensional Quantum Machine Learning With Small Quantum Computers. - Simon C. Marshall; Casper Gyurik; Vedran Dunjko (Quantum, 2023)

On Bounded Advice Classes. - Simon C. Marshall; Casper Gyurik; Vedran Dunjko (CoRR, 2024)

Quantum computing and persistence in topological data analysis. - Casper Gyurik; Alexander Schmidhuber; Robbie King; Vedran Dunjko; Ryu Hayakawa (CoRR, 2024)

On the Relation between Trainability and Dequantization of Variational Quantum Learning Models. - Elies Gil-Fuster; Casper Gyurik; Adrián Pérez-Salinas; Vedran Dunjko (ICLR, 2025)

Differential equation quantum solvers: engineering measurements to reduce cost. - Annie E. Paine; Casper Gyurik; Antonio Andrea Gentile (CoRR, 2025)