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)

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

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

Understanding polysemanticity in neural networks through coding theory. - Simon C. Marshall; Jan H. Kirchner (CoRR, 2024)

PDQMA = DQMA = NEXP: QMA With Hidden Variables and Non-collapsing Measurements. - Scott Aaronson; Sabee Grewal; Vishnu Iyer; Simon C. Marshall; Ronak Ramachandran (CoRR, 2024)

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

Improved Separation Between Quantum and Classical Computers for Sampling and Functional Tasks. - Simon C. Marshall; Scott Aaronson; Vedran Dunjko (CCC, 2025)