Photonic architecture for reinforcement learning. - Fulvio Flamini; Arne Hamann 002; Sofiène Jerbi; Lea M. Trenkwalder; Hendrik Poulsen Nautrup; Hans J. Briegel (CoRR, 2019)

A framework for deep energy-based reinforcement learning with quantum speed-up. - Sofiène Jerbi; Hendrik Poulsen Nautrup; Lea M. Trenkwalder; Hans J. Briegel; Vedran Dunjko (CoRR, 2019)

On the convergence of projective-simulation-based reinforcement learning in Markov decision processes. - Walter L. Boyajian; Jens Clausen; Lea M. Trenkwalder; Vedran Dunjko; Hans J. Briegel (Quantum Mach. Intell., 2020)

Reinforcement learning for optimization of variational quantum circuit architectures. - Mateusz Ostaszewski; Lea M. Trenkwalder; Wojciech Masarczyk; Eleanor Scerri; Vedran Dunjko (NeurIPS, 2021)

Automated Gadget Discovery in Science. - Lea M. Trenkwalder; Andrea López-Incera; Hendrik Poulsen Nautrup; Fulvio Flamini; Hans J. Briegel (CoRR, 2022)

Operationally meaningful representations of physical systems in neural networks. - Hendrik Poulsen Nautrup; Tony Metger; Raban Iten; Sofiène Jerbi; Lea M. Trenkwalder; Henrik Wilming; Hans J. Briegel; Renato Renner (Mach. Learn. Sci. Technol., 2022)

Compilation of product-formula Hamiltonian simulation via reinforcement learning. - Lea M. Trenkwalder; Eleanor Scerri; Thomas E. O'Brien; Vedran Dunjko (CoRR, 2023)

Automated gadget discovery in the quantum domain. - Lea M. Trenkwalder; Andrea López-Incera; Hendrik Poulsen Nautrup; Fulvio Flamini; Hans J. Briegel (Mach. Learn. Sci. Technol., 2023)

Hybrid discrete-continuous compilation of trapped-ion quantum circuits with deep reinforcement learning. - Francesco Preti; Michael Schilling 004; Sofiène Jerbi; Lea M. Trenkwalder; Hendrik Poulsen Nautrup; Felix Motzoi; Hans J. Briegel (Quantum, 2024)