A high-bias, low-variance introduction to Machine Learning for physicists. - Pankaj Mehta; Marin Bukov; Ching-Hao Wang; Alexandre G. R. Day; Clint Richardson; Charles K. Fisher; David J. Schwab (CoRR, 2018)

Policy Gradient based Quantum Approximate Optimization Algorithm. - Jiahao Yao; Marin Bukov; Lin Lin 001 (MSML, 2020)

Reinforcement Learning for Many-Body Ground State Preparation based on Counter-Diabatic Driving. - Jiahao Yao; Lin Lin 001; Marin Bukov (CoRR, 2020)

Noise-Robust End-to-End Quantum Control using Deep Autoregressive Policy Networks. - Jiahao Yao; Paul Köttering; Hans Gundlach; Lin Lin 001; Marin Bukov (MSML, 2021)

Monte Carlo Tree Search based Hybrid Optimization of Variational Quantum Circuits. - Jiahao Yao; Haoya Li; Marin Bukov; Lin Lin 001; Lexing Ying (MSML, 2022)

Self-correcting quantum many-body control using reinforcement learning with tensor networks. - Friederike Metz; Marin Bukov (Nat. Mac. Intell., 2023)

Reinforcement Learning to Disentangle Multiqubit Quantum States from Partial Observations. - Pavel Tashev; Stefan Petrov; Friederike Metz; Marin Bukov (CoRR, 2024)