Title: Reinforcement Learning based approaches to Quantum Control

Abstract: 
In recent years, the ever-growing field of Machine Learning has gathered widespread interest across multiple disciplines, including quantum physics. Current research on the benefits of using machine learning techniques to address quantum problems and their possible overalp with quantum technologies ranges from the applications to phase recognition, to the learning of states and operations to the research of novel quantum experiments and even to the design of quantum learning algorithms, only to name a few cases.
In particular, (Deep) Reinforcement Learning (RL) seems to be especially well suited for problems of control of quantum systems in different scenarios. Reinforcement Learning methods can be adapted to address quantum problems and can offer a valid alternative to Optimal Control techniques or can be used together with the latter to improve our ability to better control quantum systems.
In this talk I will give a brief overview of Reinforcement Learning and its application to quantum control [1]. I will then present my works on the application of Reinforcement Learning to problems of entropy production reduction in closed quantum systems [2] and coherent population tranfer in a three level system [3], focusing on how the RL setting can be adapted and modified to specifically address quantum control problems, it's advantages, and possible combinations with more traditional optimization techniques.

[1] "A Tutorial on Optimal Control and Reinforcement Learning methods for Quantum Technologies", Luigi Giannelli, Pierpaolo Sgroi, Jonathon Brown, Gheorghe Sorin Paraoanu, Mauro Paternostro, Elisabetta Paladino, Giuseppe Falci,
Physics Letters A 434 (2022)
[2] "Reinforcement learning approach to non-equilibrium quantum thermodynamics", Pierpaolo Sgroi, G. Massimo Palma, Mauro Paternostro, Phys. Rev. Lett. 126 (2021)
[3] "Reinforcement learning-enhanced protocols for coherent population-transfer in three-level quantum systems", Jonathon Brown, Pierpaolo Sgroi, Luigi Giannelli, Gheorghe Sorin Paraoanu, Elisabetta Paladino, Giuseppe Falci, Mauro Paternostro, Alessandro Ferraro, New J. Phys. 23 (2021)