A tutorial on optimal control and reinforcement learning methods for quantum technologies

| openaire: EC/H2020/766900/EU//TEQ Funding Information: The authors thank Alessandro Ferraro, Nicola Macrì, Federico Roy, Phila Rembold, Riccardo Sessa, Francesco M. D. Pellegrino, Christiane P. Koch, and Dominique Sugny for useful discussions. This work was supported by the Northern Ireland Department for Economy (DfE) , the EU H2020 framework through Collaborative Projects TEQ (Grant Agreement No. 766900 ), the DfE-SFI Investigator Programme (Grant No. 15/IA/2864 ), the Leverhulme Trust Research Project Grant UltraQute (Grant No. RGP-2018-266 ), COST Action CA15220 , the Royal Society Wolfson Research Fellowship scheme ( RSWF/R3/183013 ) and International Mobility Programme, the UK EPSRC (Grant No. EP/T028106/1 ), the Finnish Center of Excellence in Quantum Technology QTF (projects 312296 , 336810 ) of the Academy of Finland, and RADDESS programme (project 328193 ), Grant No. FQXi-IAF19-06 (“Exploring the fundamental limits set by thermodynamics in the quantum regime”) of the Foundational QuestionsInstitute Fund (FQXi), the QuantERA grant SiUCs (Grant No. 731473 QuantERA), and by University of Catania , Piano per la Ricerca 2016–18 - linea di intervento “Chance”, Piano di Incentivi per la Ricerca di Ateneo 2020/2022, proposal Q-ICT. Publisher Copyright: © 2022 Elsevier B.V.Quantum Optimal Control is an established field of research which is necessary for the development of Quantum Technologies. In recent years, Machine Learning techniques have been proved useful to tackle a variety of quantum problems. In particular, Reinforcement Learning has been employed to address typical problems of control of quantum systems. In this tutorial we introduce the methods of Quantum Optimal Control and Reinforcement Learning by applying them to the problem of three-level population transfer. The jupyter notebooks to reproduce some of our results are open-sourced and available on github1.Peer reviewe