Learning Algorithms for Control and Characterization of Quantum Matter

Precise verification and parameter estimation of quantum devices are critical for further technological progress in simulation and understanding of quantum matter. In this presentation, I will discuss how learning algorithms can be used efficiently for this task. We show how to use Bayesian learning and neural networks to reconstruct the physics of large scale out-of-equilibrium quantum systems in the context of quantum simulation with ultracold atoms. We investigate the scalability of this class of methods for efficient estimation of all local parameters with previously inaccessible levels of precision. Moreover, we show how reinforcement learning can be used to design an optimal workflow for parameter estimation protocols. Finally, we discuss how this class of learning methods can be generalised for other quantum device calibration purposes, such as the characterization of energy levels of quantum dots in bilayer graphene.