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Machine Learning Quantum Optimal Control

Authors: Noah Hoffmann

Ref.: Master thesis, Martin-Luther University of Halle-Wittenberg (2020)

Abstract: In this thesis, we will focus on the one-dimensional harmonic oscillator and try to maximize the excitation of the first excited energy level starting from the ground state. For this purpose, we will create a training set by propagating the Schrödinger equation forward in time with the Crank-Nicolson method. Based on this data we will train a neural network and optimize the excitation with a genetic algorithm using the network as a surrogate model. Additionally, a new method is discussed, which uses automatic differentiation algorithms allowing us to directly optimize the target quantity.

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