Analyzing Reinforcement Learning strategies from a parameterized quantum walker (en)

Analyzing Reinforcement Learning strategies from a parameterized quantum walker

Abstract:

Reinforcement Learning has made significant progress in solving complex problems. Hence, it is not surprising that it can be found in various application domains. Quantum Computing as well is a prospering field, where big advancements could be seen over the last decades. Better quantum computers led to first experimentally proven quantum supremacy. Hence, the field of research grew which led to improvements in various application domains of quantum computing, one of them being quantum Reinforcement Learning where quantum computing is combined with classical reinforcement learning techniques. Among other approaches, quantum walks are used as quantum computational framework which is also the case in the present work. Here, the approach of using parameterized coin matrices to determine the behaviour of the walker adapted to grid graphs is used. Thereby, the parameters of the coin matrices should be learned, such that an optimized performance of the walker to perform a specific task is reached. In this thesis the feasibility of this approach applied to a grid world is investigated using grids of the size 2×2 and 4×4. Furthermore, a new concept for including additional constraints by introducing an extra environment qubit is presented and its influence on the optimization process of the parameters examined. The results can be seen as a proof of concept as for all experiments the approach used here shows better results than the random baseline. Moreover, no negative influence of the environment qubit can be detected. The results gained here are a basis for further research using this approach.

Author:

Lorena Wemmer

Advisors:

Jonas Stein, Michael Kölle, Claudia Linnhoff-Popien

Student Thesis | Published May 2023 | Copyright © QAR-Lab
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