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Conception and Integration of Accessible Hardware Demonstrators for Quantum Computing and Quantum Reinforcement Learning

Quantum Computing (QC) offers the potential for substantial computational speed-ups by exploiting the principles of quantum mechanics. Several quantum algorithms have already been shown to outperform classical methods. However, their practical useful- ness on current Noisy Intermediate-Scale Quantum (NISQ) devices is still limited due to hardware noise and the small number of available qubits. Even with these limita- tions, variational hybrid quantum–classical algorithms are viewed as a promising way to achieve early quantum advantages on existing hardware.

This bachelor’s thesis investigates compact hardware demonstrators that make quan- tum concepts accessible and examines the feasibility of Quantum Machine Learning (QML) embedded systems. The first part focuses on an empirical study of Variational Quantum Deep Q-Learning (VQ-DQL) in embedded systems. For this purpose, a VQ-DQL agent was developed that controls an Anki Overdrive car on a custom-designed track. The training results show that the quantum agent can reach a performance level similar to a classical Deep Q-Network (DQN).

The second part presents a compact Quantum circuit demonstrator based on an afford- able ESP32 microcontroller. The system is designed for accessibility and hands-on ex- perimentation. It uses Radio Frequency Identification (RFID)-modules as a physical in- terface for selecting quantum gates and simulates the state of a five-qubit system in real time. The successful implementation and evaluation of basic quantum circuits, includ- ing the creation of entangled states such as a five-qubit Bell state, confirm the hands-on functionality and practicality of the demonstrator.

The findings of this thesis show that low-cost and accessible quantum learning tools are feasible. They also highlight future progresses, especially in improving the scalability of such demonstrators and developing more effective optimization techniques for quantum machine learning algorithms.