2024 – Machine Learning and Positioning Systems

Haris Asif: Quantum Circuit Optimization via Hierarchical Reinforcement Learning (FAU Erlangen-Nürnberg, 2024)

By mugga May 4, 20252024, Finished, Master Thesis, Reinforcement Learning

Motivation / Related Work Optimizing quantum circuits is essential for various applications, such as chemical simulations, due to the inherent complexity of quantum systems and the current limitations of quantum…

Thorsten Bescher: Schedule-Net and Gumbel-Alpha-Zero Play-to-Plan: a Hybrid Approach for the Job-Shop Scheduling Problem (FAU Erlangen-Nürnberg, 2024)

By mugga May 4, 20252024, Finished, Master Thesis, Reinforcement Learning

Motivation / Related Work Recently, reinforcement learning (RL) is applied frequently for combinatorial optimization problems [1] with real world applications, e.g., the job-shop scheduling problem (JSSP) or the travelling salesman…

Stephan Geisler: Designing and training foundation models for positioning applications (FAU Erlangen-Nürnberg, 2024)

By mugga May 4, 20252024, 5G & 6G, Finished, Master Thesis

Foundation models (like OpenAI’s GPT-4/ChatGPT [1] or Meta’s Llama 2 [2]) have been a disruptive technology that within just few months has started to transform the modus operandi of several industries. There have…

Nicolas Kolbenschlag: Automated Generation of Block-Encodings for Quantum Policy Iteration (FAU Erlangen-Nürnberg, 2024)

By mugga May 4, 20252024, Finished, Master Thesis, Reinforcement Learning

Motivation The rise of quantum computing has offered new opportunities to achieve computational tasks that classical computers cannot. However, to achieve quantum advantage over classical algorithms, it is necessary to…