4 papers
Agentic Exploration of Physics Models
Maximilian Nägele, Florian Marquardt
The process of scientific discovery relies on an interplay of observations, analysis, and hypothesis generation. Machine learning is increasingly being adopted to address individua…
Reusability Report: Optimizing T-count in General Quantum Circuits with AlphaTensor-Quantum
Remmy Zen, Maximilian Nägele, Florian Marquardt
Quantum computing has the potential to solve problems that are intractable for classical computers, with possible applications in areas such as drug discovery and high-energy physi…
Tackling Decision Processes with Non-Cumulative Objectives using Reinforcement Learning
Maximilian Nägele, Jan Olle, Thomas Fösel +2
Markov decision processes (MDPs) are used to model a wide variety of applications ranging from game playing over robotics to finance. Their optimal policy typically maximizes the e…
Optimizing ZX-Diagrams with Deep Reinforcement Learning
Maximilian Nägele, Florian Marquardt
ZX-diagrams are a powerful graphical language for the description of quantum processes with applications in fundamental quantum mechanics, quantum circuit optimization, tensor netw…