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About

Felix Wiebe is a researcher at the intersection of quantum computing and underactuated robotics, whose work bridges cutting-edge theory with practical, open-source implementation. His primary research areas include quantum deep reinforcement learning for robotic control and the benchmarking of control algorithms for underactuated systems. Wiebe’s major contribution lies in demonstrating how hybrid quantum-classical setups—specifically parameterized quantum circuits (PQCs)—can be applied to real-world robot navigation tasks, achieving notable results in simulated environments of increasing complexity. His most-cited paper, “Quantum Deep Reinforcement Learning for Robot Navigation Tasks” (2024), has garnered 28 citations, highlighting its impact on the emerging field of quantum robotics. Additionally, Wiebe has made significant strides in open-source robotics with his “Open Source Dual-Purpose Acrobot and Pendubot Platform” (2023, 15 citations), a widely-used benchmarking tool for underactuated control algorithms. His “Torque-limited simple pendulum” toolkit (2022, 9 citations), published in the Journal of Open Source Software, provides an accessible entry point for students and researchers to familiarize themselves with control algorithms. Wiebe’s work is characterized by a commitment to reproducibility and education, making complex topics in quantum and underactuated robotics more approachable for the broader research community.