3 papers
quant-ph2026
Interpreting Quantum Learning Models via Stochastic Processes
Johannes Fankhauser, Lukas J. Fiderer, Hans J. Briegel
Quantum machine learning models define probabilistic input--output maps through coherent quantum evolution and measurement. While such models can exhibit computational advantages,…
cs.LG2025
The Work Capacity of Channels with Memory: Maximum Extractable Work in Percept-Action Loops
Lukas J. Fiderer, Paul C. Barth, Isaac D. Smith +1
Predicting future observations plays a central role in machine learning, biology, economics, and many other fields. It lies at the heart of organizational principles such as the va…
cs.AI2024
Free Energy Projective Simulation (FEPS): Active inference with interpretability
Joséphine Pazem, Marius Krumm, Alexander Q. Vining +2
In the last decade, the free energy principle (FEP) and active inference (AIF) have achieved many successes connecting conceptual models of learning and cognition to mathematical m…