Thermodynamics of classifiers
The study establishes a fundamental error-cost trade-off in information processing by deriving lower bounds for binary classifiers based on Markov processes and quantum dynamics. It shows that when thermodynamic costs like entropy production or dynamical activity are high, the minimum classification error can be lower than random guessing (1/2).