Dorian Daimer, "Thermodynamics of Physical Observers"

Friday Mar 7, 2025, 3:00 PM → 5:00 PM Pacific/Honolulu

Room 112 (Watanabe Hall)

Observers are not abstract entities but physical systems subject to physical laws and limitations. We investigate the thermodynamics of physical observers, focusing on how physical limits shape intelligent information processing and decision-making under uncertainty. Two model classes of generalized, partially observable information engines are introduced and analyzed. They extend Szilárd's classic thought experiment to settings where the observer must infer relevant information from available data. Observers memorize information and use it to take actions. A simple physical memory model is introduced and the thermodynamic costs of different encoding strategies are analyzed. Thermodynamically rational encodings maximize the net engine work output. They are probabilistic in general and outperform naive coarse graining based encodings. Through analytical and numerical studies, physical codebooks for rational decision-making are developed from a purely physical perspective, characterizing thermodynamically optimal decision-making strategies in two classes of binary decision problems under uncertainty. By establishing a mapping from abstract binary decision problems to partially observable Szilárd engines, the physical framework becomes widely applicable, intimately linking decision theory and thermodynamics. Extending the analysis of physical observers to quantum systems requires a description in terms of quantum thermodynamics. This is not a straightforward extension, as important thermodynamic concepts such as work and heat are not uniquely defined in quantum thermodynamics. Two definitions of thermodynamic work are compared for qubit systems and it is shown that they only agree under specific conditions. Their comparison reveals the excess work costs required for perfect preparation of qubit states, with possible implications for high fidelity information processing in quantum systems. 
This research opens the door to a first-principles approach to understanding information processing, learning, and decision-making in physical systems, both biological and artificial. By incorporating the observer into the thermodynamic analysis, it provides a novel perspective on the interplay between physics, information processing, and decision-making under uncertainty.