arXiv subjects

Dorian Daimer

Publications and source records attributed to Dorian Daimer.

4 recordsLinked to original sources

arXiv2025

Physical Observers and Quantum Reconstructions

There is a multitude of interpretations of quantum mechanics, but foundational principles are lacking. Relational quantum mechanics views the observer as a physical system, which allows for an unambiguous interpretation as all axioms are purely operational, describing how observers acquire information. The approach, however, is based on the premise that the observer retains only predictive information about the observed system. Here, we justify this premise using the following principle: Physically embedded observers choose information processing strategies that provide them with the option to approach physical limits to the greatest possible extent. Applied to a lower limit on energy dissipation, the principle leads directly to a compact predictive model, thus justifying this core premise of relational quantum mechanics.

quant-ph↗

arXiv2023

Thermodynamically rational decision making under uncertainty

An analytical characterization of thermodynamically rational agent behaviour is obtained for a simple, yet non--trivial example of a ``Maxwell's demon" operating with partial information. Our results provide the first fully transparent physical understanding of a decision problem under uncertainty.

physics.data-an↗

arXiv2023

The physical observer in a Szilard engine with uncertainty

Information engines model ``Maxwell's demon" mechanistically. However, the demon's strategy is pre-described by an external experimenter, and information engines are conveniently designed such that observables contain complete information about variables pertinent to work extraction. In real world scenarios, it is more realistic to encounter partial observability, which forces the physical observer, an integral part of the information engine, to make inferences from incomplete knowledge. Here, we use the fact that an algorithm for computing optimal strategies can be directly derived from maximizing overall engine work output. For a simple binary decision problem, we discover interesting optimal strategies that differ notably from naive coarse graining. They inspire a model class of simple, yet compelling, parameterized soft partitionings of the observable.

cond-mat.stat-mech↗

arXiv2021

Partially Observable Szilard Engines

Leo Szilard pointed out that Maxwell's demon can be replaced by machinery, thereby laying the foundation for understanding the physical nature of information. Szilard's information engine still serves as a canonical example after almost a hundred years, despite recent significant growth of the area. The role the demon plays can be reduced to mapping observable data to a meta-stable memory, which is utilized to extract work. While Szilard showed that the map can be implemented mechanistically, it was chosen a priori. The choice of how to construct a meaningful memory constitutes the demon's intelligence. Recently, it was shown that this can be automated as well. To that end, generalized, partially observable information engines were introduced, providing a basis for understanding the physical nature of information processing. Partial observability is ubiquitous in real world systems which have limited sensor types and information acquisition bandwidths. Generalized information engines can run work extraction at a different temperature, T' > T, from the memory forming process. This enables the combined treatment of heat engines and information engines. We study the physical characteristics of intelligent observers by introducing a canonical model that displays physical richness, despite its simplicity. A minor change to Szilard's engine - inserting the divider at an angle - results in a family of partially observable Szilard engines. Their analysis shows how the demon's intelligence can be automated. For each angle, and for each value of T'/T, an optimal memory can be found, enabling the engine to run with minimal dissipation. Those optimal memories are probabilistic maps, computed algorithmically. We discuss how they can be implemented with a simple physical system, characterize their performance, and compare their quality to that of naive, deterministic quantizations of the observable.

cond-mat.stat-mech↗