David Wolpert, Santa Fe Institute
The new field of stochastic thermodynamics allows us to analyze the thermodynamic behavior of dynamic systems arbitrarily far from thermal equilibrium, and has produced many powerful theorems concerning phenomena completely absent in traditional statistical physics. However, to date stochastic thermodynamics has (mostly) been applied to systems with only one or two subsystems, and a limited number of degrees of freedom. Here I present some preliminary results concerning stochastic thermodynamics of distributed systems with multiple, heterogenous subsystems. I focus on how the the interaction network among the subsystems affects thermodynamic behavior of the full system. I first present results concerning Bayes nets, then concerning (loop-free) Boolean circuits, and then concerning multipartite processes, in which any of the subsystems may undergo a state transition at any time. These results start to lay the foundation for the theic analysis of distributed computational systems, ranging from brains to concurrent processors to digital circuits.
Gregory Wheeler, Frankfurt School of Finance & Management
The theory of 'imprecise probabilities' -- otherwise known as the theory of lower previsions -- is a generalization of de Finetti's theory of linear previsions (a.k.a., subjective probability theory). The theory of lower previsions stands to the theory of linear previsions similarly as first-order logic stands to propositional logic. That said, first-order logic is weird: you can have syntactically well-formed formula first-order logic that are not semantically interpretable, it is impossible to provide a truth-table semantics for it, and first-order logic is undecidable, for crying out loud. What kind of logic has no effective method for deriving the correct answer?! The theory of lower previsions raises a slew of its own deeply vexing questions. But unlike first-order logic, where questions about its weirdness are largely handled in the classroom, lower prevision weirdness is still primarily argued over in learned journals. This talk makes the case that lower prevision weirdness is ready to be moved to the classroom.
Giovanna Morigi, Universität des Saarlandes
Starting from the famous sentence by Landauer, “Information is physical”, I will discuss how quantum computing is intrinsically different from classical computing in terms of the underlying physical dynamics. I will then argue how the fragility of quantum dynamics to external perturbations, which is a central challenge in quantum computing, could be overcome by taking inspiration from biology.