Minimal entropy production due to constraints on rate matrix dependencies in multipartite processes
This work addresses entropy production in constrained multipartite systems, which is incremental as it extends concepts like learning rate and information flow from bipartite to multipartite cases.
The paper tackles the problem of entropy production in multipartite processes with constraints on subsystem interactions, deriving a strictly nonzero lower bound on the minimal achievable entropy production rate based on these constraints.
I consider multipartite processes in which there are constraints on each subsystem's rate matrix, restricting which other subsystems can directly affect its dynamics. I derive a strictly nonzero lower bound on the minimal achievable entropy production rate of the process in terms of these constraints on the rate matrices of its subsystems. The bound is based on constructing counterfactual rate matrices, in which some subsystems are held fixed while the others are allowed to evolve. This bound is related to the "learning rate" of stationary bipartite systems, and more generally to the "information flow" in bipartite systems.