Year: 2025
Type: preprint
Abstract: We develop a compositional framework for generalized reversible computing using copy-discard categories and resource theories. We introduce partitioned matrices between partitioned sets as subdistribution matrices which preserve the equivalence relation of its domain. We model computational and physical transformations as subdistribution matrices over the category of sets and partitioned matrices on partitioned sets, respectively. We show that the interactions between the physical and computational transformations are governed by an aggregation functor whose functoriality and monoidality we deduce from general principles of the formal theory of monads. We study the associated copy-discard structures, in particular, general conditions for determinism and partial invertibility. We then define several notions of entropies that we use to state and prove the fundamental theorem of generalized reversible computing.
Source: arXiv (Cornell University)
Authors: Chanavat, Clémence, Srinivasan, Priyaa Varshinee
Institution: Tallinn University of Technology
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
Funders: European Commission, Eesti Teadusagentuur
OpenAlex ID: W7104079456
Cites:
Cited by:
Related to: