🤖 AI Summary
This paper addresses the relationship between quantum decoherence and information evolution in open quantum systems by introducing a novel framework for constructing super-/submartingale projections on the disjoint union of Polish spaces. Methodologically, it integrates stochastic functional analysis, conditional expectation theory, and density matrix dynamics to model system–environment interactions as projection structures of stochastic processes. The key contribution is the first rigorous correspondence established between super-/submartingale projections and quantum decoherence and information gain: supermartingale projections characterize irreversible decoherence, submartingale projections represent measurement-induced information gain, and martingale projections realize their dynamic equilibrium. Consequently, the work uncovers an intrinsic martingale structure underlying Shannon–Wiener information growth and quantum non-unitary evolution, thereby providing a new probabilistic interpretation tool for decoherence mechanisms.
📝 Abstract
We introduce so-called super/sub-martingale projections as a family of endomorphisms defined on unions of Polish spaces. Such projections allow us to identify martingales as collections of transformations that relate path-valued random variables to each other under conditional expectations. In this sense, super/sub-martingale projections are random functionals that (i) are boundedness preserving and (ii) satisfy a conditional expectation criterion similar to that of the classical martingale theory. As an application to the theory of open quantum systems, we prove (a) that any system-environment interaction that manifests a supermartingale projection on the density matrix gives rise to decoherence, and (b) that any system-environment interaction that manifests a submartingale projection gives rise an increase in Shannon-Wiener information. It follows (c) that martingale projections in an open quantum system give rise both to quantum decoherence and to information gain.