Spin coherent states and stochastic hybrid path integrals

TL;DR

This paper introduces a new path integral approach using coherent spin states for stochastic hybrid systems, enabling analysis beyond weak noise regimes and connecting to large deviation principles.

Contribution

It develops a novel path integral representation for stochastic hybrid systems using coherent spin states, extending analysis capabilities beyond weak noise conditions.

Findings

Derives Langevin equations in the semi-classical limit.

Shows equivalence of the path integral to Doi-Peliti operator representation in weak noise limit.

Links the action functional to a large deviation principle.

Abstract

Stochastic hybrid systems involve a coupling between a discrete Markov chain and a continuous stochastic process. If the latter evolves deterministically between jumps in the discrete state, then the system reduces to a piecewise deterministic Markov process (PDMP). Well known examples include stochastic gene expression, voltage fluctuations in neurons, and motor-driven intracellular transport. In this paper we use coherent spin states to construct a new path integral representation of the probability density functional for stochastic hybrid systems, which holds outside the weak noise regime. We use the path integral to derive a system of Langevin equations in the semi-classical limit, which extends previous diffusion approximations based on a quasi-steady-state reduction. We then show how in the weak noise limit the path integral is equivalent to an alternative representation that was previously derived using Doi-Peliti operators. The action functional of the latter is related to a large deviation principle for stochastic hybrid systems.