Schr\"{o}dinger Bridge with Quadratic State Cost is Exactly Solvable
Alexis M.H. Teter, Wenqing Wang, and Abhishek Halder

TL;DR
This paper introduces a regularized Schr"{o}dinger bridge with quadratic cost, deriving a closed-form solution for its Markov kernel, which is applicable even for non-Gaussian endpoints, advancing the field of stochastic optimal control.
Contribution
It presents a novel regularized Schr"{o}dinger bridge model with an explicit solution for its Markov kernel, extending solvability beyond Gaussian endpoints.
Findings
Closed-form Markov kernel for the regularized Schr"{o}dinger bridge derived.
Solution recovers the heat kernel in the limit of vanishing regularization.
Connects the model with exactly solvable quantum mechanics models.
Abstract
Schr\"{o}dinger bridge is a diffusion process that steers a given distribution to another in a prescribed time while minimizing the effort to do so. It can be seen as the stochastic dynamical version of the optimal mass transport, and has growing applications in generative diffusion models and stochastic optimal control. {\black{We say a Schr\"{o}dinger bridge is ``exactly solvable'' if the associated uncontrolled Markov kernel is available in closed form, since then the bridge can be numerically computed using dynamic Sinkhorn recursion for arbitrary endpoint distributions with finite second moments.}} In this work, we propose a regularized variant of the Schr\"{o}dinger bridge with a quadratic state cost-to-go that incentivizes the optimal sample paths to stay close to a nominal level. Unlike the conventional Schr\"{o}dinger bridge, the regularization induces a state-dependent rate…
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Taxonomy
TopicsOptical Network Technologies · Photonic and Optical Devices · Quantum optics and atomic interactions
MethodsDiffusion
