Exact Solutions to the Quantum Schrödinger Bridge Problem

• URL: http://arxiv.org/abs/2509.25980v1
• Date: Tue, 30 Sep 2025 09:11:24 GMT
• Title: Exact Solutions to the Quantum Schrödinger Bridge Problem
• Authors: Mykola Bordyuh, Djork-Arné Clevert, Marco Bertolini,
• Abstract summary: The Quantum Schr"odinger Bridge Problem (QSBP) describes the evolution of a process between two arbitrary probability distributions.<n>We show that the resulting evolution equations involve the so-called Bohm (quantum) potential, representing a notion of non-locality in the process.<n>We present a modified algorithm based on a Gaussian Mixture Model framework, and demonstrate its effectiveness across several experimental settings.
• Score: 3.2480327833037226
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The Quantum Schr\"odinger Bridge Problem (QSBP) describes the evolution of a stochastic process between two arbitrary probability distributions, where the dynamics are governed by the Schr\"odinger equation rather than by the traditional real-valued wave equation. Although the QSBP is known in the mathematical literature, we formulate it here from a Lagrangian perspective and derive its main features in a way that is particularly suited to generative modeling. We show that the resulting evolution equations involve the so-called Bohm (quantum) potential, representing a notion of non-locality in the stochastic process. This distinguishes the QSBP from classical stochastic dynamics and reflects a key characteristic typical of quantum mechanical systems. In this work, we derive exact closed-form solutions for the QSBP between Gaussian distributions. Our derivation is based on solving the Fokker-Planck Equation (FPE) and the Hamilton-Jacobi Equation (HJE) arising from the Lagrangian formulation of dynamical Optimal Transport. We find that, similar to the classical Schr\"odinger Bridge Problem, the solution to the QSBP between Gaussians is again a Gaussian process; however, the evolution of the covariance differs due to quantum effects. Leveraging these explicit solutions, we present a modified algorithm based on a Gaussian Mixture Model framework, and demonstrate its effectiveness across several experimental settings, including single-cell evolution data, image generation, molecular translation and applications in Mean-Field Games.

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