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Exact Solutions to the Quantum Schr"odinger Bridge Problem

arXiv 2509.25980 ↗First posted 30 Sep 2025cs.LG math-ph math.MPcited by 0

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Abstract (arXiv)

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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References · 24

  1. 2020Denoising Diffusion Probabilistic Modelscited by 21,076
  2. 2020Score-Based Generative Modeling through Stochastic Differential Equationscited by 9,122
  3. 2015Deep Unsupervised Learning using Nonequilibrium Thermodynamicscited by 7,058
  4. 2013Sinkhorn Distances: Lightspeed Computation of Optimal Transportation Distancescited by 2,316
  5. 2021Diffusion Schr"odinger Bridge with Applications to Score-Based Generative Modelingcited by 602
  6. 2013A survey of the Schr"odinger problem and some of its connections with optimal transportcited by 292
  7. 2020Scalable Gradients for Stochastic Differential Equationscited by 271
  8. 2021Likelihood Training of Schr"odinger Bridge using Forward-Backward SDEs Theorycited by 219
  9. 2021A Variational Perspective on Diffusion-Based Generative Models and Score Matchingcited by 183
  10. 2020TrajectoryNet: A Dynamic Optimal Transport Network for Modeling Cellular Dynamicscited by 160
  11. 2020OT-Flow: Fast and Accurate Continuous Normalizing Flows via Optimal Transportcited by 143
  12. 2021Solving Schr"odinger Bridges via Maximum Likelihoodcited by 141
12 more
  1. 2019A Machine Learning Framework for Solving High-Dimensional Mean Field Game and Mean Field Control Problemscited by 97
  2. 2022The Schr"odinger Bridge between Gaussian Measures has a Closed Formcited by 63
  3. 2022Deep Generalized Schr"odinger Bridgecited by 47
  4. 2020Alternating the Population and Control Neural Networks to Solve High-Dimensional Stochastic Mean-Field Gamescited by 35
  5. 2020Entropy-Regularized 2-Wasserstein Distance between Gaussian Measurescited by 32
  6. 2021Averaging on the Bures-Wasserstein manifold: dimension-free convergence of gradient descentcited by 32
  7. 2023Light Schr"odinger Bridgecited by 18
  8. 2021Density control of interacting agent systemscited by 13
  9. 2025Diffusion Generative Modeling on Lie Group Representationscited by 8
  10. 2022Neural Lagrangian Schr"odinger Bridge: Diffusion Modeling for Population Dynamicscited by 7
  11. 2018Traversing the Schroedinger Bridge strait: Robert Fortet's marvelous proof reduxcited by 6
  12. 2022Explaining, Evaluating and Enhancing Neural Networks' Learned Representationscited by 6

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