Skip to content
DraconiticResearch
Search Sources API
203,234 studies31 sourcesUpdated 16 min ago
PreprintCC BY 4.0arXiv

Reverse quantum state diffusion from differential geometry

Abstract

Quantum state diffusion unravels a Lindblad master equation into stochastic trajectories of pure states. The ensemble of trajectories is described by a probability density on the manifold of pure states, which contains much more information than the density matrix, its first moment. We derive the time reversal of this diffusion. Writing the reversal of a general diffusion on a manifold in Stratonovich form, and passing through the manifold of pure states, we obtain a backward stochastic differential equation for the state vector, valid for any Hilbert space dimension and any Lindbladian. The backward stochastic equation provides a physical, nonlinear unravelling of the inverse Lindbladian that holds only for the ensemble from which it was constructed. Following score-based generative models, we then use the backward equation to generate a new ensemble close to the original. For the depolarizing channel the unravelling is a Brownian motion on complex projective space, so the uniform distribution of pure states is its stationary law in any dimension. Starting the backward equation from that prior, we bound the error of the resulting distribution compared to the original one in different settings: exact and learned scores, continuous and discrete time.

Related

  1. Reverse Quantum Annealing for Ground-State Energy Calculation in Molecule
    Pradyot Pritam Sahoo
    arXivPreprintCC BY 4.0Full textPhysical chemistryAtomic, molecular & optical physicsarXiv
  2. Reverse quantum aging: Restoring rhythmicity from quantum aging state
    Bulti Paul, Biswabibek Bandyopadhyay, Tanmoy Banerjee
    arXivPreprintCC BY 4.0Full textApplied physics & instrumentationQuantum physics & quantum informationarXiv
  3. Bell Test and Quantum State-Space Geometry
    Michael Siomau
    arXivPreprintCC BY 4.0Full textQuantum physics & quantum informationarXiv
  4. A deterministic polynomial chaos Galerkin method for non-Markovian quantum state diffusion
    Quanhui Zhu, Zhenning Cai
    arXivPreprintCC BY 4.0Full textQuantum physics & quantum informationarXiv
  5. A Design Space Study of Density Matrix Parameterizations for Diffusion-Based Quantum State Tomography
    Shuangu Chang
    arXivPreprintCC BY 4.0Full textMathematical physicsQuantum physics & quantum informationarXiv
BibTeX
@misc{vu2026reverse,
  title = {{Reverse quantum state diffusion from differential geometry}},
  author = {Dinh-Long Vu and Patrick Rebentrost},
  howpublished = {arXiv},
  year = {2026},
  month = sep,
  doi = {10.48550/arxiv.2609.39861},
  eprint = {2609.39861},
  archivePrefix = {arXiv},
  url = {https://arxiv.org/abs/2609.39861},
  note = {CC BY 4.0},
}
RIS
TY  - UNPB
TI  - Reverse quantum state diffusion from differential geometry
AU  - Dinh-Long Vu
AU  - Patrick Rebentrost
PY  - 2026
DA  - 2026/09/30
PB  - arXiv
DO  - 10.48550/arxiv.2609.39861
UR  - https://arxiv.org/abs/2609.39861
AB  - Quantum state diffusion unravels a Lindblad master equation into stochastic trajectories of pure states. The ensemble of trajectories is described by a probability density on the manifold of pure states, which contains much more information than the density matrix, its first moment. We derive the time reversal of this diffusion. Writing the reversal of a general diffusion on a manifold in Stratonovich form, and passing through the manifold of pure states, we obtain a backward stochastic differential equation for the state vector, valid for any Hilbert space dimension and any Lindbladian. The backward stochastic equation provides a physical, nonlinear unravelling of the inverse Lindbladian that holds only for the ensemble from which it was constructed. Following score-based generative models, we then use the backward equation to generate a new ensemble close to the original. For the depolarizing channel the unravelling is a Brownian motion on complex projective space, so the uniform distribution of pure states is its stationary law in any dimension. Starting the backward equation from that prior, we bound the error of the resulting distribution compared to the original one in different settings: exact and learned scores, continuous and discrete time.
ER  -