pith. sign in

Stochastic Schr\"odinger Equations for Quantum Reverse Diffusion

1 Pith paper cite this work. Polarity classification is still indexing.

1 Pith paper citing it
abstract

The ensemble-averaged dynamics of open quantum systems are typically irreversible. We show that this irreversibility need not hold at the level of individually monitored quantum trajectories. Our main results are analytical stochastic Schr\"odinger equations for quantum reverse diffusion, along with corresponding stochastic master equations. These equations describe the exact and approximate stochastic reverse processes for continuously monitored Pauli channels, including time-dependent depolarizing noise. We show that the reverse processes generalize the forward dynamics by combining the noise effects of the forward processes with an additional stochastic drift that dynamically steers a quantum state back to its initial configuration. Consequently, the exact reverse stochastic Schr\"odinger equations admit closed-form solutions that can be implemented in real-time without the need for variational techniques. Our findings establish an analytical framework for quantum state recovery, noise-resilient quantum gates, quantum generative modelling, quantum tomography via forward-reverse cycles, and potential paradigms for quantum error correction based on reverse diffusion.

fields

quant-ph 1

years

2026 1

verdicts

UNVERDICTED 1

clear filters

representative citing papers

Generating quantum ensembles via reverse-time quantum diffusions

quant-ph · 2026-06-02 · unverdicted · novelty 8.0

The paper establishes a reverse-time quantum diffusion framework that generates complex quantum ensembles from simple distributions by deriving and learning a feedback Hamiltonian from forward trajectory data.

citing papers explorer

Showing 1 of 1 citing paper after filters.

  • Generating quantum ensembles via reverse-time quantum diffusions quant-ph · 2026-06-02 · unverdicted · none · ref 13 · internal anchor

    The paper establishes a reverse-time quantum diffusion framework that generates complex quantum ensembles from simple distributions by deriving and learning a feedback Hamiltonian from forward trajectory data.