Score Reversal Is Not Free for Quantum Diffusion Models
Not on the map. Why?
Abstract (arXiv)
Classical reverse diffusion is generated by changing the drift at fixed noise. We show that the quantum version of this principle obeys an exact law with a sharp phase boundary. For Gaussian pure-loss dynamics, the canonical model of continuous-variable decoherence, we prove that the unrestricted instantaneous reverse optimum exhibits a noiseless-to-noisy transition: below a critical squeezing-to-thermal ratio, reversal can be noiseless; above it, complete positivity forces irreducible reverse noise whose minimum cost we determine in closed form. The optimal reverse diffusion is uniquely covariance-aligned and simultaneously minimizes the geometric, metrological, and thermodynamic price of reversal. For multimode trajectories, the exact cost is additive in a canonical set of mode-resolved data, and a globally continuous protocol attains this optimum on every mixed-state interval. If a pure nonclassical endpoint is included, the same pointwise law holds for every t>0, but the optimum diverges as 2/t: exact Gaussian reversal of a pure quantum state is dynamically unattainable. These results establish the exact Gaussian benchmark against which any broader theory of quantum reverse diffusion must be measured.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 0 of its 4 references that are in the map. First posted on arXiv 6 Mar 2026.
Builds on
0 of 4 references.
No references here yet.
Built on by
Not cited yet.
Similar, not cited
Close in wording, no citation either way
- 2026The Feedback Hamiltonian is the Score Function: A Diffusion-Model Framework for Quantum Trajectory Reversal
- 2025Squeezed Diffusion Models
- 2025Mitigating Barren Plateaus in Quantum Denoising Diffusion Probabilistic Model
- 2025Continuous-variable Quantum Diffusion Model for State Generation and Restoration
- 2024Quantum Generative Diffusion Model: A Fully Quantum-Mechanical Model for Generating Quantum State Ensemble
- 2023Quantum-Noise-Driven Generative Diffusion Models
- 2026The Score Hamiltonian: Mapping Diffusion Models to Adiabatic Transport
- 2023QonFusion -- Quantum Approaches to Gaussian Random Variables: Applications in Stable Diffusion and Brownian Motion