Year: 2026
Type: preprint
Abstract: Diffusion sampling is related to imaginary-time quantum annealing through a forward/backward Schrödinger equation pair and its one-sided counterdiabatic connection. For a smooth, gapped Score-Hamiltonian path, the exact connection transports the target density at any evolution time. We apply quantum-annealing performance analysis to residuals from approximating this connection with a trained score. If the first $r$ terminal schedule derivatives vanish, the squared Hellinger error scales as $T^{-2r-2}$, with a prefactor determined by the terminal learning residual and inverse gaps. Direct two-dimensional imaginary-time evolution with one fixed neural score reproduces these powers and prefactors. Terminal schedule design thus suppresses the distributional effect of imperfect score learning without retraining the network.
Source: arXiv (Cornell University)
Author: Ohzeki, Masayuki
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7212007768