SOURCE-LINKED INTELLIGENCE

Hierarchical Fourier Approximation for Variational Quantum Distribution Learning

arXiv · AI, language, vision and robotics · article · Sep 5, 2026 · UTC

We study variational quantum distribution learning through a hierarchy of Walsh--Fourier approximations on the Boolean cube. At each level, a selected set of target Fourier coefficients defines a spectral truncation, which is projected onto the probability simplex and used as the target of a quantum circuit Born machine. Parameters learned at one level initialize the next through a warm-start map. We prove an end-to-end expected learning guarantee where the approximation term is determined by the omitted Fourier mass, while a normalized unbiased estimator yields an explicit statistical bound f

Read original source ↗ Open in workspace

recordType

paper

region

Global