Austin L. Huang

Spectral Born machines: classically trainable quantum generative models for discrete data

This work presents spectral Born machines, a class of quantum generative models that results from viewing and generalizing the class of IQP Born machines through the lens of group Fourier analysis, and suggests that highly over-parameterized spectral Born machines may be immune to overfitting, even in strongly data-scarce regimes.