Generation via Classical Noise Reuploading

TL;DR

This paper introduces a new quantum generative model that enables direct single-step quantum data generation, improving efficiency and quality over previous multi-step models.

Contribution

It presents a novel paradigm that avoids small post-selection probabilities and simplifies quantum state preparation by directly sampling classical noise.

Findings

Outperforms existing models in generation quality

Enables single-step quantum data generation

Reduces training and state preparation complexity

Abstract

We propose a novel quantum generative model paradigm that fundamentally avoids the issue of extremely small post-selection probabilities present in previous models. Unlike existing methods that require multi-step noise addition and denoising, this paradigm enables direct single-step generation of quantum data, significantly improving generation efficiency while substantially reducing the complexity of training and quantum state preparation. Furthermore, by directly sampling classical noise to generate quantum states, the sampling process becomes easier to implement. Experimental results demonstrate that this paradigm outperforms existing quantum generative models in terms of generation quality.