Quantum-Inspired Stochastic Sampling for Deep Generative Models

Jincheng Zhang

Published Sep 2, 2026• 0 citations

Abstract

Deep generative models, such as Generative Adversarial Networks (GANs), have demonstrated remarkable success in generating realistic data across various domains. However, a fundamental limitation lies in their reliance on traditional stochastic sampling methods, predominantly uniform or Gaussian distributions. These methods often struggle to accurately represent the complex, high-dimensional structure inherent in real-world data. This work introduces a novel approach – Quantum-Inspired Stochastic Sampling – that draws inspiration from quantum mechanics to overcome these limitations. We propose a parameterized "quantum circuit" that simulates superposition and interference, enabling the generation of stochastic samples from distributions that are not constrained by the limitations of classical sampling techniques. The core idea is to map a random seed into a distribution that mirrors the probabilistic behavior observed within quantum systems. We aim to demonstrate that this quantum-inspired approach can lead to improved sample quality and a more accurate representation of complex data distributions within deep generative models. The theoretical framework and methodology are presented, highlighting the potential for this technique to significantly advance the field of generative modeling.

DOI: 10.5281/zenodo.22248167

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