Learning Quantum Data Distribution via Chaotic Quantum Diffusion Model

TL;DR

This paper introduces a chaotic quantum diffusion model that efficiently generates quantum data distributions using global control, reducing hardware complexity while maintaining high accuracy, thus enhancing quantum generative modeling.

Contribution

The paper proposes a novel chaotic Hamiltonian-based diffusion framework that is hardware-friendly and improves upon existing quantum generative models in efficiency and robustness.

Findings

Achieves comparable accuracy to existing QuDDPMs

Reduces implementation complexity on analog quantum hardware

Enhances trainability and robustness of quantum generative models

Abstract

Generative models for quantum data pose significant challenges but hold immense potential in fields such as chemoinformatics and quantum physics. Quantum denoising diffusion probabilistic models (QuDDPMs) enable efficient learning of quantum data distributions by progressively scrambling and denoising quantum states; however, existing implementations typically rely on circuit-based random unitary dynamics that can be costly to realize and sensitive to control imperfections, particularly on analog quantum hardware. We propose the chaotic quantum diffusion model, a framework that generates projected ensembles via chaotic Hamiltonian time evolution, providing a flexible and hardware-compatible diffusion mechanism. Requiring only global, time-independent control, our approach substantially reduces implementation overhead across diverse analog quantum platforms while achieving accuracy comparable to QuDDPMs. This method improves trainability and robustness, broadening the applicability of quantum generative modeling.