Learning Quantum Data Distribution via Chaotic Quantum Diffusion Model

2026-02-25

AI Summary

This work proposes a quantum diffusion framework based on time evolution under chaotic Hamiltonians to address the limitations of existing quantum generative models, which rely on complex, error-sensitive random quantum circuits that are challenging to implement efficiently on near-term devices. By leveraging global, time-invariant control, the proposed approach performs noise addition and removal directly in the quantum state space to learn target data distributions, eliminating the need for conventional random circuits. This design substantially reduces hardware control complexity and resource overhead while enhancing model trainability and robustness. Empirical evaluations across multiple simulated quantum platforms demonstrate that the method achieves generation accuracy comparable to QuDDPM, with markedly improved hardware compatibility and practicality for near-term quantum applications.

Technology Category

• Machine Learning: Quantum Machine Learning
• Computer Vision: Diffusion Models for Vision
• Search and Optimization: Learning to Search

Application Category

• Economics, Online Markets and Human Computation: LLM based quality controls for crowd work
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

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.

Problem

• quantum generative modeling
• quantum data distribution
• analog quantum hardware
• diffusion models
• control imperfections

Innovation

• chaotic quantum diffusion
• quantum generative modeling
• Hamiltonian time evolution
• analog quantum hardware
• projected ensembles