Learning Quantum Data Distribution via Chaotic Quantum Diffusion Model
This work addresses the problem of high implementation costs and sensitivity in quantum generative models for fields like chemoinformatics and quantum physics, offering an incremental improvement in hardware compatibility and robustness.
The paper tackled the challenge of efficiently learning quantum data distributions by proposing a chaotic quantum diffusion model that uses chaotic Hamiltonian time evolution instead of costly circuit-based random unitaries, achieving comparable accuracy to existing methods while reducing implementation overhead on analog quantum hardware.
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.