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This paper introduces a hybrid generative pipeline that integrates a classical autoencoder with a mixed-state quantum denoising diffusion probabilistic model (MSQuDDPM) to efficiently generate images from high-dimensional classical data. By compressing data into compact latent codes, the method reduces the qubit cost associated with state encoding, allowing for practical application of quantum diffusion models. The approach is validated through MNIST image generation, showcasing its potential as a scalable solution for hybrid quantum-classical generative modeling.
Mixed-state quantum diffusion can effectively bridge the gap between classical data and quantum generative models, enabling efficient image generation with reduced qubit requirements.
Quantum diffusion models provide a physics-consistent route to generative learning by formulating noising and denoising directly on quantum states. However, applying such models to classical high-dimensional data is constrained by the qubit cost of state encoding and the computational burden of simulating large density operators. We propose a scalable hybrid generative pipeline that combines a classical autoencoder for dimensionality reduction with a mixed-state quantum denoising diffusion probabilistic model (MSQuDDPM) operating in the learned latent space. The autoencoder compresses data into compact latent codes that can be embedded into a small-qubit Hilbert space, after which the quantum diffusion model learns a generative distribution over latent density operators and decodes samples back to the original domain. Algorithmically, we simplify the reverse dynamics by predicting an estimate of the clean state $\rho_0$ at timestep $t$ and computing the one-step reverse update via an analytic backward propagation rule, rather than learning an explicit predictor for $\rho_{t-1}$. We demonstrate the proposed approach on MNIST image generation and discuss how mixed-state quantum diffusion can serve as a practical backbone for hybrid quantum--classical generative modeling under realistic qubit budgets.
