Mitigating Barren Plateaus in Quantum Denoising Diffusion Probabilistic Model

ArXi:2512.06695v2 Announce Type: replace Quantum generative models exploit quantum superposition and entanglement to enhance learning efficiency for both classical and quantum data. Recently, inspired by classical diffusion frameworks, the quantum denoising diffusion probabilistic model (QuDDPM) has emerged as a powerful tool for learning correlated noise models, many-body phases, and topological data structure. However, we nstrate that QuDDPM's efficacy is currently restricted to small-scale systems (typically $\le$ 5 qubits