AI RESEARCH
Multi-Mode Quantum Annealing for Variational Autoencoders with General Boltzmann Priors
arXiv CS.LG
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ArXi:2604.00919v1 Announce Type: cross Variational autoencoders (VAEs) learn compact latent representations of complex data, but their generative capacity is fundamentally constrained by the choice of prior distribution over the latent space. Energy-based priors offer a principled way to move beyond factorized assumptions and capture structured interactions among latent variables, yet