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quant-ph2026
Multi-Mode Quantum Annealing for Generative Representation Learning with Boltzmann Priors
Gilhan Kim, Daniel K. Park
Energy-based models provide a natural bridge between statistical physics and machine learning by representing data through structured energy landscapes. Boltzmann machines are a pa…
quant-ph2025★ 1 cited
Diabatic quantum annealing for training energy-based generative models
Gilhan Kim, Ju-Yeon Gyhm, Daniel K. Park
Energy-based generative models, such as restricted Boltzmann machines (RBMs), require unbiased Boltzmann samples for effective training. Classical Markov chain Monte Carlo methods,…