Multi-Mode Quantum Annealing for Generative Representation Learning with Boltzmann Priors
Energy-based models provide a natural bridge between statistical physics and machine learning by representing data through structured energy landscapes. Boltzmann machines are a particularly compelling class of such models for capturing complex interactions among latent variables, but their use in modern generative learning has been limited by the classical intractability of sampling from general (non-restricted) Boltzmann distributions. Here we develop a quantum-annealing-based framework that enables variational autoencoders with general Boltzmann priors. The framework employs three complementary annealing modes tailored to different stages of learning and deployment: diabatic quantum an...
| Authors | Gilhan Kim, Daniel K. Park |
|---|---|
| Primary category | quant-ph |
| Published | 1 April 2026 |
| Last updated at source | 18 May 2026 |
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