Quantum-JEPA: Quantum Born Priors for Latent-Action JEPA World Models

Quantum-JEPA introduces a latent-action JEPA framework utilizing a conditional quantum Born prior to model uncertainty in future representations. By using circuit measurements for binary latent actions, the model learns distinct continuations on multimodal tasks via a sampled energy objective. This development aligns with the broader convergence of quantum mechanics and deep neural networks. As classical silicon hardware faces physical limitations and energy barriers, Quantum Machine Learning aims to redefine AI by operating at the atomic scale.