Trainable Quantum-Native Language Models: Dissipative Memory, Capacity Limits, and Hardware Validation

We study byte-level autoregressive language models whose hidden state is a small quantum register, whose transitions are learned token-conditioned quantum channels, and whose next-byte probabilities come directly from Born-rule measurements without a learned classical vocabulary projection or softma