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 softmax. In the fixed-width recurrent setting, unitary dynamics cannot contract or reset stored distinctions; experimentally, unitary recurrence loses to its retrained one-token control in every tested variant. Introducing learned dissipation, then token-conditioned dissipative gating, yields useful memory beyond one token. The gated model improves test negative log-likelihood by 0.20 +/- 0.03 nats/b...
| Authors | Deniz Yunus Göğüş |
|---|---|
| Primary category | quant-ph |
| Published | 8 October 2026 |
| Last updated at source | 8 October 2026 |
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