Trainable Quantum-Native Language Models: Dissipative Memory, Capacity Limits, and Hardware Validation
Read the original on arXiv Machine Learning →The paper introduces byte‑level autoregressive language models that use a small quantum register as hidden state, with token‑conditioned quantum channels and Born‑rule measurements providing next‑byte probabilities. Unitary dynamics alone fail to retain memory beyond one token, but adding learned dissipative gating improves test negative log‑likelihood and key‑value recall up to a limited capacity. Experiments on IBM Heron hardware show measurable quantum memory signals and autoregressive byte generation, though the models remain classically simulable and do not outperform classical n‑gram baselines.
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