Do Quantum Models Scale Like LLMs?
Read the original on arXiv Machine Learning →The paper investigates neural scaling laws for RydbergGPT, an autoregressive transformer trained on qubit measurement data from Rydberg atom arrays. Near a critical point in the quantum system, the transformer’s loss scales with dataset size following a power‑law with a loss‑floor correction, whereas this relationship weakens away from criticality. By comparing entropy‑normalized mutual‑information two‑point functions of Rydberg data and natural‑language corpora, the authors find that near‑critical statistics resemble natural language more closely, suggesting that multi‑scale dependence underlies stable neural scaling and that scaling behaviour depends on the model–data pair.
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