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English(EN) Quantum-enhanced Large Language Models on Quantum Hardware via Cayley Unitary Adapters

量子适配器在 IBM 量子硬件上提升 Llama 3.1 LLM 性能

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员开发了一种方法，通过将称为 Cayley Unitary Adapters 的量子电路模块集成到经典大语言模型 (LLM) 中来增强 LLM。该方法在 IBM Quantum System Two 处理器上执行，将 Llama 3.1 8B 模型的困惑度提高了 1.4%。一个较小的模型 SmolLM2 证明，增加这些量子适配器的维度可以提高性能，甚至能够正确回答经典模型无法回答的问题。 AI

影响 展示了量子硬件在超越经典限制方面提升 LLM 性能的潜在途径。

排序理由 详细介绍了一种将量子计算与 LLM 集成的新颖方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

AI 生成摘要 · Google Gemini · 来自 1 个来源。 我们如何撰写摘要 →

量子适配器在 IBM 量子硬件上提升 Llama 3.1 LLM 性能

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详细介绍了一种将量子计算与 LLM 集成的新颖方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.LG TIER_1 English(EN) · Borja Aizpurua, Sukhbinder Singh, Augustine Kshetrimayum, Saeed S. Jahromi, Roman Orus ·

    量子硬件上的量子增强大语言模型通过Cayley Unitary Adapters实现

    arXiv:2605.05914v1 Announce Type: cross Abstract: Large language models (LLMs) have transformed artificial intelligence, yet classical architectures impose a fundamental constraint: every trainable parameter demands classical memory that scales unfavourably with model size. Quant…

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