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arXiv 2602.04072quant-ph

数据验证是量子计算copilot的未来

Data Verification is the Future of Quantum Computing Copilots

Junhao Song, Ziqian Bi, Xinliang Chia, William Knottenbelt, Yudong Cao

AI总结:

本研究提出数据验证在量子计算copilot和AI自动化中的重要性,强调验证作为架构基础的必要性,通过实验表明未验证的LLMs在电路优化中准确率仅为79%。

摘要:

量子程序生成需要一种精度水平,这可能与在大型语言模型(LLMs)推理中进行的统计推理不兼容。幻觉在数学上是不可避免的,且无法通过扩展来解决,这导致了不可行的解决方案。我们主张在量子copilot和AI自动化领域中优先考虑验证的架构是必要的。我们的观点基于三个关键点:经过验证的训练数据使模型能够将精确的约束内化为学习结构,而不是统计近似;验证必须限制生成而不是过滤输出,因为有效的设计占据着指数级缩小的子空间;在物理定律 impose 正确性标准的领域中,需要将验证嵌入到架构中作为基本构件。早期实验显示,没有数据验证的LLMs在电路优化中的最大准确率仅为79%。我们的观点是作为量子计算和AI4Research社区的必要性,呼吁将验证从事后考虑提升到AI4Research的架构基础。

英文摘要:

Quantum program generation demands a level of precision that may not be compatible with the statistical reasoning carried out in the inference of large language models (LLMs). Hallucinations are mathematically inevitable and not addressable by scaling, which leads to infeasible solutions. We argue that architectures prioritizing verification are necessary for quantum copilots and AI automation in domains governed by constraints. Our position rests on three key points: verified training data enables models to internalize precise constraints as learned structures rather than statistical approximations; verification must constrain generation rather than filter outputs, as valid designs occupy exponentially shrinking subspaces; and domains where physical laws impose correctness criteria require verification embedded as architectural primitives. Early experiments showed LLMs without data verification could only achieve a maximum accuracy of 79% in circuit optimization. Our positions are formulated as quantum computing and AI4Research community imperatives, calling for elevating verification from afterthought to architectural foundation in AI4Research.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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