English(EN) LLM-Driven Algorithm Design for Quantum Circuit Synthesis based on Binary Decision Diagrams

LLM驱动框架优化量子电路综合

作者 PulseAugur 编辑部 · [1 个来源] · 2026-09-07 04:00

研究人员开发了QuantumEvo，一个新颖的进化框架，它利用大型语言模型（LLM）来优化二叉决策图（BDD）在量子电路综合中的变量排序。该方法旨在提高综合电路的量子成本（QCC），这是一个比单独的BDD大小更准确的指标。该框架发现的启发式算法HGA-QE修改了遗传算法中的筛选步骤，以更好地与QCC对齐，在基准函数上与现有基线相比显示出具有竞争力的胜率。 AI

影响这项研究可能通过使用LLM来改进算法设计，从而实现更高效的量子电路。

排序理由关于量子电路综合新方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

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Signal score: 42 / 100; Composite score across the factors below. Higher = stronger signal that this story matters right now.
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报道来源 [1]

arXiv cs.AI TIER_1 English(EN) · Yoonju Sim, Federico Berto, Chuanbo Hua, Jinkyoo Park, Changhyun Kwon · 2026-09-07 04:00

基于二叉决策图的LLM驱动量子电路合成算法设计

arXiv:2609.05327v1 Announce Type: new Abstract: Quantum circuits are central to implementing quantum algorithms on quantum devices, where quantum gates must be reversible. Many quantum algorithms rely on Boolean functions, which must therefore be implemented reversibly within qua…

本文如何被排名

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基于二叉决策图的LLM驱动量子电路合成算法设计

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