English(EN) Reinforcement Learning for Adaptive Composition of Quantum Circuit Optimisation Passes

强化学习优化量子电路过程

作者 PulseAugur 编辑部 · [1 个来源] · 2026-11-06 05:00

研究人员开发了一种强化学习代理，能够组合量子电路优化过程的序列。与默认序列相比，该代理在减少两比特门方面表现出显著的改进，平均移除了 57.7% 的门。该方法将优化过程的设计与其最佳应用分开，有可能促进这两个领域的创新。 AI

影响这项研究可能通过自动化量子电路的优化来提高量子计算的效率。

排序理由学术论文，详细介绍了强化学习的新颖应用。[lever_c_demoted from research: ic=1 ai=1.0]

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

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arXiv cs.LG TIER_1 English(EN) · Daniel Mills, Ifan Williams, Jacob Swain, Gabriel Matos, Enrico Rinaldi, Alexander Koziell-Pipe · 2026-11-06 05:00

用于量子电路优化通道自适应组合的强化学习

arXiv:2601.21629v2 Announce Type: replace-cross Abstract: Many quantum software development kits provide a suite of circuit optimisation passes. These passes have been highly optimised and tested in isolation. However, the order in which they are applied is left to the user, or e…