新框架加速量子神经网络架构的优化搜索

作者 PulseAugur 编辑部 · [1 个来源] · 2026-05-28 04:00

研究人员开发了一个名为 MZeQAS 的新框架，用于高效搜索变分量子算法（VQA）的最优架构。该方法利用基于量子神经切线核（Quantum Neural Tangent Kernel）的零样本代理模型，无需完全训练即可估算候选电路的性能，从而显著降低了计算成本。MZeQAS 将这种基于代理的估算与蒙特卡洛树搜索（Monte Carlo Tree Search）相结合，以发现高性能的 VQA 架构，在近期的量子设备上，其效率和解决方案质量均优于现有方法。 AI

排序理由这是一篇研究论文，详细介绍了一种优化量子算法的新方法。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

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arXiv cs.LG TIER_1 English(EN) · Tung Dao, Son Tran, Huynh Thi Thanh Binh · 2026-05-28 04:00

零样本量子神经网络架构搜索

arXiv:2605.27410v1 Announce Type: cross Abstract: Variational Quantum Algorithms (VQAs) are a leading approach to exploiting near-term quantum hardware, leveraging parameterized quantum circuits and classical optimization to achieve advantage. Despite their promise, the practical…

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零样本量子神经网络架构搜索

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