English(EN) Advantage of Sample Complexity in Quantum PAC Learning Requires Inverse Access to State-Preparation Unitaries

量子 PAC 学习样本复杂度优势需要逆向访问状态制备酉算子

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-01 04:00

一篇新论文探讨了量子 PAC 学习的样本复杂度优势，特别是研究量子计算是否能减少学习预测规则所需的数据。研究表明，仅正向访问量子数据并不能比经典学习方法或从量子数据副本学习提供渐近查询复杂度优势。研究结果强调了对状态制备酉算子进行逆向访问在实现可实现学习环境中已知改进方面的重要作用。 AI

影响阐明了量子在机器学习数据采样方面优势的理论限制和要求。

排序理由阐述量子机器学习理论发现的学术论文。[lever_c_降级自研究：ic=1 ai=1.0]

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arXiv stat.ML TIER_1 English(EN) · Natsuto Isogai, Satoshi Yoshida, Mio Murao · 2026-10-01 04:00

量子PAC学习中样本复杂度优势需要状态制备酉变换的逆访问

arXiv:2609.38403v1 Announce Type: cross Abstract: Whether quantum computation can reduce the amount of data sampled from an unknown probability distribution required to learn a prediction rule is a fundamental question in quantum machine learning. Quantum PAC learning studies thi…