English(EN) Classical Hardness of Learning Functions of Hamiltonians

已证明哈密顿量函数学习的经典硬度

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-03 04:00

研究人员已严格证明了特定哈密顿量函数学习问题的平均情况经典硬度。这项工作建立在 Morohoshi、Nakayama、Manabe 和 Mitarai 提出的一个量子机器学习问题之上，该问题的目标是预测与未知哈密顿量函数相关的量。新的研究结果表明，如果存在这些问题的有效经典学习器，则假设该任务的平均情况硬度，这将意味着一个经典多项式时间算法可以用于分解随机 RSA 模数。 AI

排序理由学术论文，详细介绍了量子机器学习问题的理论硬度证明。[lever_c_demoted from research: ic=1 ai=0.4]

在 arXiv cs.LG 阅读 →

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arXiv cs.LG TIER_1 English(EN) · Sota Hashimoto, Akinori Kawachi · 2026-10-03 04:00

Hamiltonian函数学习的经典困难性

arXiv:2610.01141v1 Announce Type: cross Abstract: Morohoshi, Nakayama, Manabe, and Mitarai proposed a physically motivated quantum machine learning problem in which the goal is to predict quantities of the form $\operatorname{Tr}[f(H)\rho]$ from classical descriptions of a Hamilt…

本文如何被排名

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Hamiltonian函数学习的经典困难性

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