English(EN) Learning ground state observables from quantum computing experiments

量子计算数据训练神经网络进行物理预测

作者 PulseAugur 编辑部 · [1 个来源] · 2026-06-16 04:00

研究人员演示了在海森堡XXZ模型的实验量子数据上应用机器学习的实际效果，使用了多达115个量子比特。通过在期望值和相关性数据上训练神经网络，他们能够准确预测未见过哈密顿量参数下的可观测量，甚至接近相边界。这项工作展示了量子处理器为机器学习模型生成数据的潜力，其能力超越了经典近似方法。 AI

影响展示了一种利用量子计算为机器学习生成数据的新颖方法，有望加速物理学领域的科学发现。

排序理由学术论文，详细介绍了新的研究方法和实验结果。[lever_c_demoted from research: ic=1 ai=1.0]

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arXiv cs.LG TIER_1 English(EN) · Ben Jaderberg, Freya Shah, Minjun Jeon, M. Emre Sahin, Christa Zoufal, Kunal Sharma · 2026-06-16 04:00

从量子计算实验中学习基态可观测量

arXiv:2606.15983v1 Announce Type: cross Abstract: Recent theoretical progress has established conditions under which machine learning models can efficiently predict ground-state properties of gapped local Hamiltonians when trained on quantum-generated data. Previous experimental …