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English(EN) Bayesian quantum sensing using graybox machine learning

灰盒机器学习提升量子传感器精度

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员开发了一种新颖的量子传感器灰盒建模策略，将基于物理的模型与描述实验缺陷的数据驱动模型相结合。与纯粹的基于物理的模型或完全的机器学习模型相比，这种混合方法在应用于用于磁场估计的单自旋量子传感器时，精度得到了显著提高。灰盒方法在训练数据点少得多的情况下，实现了数量级上更低的均方误差，表明其在各种量子传感平台和实时自适应协议中具有广泛的适用性。 AI

影响 这种混合建模方法有望带来更精确、更高效的量子传感器，从而影响依赖高精度测量的领域。

排序理由 详细介绍量子传感新方法的学术论文。[lever_c_demoted from research: ic=1 ai=0.7]

在 arXiv cs.LG 阅读 →

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灰盒机器学习提升量子传感器精度

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详细介绍量子传感新方法的学术论文。[lever_c_demoted from research: ic=1 ai=0.7]
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报道来源 [1]

  1. arXiv cs.LG TIER_1 English(EN) · Akram Youssry, Stefan Todd, Patrick Murton, Muhammad Junaid Arshad, Nicholas Werren, Alberto Peruzzo, Cristian Bonato ·

    基于灰盒机器学习的贝叶斯量子传感

    arXiv:2601.17465v2 Announce Type: replace-cross Abstract: Quantum sensors offer significant advantages over classical devices in spatial resolution and sensitivity, enabling transformative applications across materials science, healthcare, and beyond. Their practical performance,…

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