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English(EN) Balancing Expressivity and Learnability in Quantum Kernel Bandit Optimization

量子核带土优化平衡表达能力与可学性

作者 PulseAugur 编辑部 · [2 个来源] ·

研究人员开发了使用量子核的高斯过程带土优化新方法，特别解决了嘈杂中等规模量子（NISQ）时代面临的挑战。该研究侧重于平衡量子核的表达能力与其可学性，而可学性可能因高维和复杂性而受阻。为解决此问题，该团队提出了投影量子核和经典核近似技术，这些技术在降低维度的同时保留了关键的量子特性。这些方法旨在提高样本效率并减少量子原生应用的计算开销。 AI

影响 这项研究可能为量子机器学习应用带来更有效和可扩展的优化技术。

排序理由 该集群包含一篇学术论文，详细介绍了量子核带土优化的一种新方法。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

AI 生成摘要 · Google Gemini · 来自 2 个来源。 我们如何撰写摘要 →

量子核带土优化平衡表达能力与可学性

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该集群包含一篇学术论文，详细介绍了量子核带土优化的一种新方法。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [2]

  1. arXiv cs.LG TIER_1 English(EN) · Yuqi Huang, Vincent Y. F. Tan, Sharu Theresa Jose ·

    量子核赌博优化中的表达性与可学性平衡

    arXiv:2607.01080v1 Announce Type: new Abstract: We investigate Gaussian process (GP) bandit optimization with quantum kernels, assuming the mean reward function lies in the reproducing kernel Hilbert space (RKHS) induced by the quantum kernel. This setting is motivated by NISQ-er…

  2. arXiv cs.LG TIER_1 English(EN) · Sharu Theresa Jose ·

    量子核赌博优化中的表达性与可学性平衡

    We investigate Gaussian process (GP) bandit optimization with quantum kernels, assuming the mean reward function lies in the reproducing kernel Hilbert space (RKHS) induced by the quantum kernel. This setting is motivated by NISQ-era tasks such as quantum control, state preparati…

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