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arXiv 2306.03481quant-phcs.AIcs.ITcs.LGmath.IT
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纠缠数据在量子机器学习中的过渡作用

Transition Role of Entangled Data in Quantum Machine Learning

Wuhan University(武汉大学);JD Explore Academy(京东探索研究院);Nanyang Technological University(南洋理工大学);University of Sydney(悉尼大学);Peking University(北京大学)

Xinbiao Wang, Yuxuan Du, Zhuozhuo Tu, Yong Luo, Xiao Yuan, Dacheng Tao

AI总结:

该研究建立了利用纠缠数据学习量子动力学的量子NFL定理,揭示纠缠数据对QML模型预测误差的双重效应,为资源受限的早期量子计算机上的QML协议设计提供指导。

摘要:

纠缠是赋能量子计算的资源。近期研究进展凸显了其在学习量子动力学方面的积极作用:将纠缠整合到量子机器学习(QML)模型的量子操作或测量中,可大幅减少达到指定预测误差阈值所需的训练数据量。然而,关于数据中的纠缠程度如何影响模型性能,目前仍缺乏分析层面的理解。本研究通过建立一条利用纠缠数据学习量子动力学的量子没有免费午餐(NFL)定理,填补了这一认知空白。与以往研究结论相反,我们证明纠缠数据对预测误差的影响呈现双重效应,具体取决于允许的测量次数。当测量次数充足时,增加训练数据的纠缠度会持续降低预测误差,或减少达到相同预测误差所需的训练数据规模。反之,当允许的测量次数较少时,使用高纠缠数据可能导致预测误差升高。这些结果为设计先进的QML协议提供了关键指导,尤其适用于专为量子资源有限的早期量子计算机定制的协议。

英文摘要:

Entanglement serves as the resource to empower quantum computing. Recent progress has highlighted its positive impact on learning quantum dynamics, wherein the integration of entanglement into quantum operations or measurements of quantum machine learning (QML) models leads to substantial reductions in training data size, surpassing a specified prediction error threshold. However, an analytical understanding of how the entanglement degree in data affects model performance remains elusive. In this study, we address this knowledge gap by establishing a quantum no-free-lunch (NFL) theorem for learning quantum dynamics using entangled data. Contrary to previous findings, we prove that the impact of entangled data on prediction error exhibits a dual effect, depending on the number of permitted measurements. With a sufficient number of measurements, increasing the entanglement of training data consistently reduces the prediction error or decreases the required size of the training data to achieve the same prediction error. Conversely, when few measurements are allowed, employing highly entangled data could lead to an increased prediction error. The achieved results provide critical guidance for designing advanced QML protocols, especially for those tailored for execution on early-stage quantum computers with limited access to quantum resources.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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