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arXiv 2411.03450cs.LGquant-ph
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变分量子电路在监督学习中的傅里叶分析

Fourier Analysis of Variational Quantum Circuits for Supervised Learning

Fraunhofer IIS(弗劳恩霍夫集成电路研究所)

Marco Wiedmann, Maniraman Periyasamy, Daniel D. Scherer

AI总结:

本文通过傅里叶分析探讨变分量子电路,揭示其频谱受变分参数约束,提出计算电路频谱的算法,并展示如何通过数据傅里叶变换预测最佳拟合电路。

摘要:

VQC可以通过傅里叶分析来理解。已知任何电路架构表示的功能空间可通过截断的傅里叶级数描述。我们证明,该截断傅里叶级数可用的频谱并非完全由电路的编码门决定,因为电路的变分部分可以将某些系数设为零,从而有效移除该频率。到目前为止,我们首次描述了傅里叶系数与变分参数之间的函数依赖关系作为三角多项式。这使得我们可以提供一个计算给定电路精确频谱及其相应傅里叶系数的算法。最后,我们展示通过将数据傅里叶变换与可用频谱进行比较,可以预测给定选项中哪些VQC能够最佳拟合数据。

英文摘要:

VQC can be understood through the lens of Fourier analysis. It is already well-known that the function space represented by any circuit architecture can be described through a truncated Fourier sum. We show that the spectrum available to that truncated Fourier sum is not entirely determined by the encoding gates of the circuit, since the variational part of the circuit can constrain certain coefficients to zero, effectively removing that frequency from the spectrum. To the best of our knowledge, we give the first description of the functional dependence of the Fourier coefficients on the variational parameters as trigonometric polynomials. This allows us to provide an algorithm which computes the exact spectrum of any given circuit and the corresponding Fourier coefficients. Finally, we demonstrate that by comparing the Fourier transform of the dataset to the available spectra, it is possible to predict which VQC out of a given list of choices will be able to best fit the data.

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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