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arXiv 2302.03389quant-ph

具有量子电路的多维傅里叶级数

Multidimensional Fourier series with quantum circuits

Berta Casas, Alba Cervera-Lierta

AI总结:

本文研究了生成多维傅里叶级数的各类量子电路ansatz的表达能力,发现单qudit模型表示能力受限,但通过增加qudits或提高局部维度扩大Hilbert空间可满足自由度要求并确保模型通用性。

摘要:

量子机器学习旨在将机器学习与量子计算相结合。近年来,该领域已成为一个活跃的研究领域,有望为经典机器学习问题带来新见解。该领域的挑战之一是探索参数化量子电路的表达能力,及其像经典神经网络那样成为通用函数逼近器的能力。最近的工作表明,使用量子监督学习模型可以拟合任意一维傅里叶级数,从而证明了其通用性。然而,针对多维函数的模型尚未在同等细节层面上得到探索。在本研究中,我们研究了生成多维傅里叶级数的各类电路ansatz(拟设)的表达能力。我们发现,对于某些ansatz,拟合此类函数所需的自由度增长速度比电路生成的Hilbert空间中可用自由度的增长速度更快。例如,单qudit模型在表示任意多维傅里叶级数方面能力有限。尽管如此,我们表明可以通过使用更多qudits或更高的局部维度来扩大电路的Hilbert空间,以满足自由度要求,从而确保模型的通用性。

英文摘要:

Quantum machine learning is the field that aims to integrate machine learning with quantum computation. In recent years, the field has emerged as an active research area with the potential to bring new insights to classical machine learning problems. One of the challenges in the field is to explore the expressibility of parametrized quantum circuits and their ability to be universal function approximators, as classical neural networks are. Recent works have shown that with a quantum supervised learning model, we can fit any one-dimensional Fourier series, proving their universality. However, models for multidimensional functions have not been explored in the same level of detail. In this work, we study the expressibility of various types of circuit ansatzes that generate multidimensional Fourier series. We found that, for some ansatzes, the degrees of freedom required for fitting such functions grow faster than the available degrees in the Hilbert space generated by the circuits. For example, single-qudit models have limited power to represent arbitrary multidimensional Fourier series. Despite this, we show that we can enlarge the Hilbert space of the circuit by using more qudits or higher local dimensions to meet the degrees of freedom requirements, thus ensuring the universality of the models.

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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