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arXiv 2401.03023hep-phquant-ph
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量子计算机上的圈费曼图积分

Loop Feynman integration on a quantum computer

Jorge J. Martínez de Lejarza, Leandro Cieri, Michele Grossi, Sofia Vallecorsa, Germán Rodrigo

AI总结:

本文提出新型量子蒙特卡洛积分器 QFIAE,通过引入量子神经网络将多维被积函数分解为傅里叶级数以实现二次加速,首次在量子计算机和模拟器上成功计算单圈费曼积分,验证了方法的有效性及跨领域应用潜力。

摘要:

本研究详细考察了一种新型量子蒙特卡洛积分器(称为 Quantum Fourier Iterative Amplitude Estimation, QFIAE)的性能与优势,首次在近期量子计算机和量子模拟器上数值计算圈费曼积分。为实现二次加速,QFIAE 引入了 Quantum Neural Network (QNN),将多维被积函数高效分解为其傅里叶级数。针对单圈蝌蚪费曼图,我们在真实量子计算机上成功实现了该量子算法,并与解析值取得了合理一致。具有更多外腿的单圈费曼图在量子模拟器上进行了分析。这些结果充分表明该量子算法如何有效估计圈费曼积分,所用方法也可应用于金融、人工智能或其他物理科学等领域。

英文摘要:

This work investigates in detail the performance and advantages of a new quantum Monte Carlo integrator, dubbed Quantum Fourier Iterative Amplitude Estimation (QFIAE), to numerically evaluate for the first time loop Feynman integrals in a near-term quantum computer and a quantum simulator. In order to achieve a quadratic speedup, QFIAE introduces a Quantum Neural Network (QNN) that efficiently decomposes the multidimensional integrand into its Fourier series. For a one-loop tadpole Feynman diagram, we have successfully implemented the quantum algorithm on a real quantum computer and obtained a reasonable agreement with the analytical values. One-loop Feynman diagrams with more external legs have been analyzed in a quantum simulator. These results thoroughly illustrate how our quantum algorithm effectively estimates loop Feynman integrals and the method employed could also find applications in other fields such as finance, artificial intelligence, or other physical sciences.

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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