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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2401.03023 |
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| _version_ | 1866915025601429504 |
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| author | de Lejarza, Jorge J. Martínez Cieri, Leandro Grossi, Michele Vallecorsa, Sofia Rodrigo, Germán |
| author_facet | de Lejarza, Jorge J. Martínez Cieri, Leandro Grossi, Michele Vallecorsa, Sofia Rodrigo, Germán |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2401_03023 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Loop Feynman integration on a quantum computer de Lejarza, Jorge J. Martínez Cieri, Leandro Grossi, Michele Vallecorsa, Sofia Rodrigo, Germán High Energy Physics - Phenomenology Quantum Physics 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. |
| title | Loop Feynman integration on a quantum computer |
| topic | High Energy Physics - Phenomenology Quantum Physics |
| url | https://arxiv.org/abs/2401.03023 |