Quantum Fourier integration of decay rates at next-to-leading order

Abstract

We discuss the use of quantum neural networks (QNNs) as a tool for integrating decay rates at next-to-leading order (NLO) in perturbation theory. Data-encoding quantum circuits admit a truncated Fourier-series representation, enabling a trained QNN to approximate a target integrand and provide the coefficients subsequently integrated with Quantum Fourier Iterative Amplitude Estimation (QFIAE). The causal-unitary formulation of the Loop-Tree Duality (LTD) combines virtual and real contributions into locally finite integrands suitable for this representation. We consider H→ qq̅(g), γ^*→ qq̅(g) and Φ→ϕϕ(ϕ), comparing quantum-simulator and quantum-hardware results with analytic NLO predictions. The study illustrates the potential of Fourier-based QNN integration for high-energy physics.

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BibTeX RIS

BibTeX

@misc{renteriaestrada2026quantum,
  title = {{Quantum Fourier integration of decay rates at next-to-leading order}},
  author = {David F. Rentería-Estrada},
  howpublished = {arXiv},
  year = {2026},
  month = nov,
  doi = {10.48550/arxiv.2611.08204},
  eprint = {2611.08204},
  archivePrefix = {arXiv},
  url = {https://arxiv.org/abs/2611.08204},
  note = {CC BY 4.0},
}

RIS

TY  - UNPB
TI  - Quantum Fourier integration of decay rates at next-to-leading order
AU  - David F. Rentería-Estrada
PY  - 2026
DA  - 2026/11/05
PB  - arXiv
DO  - 10.48550/arxiv.2611.08204
UR  - https://arxiv.org/abs/2611.08204
AB  - We discuss the use of quantum neural networks (QNNs) as a tool for integrating decay rates at next-to-leading order (NLO) in perturbation theory. Data-encoding quantum circuits admit a truncated Fourier-series representation, enabling a trained QNN to approximate a target integrand and provide the coefficients subsequently integrated with Quantum Fourier Iterative Amplitude Estimation (QFIAE). The causal-unitary formulation of the Loop-Tree Duality (LTD) combines virtual and real contributions into locally finite integrands suitable for this representation. We consider H→ qq̅(g), γ^*→ qq̅(g) and Φ→ϕϕ(ϕ), comparing quantum-simulator and quantum-hardware results with analytic NLO predictions. The study illustrates the potential of Fourier-based QNN integration for high-energy physics.
ER  -

Abstract

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