In particle physics, integration methods and Monte Carlo programs play an essential role as they are the central link between theory and experiment. At the Large Hadron Collider (LHC), Monte Carlo simulations are crucial as they simulate all the scattering processes generated in the experiments.

By generating theoretical events according to the underlying distribution, they allow a one-to-one correspondence with experimental events to get insight into elementary interactions.

A recent paper from the CERN QTI's Quantum Computing and Algorithms domain applied a quantum computing algorithm to the problem of integrating elementary-particle cross-sections, showing, for the first time in the context of high-energy physics (HEP), a valid alternative to improve simulation performance of current classical Monte Carlo techniques.

The core algorithm of interest is Quantum Amplitude Estimation (QAE), which was proven to provide a quadratic speedup for the integration of probability distributions, already in other application fields, and specifically in finance.

Starting from two simple, though non-trivial scattering processes, their corresponding probability distributions can be first appropriately loaded on a quantum computer. The encoding of data into quantum states through a quantum circuit is one of the most active areas of research. The authors approached the problem using either quantum Generative Adversarial Networks (GAN) or an exact method. The distributions were then integrated with a devised method extendable to *n* dimension, that make use of the Quantum Amplitude Estimation algorithm, which can manage the arbitrary reduction of the integration domain.

This contribution proves not only that elementary scattering processes data can successfully be loaded onto qubits, consistently with the results of Bravo-Prieto et all, but also that integrations with a quantum computer are accurate at the per-cent level. This makes quantum computing a viable solution for integrating elementary processes in high-energy physics.