Qubits and Vacuum Amplitudes

TL;DR

This paper explores how quantum computing can be applied to high-energy collider physics, focusing on vacuum amplitudes and high-dimensional function sampling, aiming to enhance simulation and analysis capabilities.

Contribution

It introduces novel quantum algorithms for analyzing multiloop vacuum amplitudes and for sampling high-dimensional functions relevant to collider physics.

Findings

Identification of causal structures in multiloop vacuum amplitudes

Development of quantum methods for high-dimensional function sampling

Progress toward quantum event generators for collider simulations

Abstract

High-energy colliders, such as the Large Hadron Collider (LHC) at CERN, are genuine quantum machines, so, in line with Richard Feynman's original motivation for Quantum Computing, the scattering processes that take place there are natural candidates to be simulated on a quantum system. Potential applications range from quantum machine learning methods for collider data analysis, to faster and more precise evaluations of intricate multiloop Feynman diagrams, more efficient jet clustering, improved simulations of parton showers, and many other tasks. In this work, the focus will be on two specific applications: first, the identification of the causal structure of multiloop vacuum amplitudes, a key ingredient of the Loop-Tree Duality and an area with deep connections to graph theory; and second, the integration and sampling of high-dimensional functions. The latter constitutes a first step toward the realization of a fully fledged quantum event generator operating at high perturbative orders.