The Quantum Trellis: A classical algorithm for sampling the parton shower with interference effects
Abstract
Simulations of high-energy particle collisions, such as those used at the Large Hadron Collider, are based on quantum field theory; however, many approximations are made in practice. For example, the simulation of the parton shower, which gives rise to objects called `jets', is based on a semi-classical approximation that neglects various interference effects. While there is a desire to incorporate interference effects, new computational techniques are needed to cope with the exponential growth in complexity associated to quantum processes. We present a classical algorithm called the quantum trellis to efficiently compute the un-normalized probability density over N-body phase space including all interference effects, and we pair this with an MCMC-based sampling strategy. This provides a potential path forward for classical computers and a strong baseline for approaches based on quantum computing.
Cite
@article{arxiv.2112.12795,
title = {The Quantum Trellis: A classical algorithm for sampling the parton shower with interference effects},
author = {Sebastian Macaluso and Kyle Cranmer},
journal= {arXiv preprint arXiv:2112.12795},
year = {2021}
}
Comments
9 pages, 4 figures, Machine Learning and the Physical Sciences 2021, https://github.com/SebastianMacaluso/ClusterTrellis