Application of a Quantum Search Algorithm to High- Energy Physics Data at the Large Hadron Collider

TL;DR

This paper demonstrates the application of a quantum search algorithm, Grover's Algorithm, to identify rare events in high-energy physics data from CERN's Large Hadron Collider, showcasing the potential of quantum computing in particle physics research.

Contribution

It introduces a novel method of applying Grover's Algorithm to real high-energy physics data, demonstrating its effectiveness on classical simulators and quantum computers.

Findings

Quantum search successfully identifies rare collision events.

Quantum algorithms show promise for analyzing large physics datasets.

Implementation on quantum hardware indicates potential for future high-energy physics applications.

Abstract

We demonstrate a novel method for applying a scientific quantum algorithm - the Grover Algorithm (GA) - to search for rare events in proton-proton collisions at 13 TeV collision energy using CERN's Large Hadron Collider. The search is of an unsorted database from the ATLAS detector in the form of ATLAS Open Data. As indicated by the Higgs boson decay channel $H\rightarrow ZZ^*\rightarrow 4l$, the detection of four leptons in one event may be used to reconstruct the Higgs boson and, more importantly, evince Higgs boson decay to some new phenomena, such as $H\rightarrow ZZ_d \rightarrow 4l$. In searching the dataset for collisions resulting in the detection of four leptons, the study demonstrates the effectiveness and potential of applying quantum computing to high-energy particle physics. Using a Jupyter Notebook, a classical simulation of GA, and multiple quantum computers, each with several qubits, it is demonstrated that this application makes the proper selection in the unsorted dataset. The implementation of the method on several classical simulators and on several of IBM's quantum computers using the IBM Qiskit Open Source Software exhibits the promising prospects of quantum computing in high-energy physics.