Leveraging Quantum Annealer to identify an Event-topology at High Energy Colliders

TL;DR

This paper proposes a quantum annealer-based method to efficiently identify event-topologies in high-energy collider data, significantly reducing computational complexity and doubling accuracy over traditional approaches.

Contribution

It introduces a novel quantum annealer approach for event-topology identification, enabling polynomial-time complexity and improved accuracy in analyzing collider data.

Findings

Achieves over twofold improvement in true event-topology detection.

Reduces computational complexity from exponential to polynomial.

Demonstrates the effectiveness of quantum annealing in high-energy physics data analysis.

Abstract

With increasing energy and luminosity available at the Large Hadron collider (LHC), we get a chance to take a pure bottom-up approach solely based on data. This will extend the scope of our understanding about Nature without relying on theoretical prejudices. The required computing resource, however, will increase exponentially with data size and complexities of events if one uses algorithms based on a classical computer. In this letter we propose a simple and well motivated method with a quantum annealer to identify an event-topology, a diagram to describe the history of particles produced at the LHC. We show that a computing complexity can be reduced significantly to the order of polynomials which enables us to decode the "Big" data in a very clear and efficient way. Our method achieves significant improvements in finding a true event-topology, more than by a factor of two compared to a conventional method.