# A pattern recognition algorithm for quantum annealers

## TL;DR

This paper proposes a novel pattern recognition algorithm for quantum annealers, representing the problem as a QUBO to leverage quantum computing for particle tracking in high-energy physics, achieving competitive performance at current densities.

## Contribution

It introduces a QUBO-based pattern recognition method for quantum annealers, offering an alternative to classical algorithms in particle tracking applications.

## Key findings

- Achieves competitive physics performance at current track densities.
- Performance decreases with increasing track density due to classifier purity issues.
- Demonstrates potential of quantum annealing in high-energy physics data analysis.

## Abstract

The reconstruction of charged particles will be a key computing challenge for the high-luminosity Large Hadron Collider (HL-LHC) where increased data rates lead to large increases in running time for current pattern recognition algorithms. An alternative approach explored here expresses pattern recognition as a Quadratic Unconstrained Binary Optimization (QUBO) using software and quantum annealing. At track densities comparable with current LHC conditions, our approach achieves physics performance competitive with state-of-the-art pattern recognition algorithms. More research will be needed to achieve comparable performance in HL-LHC conditions, as increasing track density decreases the purity of the QUBO track segment classifier.