Hybrid quantum-classical approach for combinatorial problems at hadron colliders

TL;DR

This paper investigates the use of quantum algorithms, specifically QAOA and FALQON, to improve combinatorial problem-solving in high-energy physics experiments, demonstrating enhanced efficiency and scalability over classical methods.

Contribution

It introduces quantum algorithms as effective tools for particle physics combinatorial problems, showing they outperform traditional methods and match machine learning in efficiency.

Findings

Quantum algorithms improve pairing accuracy in top quark event reconstruction.

Quantum algorithms match or surpass machine learning and quantum annealers.

They enable real-time data processing without extensive training.

Abstract

In recent years, quantum computing has drawn significant interest within the field of high-energy physics. We explore the potential of quantum algorithms to resolve the combinatorial problems in particle physics experiments. As a concrete example, we consider top quark pair production in the fully hadronic channel at the Large Hadron Collider. We investigate the performance of various quantum algorithms such as the Quantum Approximation Optimization Algorithm (QAOA) and a feedback-based algorithm (FALQON). We demonstrate that the efficiency for selecting the correct pairing is greatly improved by utilizing quantum algorithms over conventional kinematic methods. Furthermore, we observe that gate-based universal quantum algorithms perform on par with machine learning techniques and either surpass or match the effectiveness of quantum annealers. Our findings reveal that quantum algorithms not only provide a substantial increase in matching efficiency but also exhibit scalability and adaptability, making them suitable for a variety of high-energy physics applications. Moreover, quantum algorithms eliminate the extensive training processes needed by classical machine learning methods, enabling real-time adjustments based on individual event data.