Quantum artificial intelligence for pattern recognition at high-energy colliders: Tales of Three "Quantum's"

TL;DR

This paper reviews how emerging quantum computing technologies, including quantum gates, annealing, and quantum-inspired methods, are being explored to improve pattern recognition tasks in high-energy physics, aiming to enhance efficiency and reduce computational costs.

Contribution

It provides a comprehensive overview of the current status and potential of various quantum computing approaches for pattern recognition in high-energy collider experiments.

Findings

Quantum computing offers promising efficiency gains for pattern recognition.

Different quantum technologies have unique advantages and limitations.

Quantum-inspired methods are also actively investigated for high-energy physics applications.

Abstract

Quantum computing applications are an emerging field in high-energy physics. Its ambitious fusion with artificial intelligence is expected to deliver significant efficiency gains over existing methods and/or enable computation from a fundamentally different perspective. High-energy physics is a big data science that utilizes large-scale facilities, detectors, high-performance computing, and its worldwide networks. The experimental workflow consumes a significant amount of computing resources, and its annual cost will continue to grow exponentially at future colliders. In particular, pattern recognition is one of the most crucial and computationally intensive tasks. Three types of quantum computing technologies, i.e., quantum gates, quantum annealing, and quantum-inspired, are all actively investigated for high-energy physics applications, and each has its pros and cons. This article reviews the current status of quantum computing applications for pattern recognition at high-energy colliders.