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arXiv:2511.16713 · Modern Physics Letters A

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

Hideki Okawa

quant-phhep-exhep-phSubmitted Nov 20, 2025• Announced Nov 21, 2025• 0 citations
arXivHTMLPDF

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.

Journal reference
Modern Physics Letters A (2026) 2630008
DOI
10.1142/S0217732326300089
Comments
29 pages, 12 figures

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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.
High-energy physics is facing a daunting computing challenge with the large datasets expected from the upcoming High-Luminosity Large Hadron Collider in the next decade and even more so at future colliders. A key challenge in the reconstruction of events of simulated data and collision data is the pattern recognition algorithms used to determine the trajectories of charged particles. The field of quantum computing shows promise for transformative capabilities and is going through a cycle of rapid development and hence might provide a solution to this challenge. This article reviews current studies of quantum computers for charged particle pattern recognition in high-energy physics. This article is part of the theme issue 'Quantum technologies in particle physics'.
This paper reveals the future prospects of quantum algorithms in high energy physics (HEP). Particle identification, knowing their properties and characteristics is a challenging problem in experimental HEP. The key technique to solve these problems is pattern recognition, which is an important application of machine learning and unconditionally used for HEP problems. To execute pattern recognition task for track and vertex reconstruction, the particle physics community vastly use statistical machine learning methods. These methods vary from detector-to-detector geometry and magnetic field used in the experiment. Here, in this paper, we deliver the future possibilities for the lucid application of quantum computation and quantum machine learning in HEP, rather than focusing on deep mathematical structures of techniques arising in this domain.
High-energy physics is facing a daunting computing challenge with the large amount of data expected from the HL-LHC and other future colliders. In addition, the landscape of computation has been expanding dramatically with technologies beyond the standard x86 CPU architecture becoming increasingly available. Both of these factors necessitate an extensive and broad-ranging research and development campaign. As quantum computation has been evolving rapidly over the past few years, it is important to evaluate how quantum computation could be one potential avenue for development for future collider experiments. A wide variety of applications have been considered by different authors. We review here selected applications of quantum computing to high-energy physics, including topics in simulation, reconstruction, and the use of machine learning, and their challenges. In addition, recent advances in quantum computing technology to enhance such applications are briefly highlighted. Finally, we will discuss how such applications might transform the workflows of future collider experiments and highlight other potential applications.
This article reveals the future prospects of quantum machine learning in high energy physics (HEP). Particle identication, knowing their properties and characteristics is a challenging problem in experimental HEP. The key technique to solve these problems is pattern recognition, which is an important application of machine learning and unconditionally used for HEP problems. To execute pattern recognition task for track and vertex reconstruction, the particle physics community vastly use statistical machine learning methods. These methods vary from detector to detector geometry and magnetic led used in the experiment. Here in the present introductory article, we deliver the future possibilities for the lucid application of quantum machine learning in HEP, rather than focusing on deep mathematical structures of techniques arise in this domain.
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