Quantum artificial intelligence for pattern recognition at high-energy colliders: Tales of Three "Quantum's"
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arXiv
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866908668148056064 |
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| author | Okawa, Hideki |
| author_facet | Okawa, Hideki |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_16713 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Quantum artificial intelligence for pattern recognition at high-energy colliders: Tales of Three "Quantum's" Okawa, Hideki Quantum Physics High Energy Physics - Experiment High Energy Physics - Phenomenology 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. |
| title | Quantum artificial intelligence for pattern recognition at high-energy colliders: Tales of Three "Quantum's" |
| topic | Quantum Physics High Energy Physics - Experiment High Energy Physics - Phenomenology |
| url | https://arxiv.org/abs/2511.16713 |