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Toward a Community Roadmap for High Energy Physics and Artificial Intelligence in China and Beyond

Tianji Cai, Ke Li, Teng Li

Abstract

Artificial Intelligence (AI) is rapidly transforming scientific research and has become central to many data-intensive disciplines. High Energy Physics (HEP), with its vast data volumes, complex theoretical structures, and precision-driven methodologies, lies at a particularly fertile intersection with modern AI. In this document, we present a community-informed overview of AI+HEP development in China and beyond, motivated in part by discussions at the 2025 Quantum Computing and Machine Learning Workshop in Qingdao, Shandong Province. We briefly review current AI activities across experimental, phenomenological, and theoretical HEP, along with key aspects of the research ecosystem. This work does not aim to represent the entire community, but rather reflects a partial and evolving snapshot informed by discussions and perspectives gathered from members of the broader AI+HEP community. We hope it serves as an initial roadmap to inform future coordinated efforts and to lay the groundwork for a more comprehensive community white paper.

Toward a Community Roadmap for High Energy Physics and Artificial Intelligence in China and Beyond

Abstract

Artificial Intelligence (AI) is rapidly transforming scientific research and has become central to many data-intensive disciplines. High Energy Physics (HEP), with its vast data volumes, complex theoretical structures, and precision-driven methodologies, lies at a particularly fertile intersection with modern AI. In this document, we present a community-informed overview of AI+HEP development in China and beyond, motivated in part by discussions at the 2025 Quantum Computing and Machine Learning Workshop in Qingdao, Shandong Province. We briefly review current AI activities across experimental, phenomenological, and theoretical HEP, along with key aspects of the research ecosystem. This work does not aim to represent the entire community, but rather reflects a partial and evolving snapshot informed by discussions and perspectives gathered from members of the broader AI+HEP community. We hope it serves as an initial roadmap to inform future coordinated efforts and to lay the groundwork for a more comprehensive community white paper.

Paper Structure

This paper contains 13 sections.