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Charged Particle Reconstruction for Future High Energy Colliders with Quantum Approximate Optimization Algorithm

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Intelligent Computers, Algorithms, and Applications (IC 2023)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 2036))

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Abstract

Usage of cutting-edge artificial intelligence will be the baseline at future high energy colliders such as the High-Luminosity Large Hadron Collider, to cope with the enormously increasing demand of the computing resources. The rapid development of quantum machine learning could bring in further paradigm-shifting improvement to this challenge. One of the two highest CPU-consuming components, the charged particle reconstruction, the so-called track reconstruction, can be considered as a quadratic unconstrained binary optimization (QUBO) problem. The Quantum Approximate Optimization Algorithm (QAOA) is one of the most promising algorithms to solve such combinatorial problems and to seek for a quantum advantage in the era of the Noisy Intermediate-Scale Quantum computers. It is found that the QAOA shows promising performance and demonstrated itself as one of the candidates for the track reconstruction using quantum computers.

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Acknowledgements

The author would like to thank Federico Meloni and David Spataro for discussions regarding the quantum tracking and Andreas Salzburger for his suggestion on the TrackML dataset. The author would also like to thank Ziwei Cui and Lei Li from Origin Quantum (Benyuan) for various feedback. The author is supported by NSFC under contract No. 12075060. This work is benefited by the libraries and quantum computing resources provided by Origin Quantum.

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Authors and Affiliations

  1. Institute of High Energy Physics, Chinese Academy of Sciences, Shijingshan, 100049, Beijing, China

    Hideki Okawa

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  1. Hideki Okawa
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Correspondence to Hideki Okawa .

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Editors and Affiliations

  1. Université de Bourgogne, Dijon, France

    Christophe Cruz

  2. Victoria University, Melbourne, VIC, Australia

    Yanchun Zhang

  3. Chinese Academy of Sciences, Beijing, China

    Wanling Gao

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© 2024 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

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Okawa, H. (2024). Charged Particle Reconstruction for Future High Energy Colliders with Quantum Approximate Optimization Algorithm. In: Cruz, C., Zhang, Y., Gao, W. (eds) Intelligent Computers, Algorithms, and Applications. IC 2023. Communications in Computer and Information Science, vol 2036. Springer, Singapore. https://doi.org/10.1007/978-981-97-0065-3_21

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  • DOI: https://doi.org/10.1007/978-981-97-0065-3_21

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-97-0064-6

  • Online ISBN: 978-981-97-0065-3

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