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This research demonstrates that gate-based universal quantum algorithms can match or exceed classical machine learning techniques in solving specific combinatorial problems within high-energy physics, such as particle pairing at the Large Hadron Collider. By eliminating the need for extensive training phases required by classical methods, these approaches offer a path toward real-time adaptability and scalability as quantum hardware matures. The study highlights a promising hybrid workflow where quantum computing directly addresses data-intensive tasks in particle physics without relying on traditional kinematic matching.
Hybrid quantum-classical approach for combinatorial problems at hadron colliders
arXiv:2410.22417v2 Announce Type: replace-cross Abstract: In recent years, quantum computing has drawn significant interest within the field of high-energy physics. We explore the potential of quantum algorithms to resolve the combinatorial problems in particle physics experiments. As a concrete example, we consider top quark pair production in the fully hadronic channel at the Large Hadron Collider. We investigate the performance of various quantum algorithms such as the Quantum Approximation Optimization Algorithm (QAOA), a feedback-based algorithm (FALQON) and a variational quantum imaginary time evolution algorithm (VarQITE). We demonstrate that the efficiency for selecting the correct pairing is greatly improved by utilizing quantum algorithms over conventional kinematic methods. Furthermore, we observe that gate-based universal quantum algorithms perform on par with machine learning techniques and either surpass or match the effectiveness of quantum annealers. Our findings reveal that quantum algorithms not only provide a substantial increase in matching efficiency but also exhibit adaptability and the potential for scalability, making them promising candidates for a variety of high-energy physics applications, as quantum hardware technology matures. Moreover, quantum algorithms eliminate the extensive training processes needed by classical machine learning methods, enabling real-time adjustments based on individual event data.
Abstract:In recent years, quantum computing has drawn significant interest within the field of high-energy physics. We explore the potential of quantum algorithms to resolve the combinatorial problems in particle physics experiments. As a concrete example, we consider top quark pair production in the fully hadronic channel at the Large Hadron Collider. We investigate the performance of various quantum algorithms such as the Quantum Approximation Optimization Algorithm (QAOA), a feedback-based algorithm (FALQON) and a variational quantum imaginary time evolution algorithm (VarQITE). We demonstrate that the efficiency for selecting the correct pairing is greatly improved by utilizing quantum algorithms over conventional kinematic methods. Furthermore, we observe that gate-based universal quantum algorithms perform on par with machine learning techniques and either surpass or match the effectiveness of quantum annealers. Our findings reveal that quantum algorithms not only provide a substantial increase in matching efficiency but also exhibit adaptability and the potential for scalability, making them promising candidates for a variety of high-energy physics applications, as quantum hardware technology matures. Moreover, quantum algorithms eliminate the extensive training processes needed by classical machine learning methods, enabling real-time adjustments based on individual event data.
arXiv:2410.22417 [hep-ph]
(or arXiv:2410.22417v2 [hep-ph] for this version)
https://doi.org/10.48550/arXiv.2410.22417 Focus to learn more arXiv-issued DOI via DataCite
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