Quantum Algorithms for Experimental High-Energy Physics Data Processing

Project funded by Fundação para a Ciência e a Tecnologia, I.P. (FCT), grant 2020.04677.BD. Runs 2020-11-01 to 2023-04-16. Source: OpenAIRE Graph, from the funder's project data (collected by OpenAIRE on 2026-07-23); national funding data, not CORDIS. Abstract given by the source: With this project we aim at developing quantum algorithms as solutions to the increasingly challenging, and soon intractable, problem of analysing events from large particle physics experiments. Indeed, the reconstruction of the particles' tracks at the detectors is one of the most important and time-consuming components of any HEP experimental analysis, and it will become even more so as the luminosities of the next generation accelerators keep increasing. We will approach track reconstruction as a quantum search problem, making use of the fundamental quantum properties of superposition and interference to aim at an algorithmic speed-up over the classical methods. We will study our algorithm’s theorical scaling and benchmark it on the available quantum hardware against the state-of-the-art solutions. Our work will furthermore benefit from the context of the pioneering European project QuantHEP, bringing together for the first time experts in both experimental particle physics and quantum computation.

Original source.

Source: OpenAIRE Graph: projects of national funders, OpenAIRE, from the project data of Fundação para a Ciência e a Tecnologia, I.P. (FCT). Retrieved 2026-10-22. Licence: Creative Commons Attribution 4.0 (OpenAIRE Graph). Selected metadata, adapted presentation; the publisher's record is authoritative. No endorsement by the source is implied. Licence: Creative Commons Attribution 4.0 (CC BY 4.0).

AI-assisted summary: The project "Quantum Algorithms for Experimental High-Energy Physics Data Processing" is funded by FCT and runs from November 1, 2020, to April 16, 2023. It aims to develop quantum algorithms for analyzing data from large particle physics experiments. Suggested technology pillar: computing. Generated by AI from the public source text; it may contain errors and the original source is authoritative.