Quantum computing and artificial intelligence applied to atomic physics

Project funded by Fundação para a Ciência e a Tecnologia, I.P. (FCT), grant 2023.01585.BD. Runs 2024-03-01 to 2028-02-29. 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: This Ph.D. work plan is dedicated to the development of modern tools based on quantum computing and artificial intelligence to produce innovative progress in the field of atomic physics. The aim of this plan is two-folded: first is to introduce a novel approach that integrates classical and quantum computation to enhance our comprehension of electronic correlation in atomic structure. Secondly, we plan to employ machine learning algorithms, specifically artificial neural networks, to expedite and optimize the calculation of electronic correlation using classical computers. Our methodology will borrow from the techniques employed in ab initio chemistry calculations. Although similar techniques have been implemented in atomic physics, this work plan will be the first to utilize machine learning in a comprehensive relativistic treatment of atomic structure.

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, funded by FCT, runs from March 1, 2024, to February 29, 2028, focusing on using quantum computing and AI to advance atomic physics. It aims to integrate classical and quantum computation and apply machine learning for electronic correlation calculations. Suggested technology pillar: computing. Generated by AI from the public source text; it may contain errors and the original source is authoritative.