Learning Quantum Data to Investigate Multipartite Entanglement

Project funded by Fundação para a Ciência e a Tecnologia, I.P. (FCT), grant 2021.05528.BD. Runs 2021-11-01 to 2025-07-30. 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: The encoding of quantum datasets is determinant; quantum machine learning is not guaranteed to surpass the classical counterpart in solving the hard numerical problems of Physics. However, quantum computers are expected to have intrinsic advantage in encoding arbitrarily entangled states --- this is one of the defining differences between classical and quantum computing. This project aims to establish the power of quantum machine learning (QML) for a task fundamentally concerning entanglement: measuring (multipartite) entanglement itself. This is known to be a non-trivial task, with ample practical use-cases. It is expected, both due to theoretical and practical concerns, that the solutions will be variational quantum algorithms (VQAs). Therefore the project will also study the theoretical resources needed for these VQAs to produce and learn quantum datasets, and how these relate to the advantage in employing QML, starting from entanglement measure and generalizing to other physical, and finally arbitrary, datasets.

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 2026-10-28. 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 "Learning Quantum Data to Investigate Multipartite Entanglement" is funded by FCT from 2021-11-01 to 2025-07-30. It aims to explore the capabilities of quantum machine learning in measuring multipartite entanglement using variational quantum algorithms. Suggested technology pillar: computing. Generated by AI from the public source text; it may contain errors and the original source is authoritative.