Understanding Quantum Matter through Gravity and Duality

Project funded by Research Council of Finland, grant 330346. Participating organisations named by the source: University of Helsinki. Funded amount: EUR 212,644, as stated by the source. Runs 2020-09-01 to 2023-08-31. Keywords: Physical sciences. 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: What forms of matter are possible in nature? To answer this big question requires both extensive experimental work and a solid theoretical framework. This project aims to increase our grasp of the most fundamental theories physicists use to describe matter: quantum field theories. This will be done by using a surprising mathematical tool, derived from string theory, known as holographic duality. The duality can take otherwise unsolvable problems in quantum field theory and relate them to easier problems in gravitational theories similar to Einsteins general theory of relativity. The project, carried out at the Helsinki Institute of Physics, will use holographic duality to construct and study new theoretical models of exotic forms of matter. These models will then be applied to gain new understanding of a diverse set of topics ranging from neutron stars to novel types of metals.

Original source.

Source: OpenAIRE Graph: projects of national funders, OpenAIRE, from the project data of Research Council of Finland. 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 "Understanding Quantum Matter through Gravity and Duality" is funded by the Research Council of Finland with a grant of EUR 212,644, running from September 1, 2020, to August 31, 2023, at the University of Helsinki. Suggested technology pillar: simulation. Generated by AI from the public source text; it may contain errors and the original source is authoritative.