SOURCE-LINKED INTELLIGENCE
Symbolic artificial intelligence for hidden topological orders in quantum physics
Symbolic artificial intelligence for hidden topological orders in quantum physics Artificial intelligence (AI) offers novel methodologies to unravel complex physical phenomena. However, most machine learning models lack transparency in their decision-making processes. In this proposal, we aim to develop a symbolic AI as a tool to reveal hidden topological orders in quantum physics. To this end, an AI-assisted symbolic regression method will be studied. We focus on three main objectives: (i) machine learning topological phases with experimental data; (ii) uncovering hidden non-local symmetry-protected topological orders; and (iii) searching for quantized topological invariants in an unsupervised fashion. The interplay between symbolic AI and quantum physics is envisioned to bring new insights into topological phases. Moreover, the project will scrutinize the explainability and the robustness of ma
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- recordType
- award
- status
- SIGNED
- region
- EU
- value
- 149453.6
- unit
- EUR
Evidence & attribution
European Commission, CORDIS Horizon Europe project dataset. Metadata adapted.
License: CORDIS reuse policy
First collected: 2026-09-20T03:21:21.440Z. This is not the publication date.