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Beyond-classical Machine learning and AI for Quantum Physics

CORDIS · observation · Publication date unknown

Beyond-classical Machine learning and AI for Quantum Physics A primary challenge in quantum computing (QC) is finding its ideal application, i.e., an essential problem with the largest advantage of quantum over classical computing. To resolve it, I propose to focus on the notoriously complex area of quantum many-body systems. This project will characterise which quantum many-body problems, in various physics domains, allow for significant quantum advantages even over any future machine learning, data-driven methods. By exploiting my pioneering research in this area, I will also develop new quantum machine learning (QML) methods to solve them better than classically possible, using a two-stage approach. In the first stage, we will develop the project's theoretical foundations. My recent works on quantum-over-classical learning advantages provide the starting points for the development of new mathematica

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recordType

award

status

SIGNED

region

EU

value

1995289

unit

EUR