Hello! Our lab is a theory lab and my group and I are pursuing research in the wide field of the Physics of Information and AI.
Current topics include, for example:
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Quantum information in quantum gravity
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Relativistic Quantum Information
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Computational complexity in adiabatic quantum computation
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AI to boost quantum technologies (e.g., machine learning for quantum error prevention/mitigation/correction)
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Quantum machine learning
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Quantum cloud technologies
We are actively involved in practical applications through partnerships with the National Research Council of Canada, IVADO in Montreal, with the Fraunhofer Institute IAO on the Bildungscampus Heilbronn and with the forthcoming Innovation Park Artificial Intelligence in Heilbronn, Germany.
We have also been researching information-theoretic aspects of engineering and biology. Oh, and yes, we showed that one can integrate by differentiating - see for example the two equations on the top right of this page. No kidding: Journal, Arxiv, Journal, Arxiv, Video. The new methods are now implemented in Maple and are making Maple faster and more powerful than the competition in integration and integral transforms.
Here is some advice I wrote for new grad students: Handbook
News
We are co-hosting the ML4QT 2026 Symposium
Our group is co-hosting the second annual Machine Learning to Advance Quantum Technologies (ML4QT) symposium next week, from September 23–25, 2026, at the Quantum-Nano Centre. This international event fosters collaboration across machine learning and quantum innovation. View the 2026 program [here].
We developed the first method to safely redundantly back up quantum information, enabling quantum clouds
We showed that, in spite of the no-cloning theorem, qubits can be cloned at will, if encrypted with a single-use decryption key. Here are the two LinkedIn announcements post, experimental post, a link to the theory paper, a link to the paper that shows it works in practice, and here is some press coverage.
We discovered new insights into the problem of solving problems
Any computational problem can be reformulated as an adiabatic quantum computation. We discovered new insights into how problem hardness translates into slowdowns due to entanglement production. See, for example, here.