Ronald de Wolf
- Full Name
- Prof.dr. R.M. de Wolf
- Function(s)
- Professor - Universiteit van Amsterdam, Scientific Staff Member
- Ronald.de.Wolf@cwi.nl
- Telephone
- +31 20 592 4078
- Room
- L234
- Department(s)
- Algorithms and Complexity
- Homepage
- http://homepages.cwi.nl/~rdewolf/
Biography
Ronald de Wolf (1973) studied computer science and philosophy at the Erasmus University Rotterdam, with a focus on logic-based machine learning. He obtained his PhD in 2001 from the University of Amsterdam and CWI on a thesis about quantum computation and communication complexity, advised by Harry Buhrman and Paul Vitanyi. Subsequently he spent a postdoctoral year at UC Berkeley. Currently he is a senior researcher at CWI and full professor at the University of Amsterdam.
Publications
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van Apeldoorn, J.T.S, Gilyén, A.P, Gribling, S.J, & de Wolf, R.M. (2020). Convex optimization using quantum oracles. Quantum, 4. doi:10.22331/q-2020-01-13-220
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Sinha, M, & de Wolf, R.M. (2019). Exponential separation between quantum communication and logarithm of approximate rank. In Proceedings of FOCS (pp. 966–981). doi:10.1109/FOCS.2019.00062
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Arunachalam, S, Chakraborty, S, Lee, T. J, Paraashar, M, & de Wolf, R.M. (2019). Two new results about quantum exact learning. In Proceedings of 46th International Colloquium on Automata, Languages and Programming (ICALP 19).
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Arunachalam, S, & de Wolf, R.M. (2018). Optimal quantum sample complexity of learning algorithms. Journal of Machine Learning Research, 19, 1–36.
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de Boer, K, Ducas, L, Jeffery, S, & de Wolf, R.M. (2018). Attacks on the AJPS Mersenne-based cryptosystem. In Lecture Notes in Computer Science/Lecture Notes in Artificial Intelligence (pp. 101–120). doi:10.1007/978-3-319-79063-3_5
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de Wolf, R.M. (2017). The potential impact of quantum computers on society. Ethics and Information Technology, 19(4), 271–276.
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Arunachalam, S, & de Wolf, R.M. (2017). Optimal quantum sample complexity of learning algorithms. In Leibniz International Proceedings in Informatics, LIPIcs: Proceedings of Conference of Computational Complexity. doi:10.4230/LIPIcs.CCC.2017.25
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Arunachalam, S, & de Wolf, R.M. (2017). A survey of quantum learning theory. ACM SIGACT News, 48(2), 41–67.
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van Apeldoorn, J.T.S, Gilyén, A.P, Gribling, S.J, & de Wolf, R.M. (2017). Quantum SDP-solvers: Better upper and lower bounds. In FOCS (pp. 403–414). doi:10.1109/FOCS.2017.44
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Arunachalam, S, & de Wolf, R.M. (2017). Optimizing the number of gates in quantum search. Quantum Information & Computation, 17(3&4), 251–261.
Current projects with external funding
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Quantum algorithms and applications (QuantAlgo)
Professional activities
- Professor: Universiteit van Amsterdam [UvA]
- Editor: Journal: Theory of Computing - [TOC]
- Editor: SIAM Journal on Computing;
- Editor: Journal: Quantum
Grants
- ERC Consolidator Grant (2013)
- NWO TOP-grant Samen met Monique Laurent en Nikhil Bansal (2013)
- Vidi Innovational Research Grant NWO (2008)
- Veni Innovational Research Grant NWO (2005)
Awards
- STOC'12 Best Paper Award : Semidefinite Extended Formulations: Exponential Separation and Strong Lower Bounds. In 44th Annual ACM Symposium on Theory of Computing (STOC 12) (2012)
- Cor Baayen Award (2003)