Vedran Dunjko
Professor in Quantum Computing
- Name
- Prof.dr. V. Dunjko
- Telephone
- +31 71 527 2873
- v.dunjko@liacs.leidenuniv.nl
- ORCID iD
- 0000-0002-2632-7955
Vedran Dunjko’s research interest lies in the intersection of computer science and quantum physics, including quantum computing and quantum cryptography. Over the course of the last few years, he has been focusing on the interplay between quantum computing, machine learning, and artificial intelligence.
More information about Vedran Dunjko
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Vedran Dunjko joined LIACS, as a tenure-track Assistant Professor in 2018 . Here, he will be investigating quantum machine learning and, more generally, work on developing the field of quantum heuristics, which promotes the connections between practical computing and quantum computing. He is one of the founders of the Leiden Applied Quantum Algorithms interdepartmental initiative, and is now affiliated with both the computer science (LIACS) and physics (LION) departments in Leiden.
Short Background
Vedran received his MSc degree in Mathematics and Computer Science from the University of Zagreb, Croatia, and has completed his PhD in 2012 in Theoretical Physics at Heriot-Watt University, Edinburgh, UK, working on new quantum cryptographic protocols.
After his PhD, he has held post-doctoral positions at the School of Informatics of the University of Edinburgh, Institute of Theoretical Physics of Innsbruck University, and the Theory Group of Max Planck Institute of Quantum Optics in Germany.
Since 2013, his research has focused on various aspects of quantum machine learning including the development of novel quantum algorithms for machine learning and AI problems, and the application of AI methods in quantum contexts.
Publications
Vedran's publications can be found on arXiv and also on Google Scholar.
More information
For more information see Vedran's personal webpage.
Professor in Quantum Computing
- Science
- Leiden Inst of Advanced Computer Science
- Bonet-Monroig X., Wang H., Vermetten D.L., Senjean B., Moussa C., Bäck T.H.W., Dunjko V. & O'Brien T.E. (2023), Performance comparison of optimization methods on variational quantum algorithms, Physical Review A 107(3): 032407.
- Gyurik C.F.S., Vreumingen D. van & Dunjko V. (2023), Structural risk minimization for quantum linear classifiers, Quantum 7: 893.
- Rennela M., Brand S., Laarman A. & Dunjko V. (2023), Hybrid divide-and-conquer approach for tree search algorithms, Quantum 7: 959.
- Requena B., Munoz-Gil G., Lewenstein M., Dunjko V. & Tura J. (2023), Certificates of quantum many-body properties assisted by machine learning, Physical Review Research 5(1): 013097.
- Moussa C., Wang H., Bäck T.H.W. & Dunjko V (2022), Unsupervised strategies for identifying optimal parameters in Quantum Approximate Optimization Algorithm, EPJ Quantum Technology 9: 11.
- Moussa C., Rijn J.N. van, Bäck T.H.W. & Dunjko V. (2022), Hyperparameter importance of quantum neural networks across small datasets. In: Pascal P. & Ienco D. (Eds.) Discovery Science. no. 13601 Cham: Springer. 32-46.
- Dunjko V. (2022), Quantum learning unravels quantum system, Science 376(6598): 1154-1155.
- Skolik A., Jerbi S. & Dunjko V. (2022), Quantum agents in the Gym: a variational quantum algorithm for deep Q-learning, Quantum 6: 720.
- Gyurik C.F.S., Cade C. & Dunjko V. (2022), Towards quantum advantage via topological data analysis, Quantum 6: 855.
- Orsucci D. & Dunjko V. (2021), On solving classes of positive-definite quantum linear systems with quadratically improved runtime in the condition number, Quantum 5: 573.
- Yalouz S., Senjean B., Miatto F. & Dunjko V. (2021), Encoding strongly-correlated many-boson wavefunctions on a photonic quantum computer: application to the attractive Bose-Hubbard model, Quantum 5: 572.
- Orsucci D. & Dunjko V. (2021), On solving classes of positive-definite quantum linear systems with quadratically improved runtime in the condition number, Quantum 5: 573.
- Yalouz S., Senjean B., Miatto F. & Dunjko V. (2021), Encoding strongly-correlated many-boson wavefunctions on a photonic quantum computer: application to the attractive Bose-Hubbard model, Quantum 5: 572.
- Sofiene J., Gyurik C.F.S., Marshall S.C., Briegel H. & Dunjko V. (2021), Parametrized quantum policies for reinforcement learning. In: Ranzato M., Beygelzimer A., Dauphin Y., Liang P.S. & Wortman Vaughan J. (Eds.) Advances in neural information processing systems. no. 34. 28362-28375.
- Moussa C., Wang H., Calandra H., Bäck T.H.W. & Dunjko V. (2020), Tabu-driven quantum neighborhood samplers. Zarges C. & Verel S. (Eds.), Evolutionary computation in combinatorial optimization. 21st European Conference on Evolutionary Computation in Combinatorial Optimization, EvoCOP 2021 7 April 2021 - 9 April 2021. arXiv no. 12692. Cham: Springer. 100-119.
- Moussa C., Calandra H. & Dunjko V. (2020), To quantum or not to quantum: towards algorithm selection in near-term quantum optimization, Quantum Science and Technology 5(4): 044009.
- Dunjko V. & Briegel H.J. (2018), Machine learning & artificial intelligence in the quantum domain: a review of recent progress, Reports on Progress in Physics 81(7): 074001.
- Zwerger M., Pirker A., Dunjko V., Briegel H.J. & Dür W. (2018), Long-range big quantum-data transmission, Physical Review Letters 120(3): 030503.
- Melnikov A.A., Poulsen Nautrup H., Krenn M., Dunjko V., Tiersch M., Zeilinger A. & Briegel H.J. (2018), Active learning machine learns to create new quantum experiments, Proceedings of the National Academy of Sciences 115(6): 1221-1226.
- Scientific advisor