Vedran Dunjko
Hoogleraar in Quantum Computing
- Naam
- Prof.dr. V. Dunjko
- Telefoon
- +31 71 527 2873
- v.dunjko@liacs.leidenuniv.nl
- ORCID iD
- 0000-0002-2632-7955
De onderzoeksinteresse van Vedran Dunjko ligt op het snijvlak van informatica en kwantumfysica, inclusief quantum computing en kwantumcryptografie. In de afgelopen jaren heeft hij zich gericht op het samenspel tussen quantum computing, machine learning en kunstmatige intelligentie. Meer informatie over Vedran Dunjko op zijn Engelstalige profielpagina.
Promovendi
Nieuws
Zie ook
Externe promovendi
Hoogleraar in Quantum Computing
- Wiskunde en Natuurwetenschappen
- Leiden Inst of Advanced Computer Science
- 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.
- 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.
- 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. (red.) Discovery Science. nr. 13601 Cham: Springer. 32-46.
- Skolik A., Jerbi S. & Dunjko V. (2022), Quantum agents in the Gym: a variational quantum algorithm for deep Q-learning, Quantum 6: 720.
- Dunjko V. (2022), Quantum learning unravels quantum system, Science 376(6598): 1154-1155.
- 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.
- 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. (red.) Advances in neural information processing systems. nr. 34. 28362-28375.
- 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.
- Moussa C., Wang H., Calandra H., Bäck T.H.W. & Dunjko V. (2020), Tabu-driven quantum neighborhood samplers. Zarges C. & Verel S. (red.), Evolutionary computation in combinatorial optimization. 21st European Conference on Evolutionary Computation in Combinatorial Optimization, EvoCOP 2021 7 april 2021 - 9 april 2021. arXiv nr. 12692. Cham: Springer. 100-119.
- 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.
- 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.
- Zwerger M., Pirker A., Dunjko V., Briegel H.J. & Dür W. (2018), Long-range big quantum-data transmission, Physical Review Letters 120(3): 030503.
- Scientific advisor