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
-
Leids onderzoek naar quantumtechnologie valt ruim in de prijzen -
Europese beurs om zelflerend vermogen van quantumcomputers verder te brengen -
Twee ERC Consolidator Grants voor Leidse wetenschappers -
De zoektocht naar een ‘quantumvoordeel’ -
Google gift voor quantumonderzoek -
Quantum Leiden brengt quantum-toekomst dichterbij -
Nederlandse en Japanse onderzoekers werken samen met toonaangevende quantumsoftware ontwikkelaar Qu&Co -
De toekomst van quantum -
615 miljoen voor quantumonderzoek -
Kunstmatige intelligentie leert sneller met quantumtechnologie -
Consortium zet Quantum Computer in de cloud -
LIACS onderdeel van Europees consortium om industriële quantum computing te stimuleren -
Nederland wil internationaal centrum quantumtechnologie worden -
PNAS Paperprijs voor quantum machine learning
Zie ook
Externe promovendi
Hoogleraar in Quantum Computing
- Wiskunde en Natuurwetenschappen
- Leiden Inst of Advanced Computer Science
- Patel Y.J., Jerbi S., Bäck T.H.W. & Dunjko V. (2024), Reinforcement learning assisted recursive QAOA, EPJ Quantum Technology (11): 6.
- Berry D.W., Su Y., Gyurik C., King R., Basso J., Barba del Toro A., Rajput A., Wiebe N., Dunjko V. & Babbush R. (2024), Analyzing Prospects for Quantum Advantage in Topological Data Analysis, PRX Quantum 5(1): 010319.
- Abbas A., Ambainis A., Augustino B., Bärtschi A., Buhrman H., Coffrin C., Cortiana G., Dunjko V., Egger D.J., Elmegreen B.G., Franco N., Fratini F., Fuller B., GaconJ., Gonciulea C., Griblin S., Gupta S., Hadfield S., Heese R., Kircher G., Kleinert T., Koch T., Korpas G., Lenk S., Marecek J., Markov V., Mazzola G., Mensa S., Mohseni N., Nannicini G., O'Meara C., Tapia E.P., Pokutta S., Proissl M., Rebentrost P., Sahin E., Symons B.C.B., Tornow S., Valls V., Woerner S., Wolf-Bauwens M.I., Yard J., Yarkoni S., Zechiel D., Zhuk S. & Zoufal C. (2024), Challenges and opportunities in quantum optimization, Nature Reviews Physics : .
- Kundu A., Bedelek P., Ostaszewski M., Danaci O., Patel Y.J., Dunjko V. & Miszczak J.A. (2024), Enhancing variational quantum state diagonalization using reinforcement learning techniques, New Journal of Physics 26(1): 013034.
- Gil-Fuster E., Eisert J. & Dunjko V. (2024), On the expressivity of embedding quantum kernels, Machine Learning: Science and Technology 5(2): 025003.
- Jerbi S., Gyurik C.F.S., Marshall S.C., Molteni R. & Dunjko V. (2024), Shadows of quantum machine learning, Nature Communications 15: 5676.
- Soliviev V.P., Dunjko V., Bielza C., Larranaga P. & Wang H. (2024), Trainability maximization using estimation of distribution algorithms assisted by surrogate modelling for quantum architecture search, EPJ Quantum Technology 11(1): 69.
- 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.
- Marshall Simon C. Gyurik Casper Dunjko Vedran (2023), High dimensional quantum machine learning with small quantum computers, Quantum 7: 1078.
- Vinkhuijzen L.T., Coopmans T.J., Elkouss D., Dunjko V. & Laarman A.W. (2023), LIMDD: a decision diagram for simulation of quantum computing including stabilizer states, Quantum 7: 1108.
- Pérez Salinas A., Draskic R., Tura Brugués J. & Dunjko V. (2023), Shallow quantum circuits for deeper problems, Physical Review A 108(6): 062423.
- 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. Pascal P. & Ienco D. (red.), Discovery Science. International Conference on Discovery Science 2022 10 oktober 2022 - 12 oktober 2022 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., Ge Y. & Cirac J.I. (2018), Computational speedups using small quantum devices, Physical Review Letters 121(25): 250501.
- 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