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
PhD Candidates
News
-
Leiden quantum technology research very successful in funding call -
Two ERC Consolidator Grants for Leiden researchers -
New professor Vedran Dunjko finds real-world problems that a quantum computer can solve -
Tricking a quantum computer to enhance its performance -
The search for a ‘quantum advantage’ -
Google gift for quantum research -
Quantum Leiden creates potential for a great quantum future -
Dutch and Japanese researchers collaborate with leading quantum software developer Qu&Co -
The future of quantum -
615 million euros for quantum research -
Artificial Intelligence learns faster with quantum technology -
Consortium puts Quantum Computer in the cloud -
LIACS part of European consortium to boost industrial quantum computing -
The Netherlands as an international centre for quantum technology -
PNAS Paper Prize for quantum machine learning
See also
External PhD Candidates
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
- 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. (Eds.), Discovery Science. International Conference on Discovery Science 2022 10 October 2022 - 12 October 2022 no. 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. (Eds.) Advances in neural information processing systems. no. 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. (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.
- 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