Vedran Dunjko

5.8k total citations · 4 hit papers
70 papers, 2.8k citations indexed

About

Vedran Dunjko is a scholar working on Artificial Intelligence, Atomic and Molecular Physics, and Optics and Computational Theory and Mathematics. According to data from OpenAlex, Vedran Dunjko has authored 70 papers receiving a total of 2.8k indexed citations (citations by other indexed papers that have themselves been cited), including 64 papers in Artificial Intelligence, 25 papers in Atomic and Molecular Physics, and Optics and 12 papers in Computational Theory and Mathematics. Recurrent topics in Vedran Dunjko's work include Quantum Computing Algorithms and Architecture (59 papers), Quantum Information and Cryptography (48 papers) and Quantum Mechanics and Applications (17 papers). Vedran Dunjko is often cited by papers focused on Quantum Computing Algorithms and Architecture (59 papers), Quantum Information and Cryptography (48 papers) and Quantum Mechanics and Applications (17 papers). Vedran Dunjko collaborates with scholars based in Netherlands, Austria and Germany. Vedran Dunjko's co-authors include Hans J. Briegel, Jacob M. Taylor, Erika Andersson, Petros Wallden, Sofiène Jerbi, Hendrik Poulsen Nautrup, Andrea Skolik, Nicolai Friis, Elham Kashefi and Adi Makmal and has published in prestigious journals such as Nature, Proceedings of the National Academy of Sciences and Physical Review Letters.

In The Last Decade

Vedran Dunjko

66 papers receiving 2.7k citations

Hit Papers

align trajectories

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

Machine learning & artificial intelligence in the quantum domain: a review of recent progress

2018 593 citations Vedran Dunjko, Hans J. Briegel Reports on Progress in Physics profile →
Quantum-Enhanced Machine Learning

2016 278 citations Vedran Dunjko, Jacob M. Taylor et al. profile →
Active learning machine learns to create new quantum experiments

2018 177 citations Vedran Dunjko, Hans J. Briegel et al. profile →
Quantum machine learning beyond kernel methods

2023 111 citations Sofiène Jerbi, Hans J. Briegel et al. Nature Communications profile →

Peers

Countries citing papers authored by Vedran Dunjko

Since Specialization

Citations

This map shows the geographic impact of Vedran Dunjko's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Vedran Dunjko with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Vedran Dunjko more than expected).

Fields of papers citing papers by Vedran Dunjko

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Vedran Dunjko. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Vedran Dunjko. The network helps show where Vedran Dunjko may publish in the future.

Co-authorship network of co-authors of Vedran Dunjko

This figure shows the co-authorship network connecting the top 25 collaborators of Vedran Dunjko. A scholar is included among the top collaborators of Vedran Dunjko based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Vedran Dunjko. Vedran Dunjko is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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20 of 20 papers shown

#	Work	Indexed citations
1	Exponential quantum advantages in learning quantum observables from classical data 2026 ·npj Quantum Information ·(unknown), (unknown), Vedran Dunjko	0
2	Approximation and Generalization Capacities of Parametrized Quantum Circuits for Functions in Sobolev Spaces 2025 ·Quantum ·(unknown), (unknown), Jordi Tura, Vedran Dunjko	0
3	Shadows of quantum machine learning 2024 ·Nature Communications ·Sofiène Jerbi, (unknown), (unknown), (unknown), Vedran Dunjko	33
4	On the expressivity of embedding quantum kernels 2024 ·Machine Learning Science and Technology ·Elies Gil-Fuster, Jens Eisert, Vedran Dunjko	8
5	Analyzing Prospects for Quantum Advantage in Topological Data Analysis 2024 ·PRX Quantum ·Dominic W. Berry, Yuan Su, (unknown), Robbie King, Joao Basso, Alexander Del Toro Barba, (unknown), Nathan Wiebe, Vedran Dunjko, Ryan Babbush	13
6	Enhancing variational quantum state diagonalization using reinforcement learning techniques 2024 ·New Journal of Physics ·(unknown), (unknown), (unknown), (unknown), (unknown), Vedran Dunjko, Jarosław Adam Miszczak	8
7	Structural risk minimization for quantum linear classifiers 2023 ·Quantum ·(unknown), (unknown), Vedran Dunjko	20
8	Performance comparison of optimization methods on variational quantum algorithms 2023 ·Physical review. A ·Xavier Bonet-Monroig, Hao Wang, Diederick Vermetten, Bruno Senjean, (unknown), Thomas Bäck, Vedran Dunjko, Thomas E. O’Brien	56
9	Hyperparameter importance and optimization of quantum neural networks across small datasets 2023 ·Machine Learning ·(unknown), (unknown), Vedran Dunjko, Thomas Bäck, Jan N. van Rijn	7
10	Shallow quantum circuits for deeper problems 2023 ·Physical review. A ·(unknown), (unknown), Jordi Tura, Vedran Dunjko	5
11	High Dimensional Quantum Machine Learning With Small Quantum Computers 2023 ·Quantum ·(unknown), (unknown), Vedran Dunjko	17
12	LIMDD: A Decision Diagram for Simulation of Quantum Computing Including Stabilizer States 2023 ·Quantum ·(unknown), (unknown), David Elkouss, Vedran Dunjko, Alfons Laarman	6
13	Encoding strongly-correlated many-boson wavefunctions on a photonic quantum computer: application to the attractive Bose-Hubbard model 2021 ·arXiv (Cornell University) ·(unknown), Bruno Senjean, Filippo M. Miatto, Vedran Dunjko	5
14	Experimental quantum speed-up in reinforcement learning agents 2021 ·Nature ·(unknown), (unknown), Arne Hamann, (unknown), (unknown), Vedran Dunjko, Nicolai Friis, Nicholas C. Harris, Michael Hochberg, Dirk Englund, Sabine Wölk, Hans J. Briegel, Philip Walther	111
15	Quantum-accessible reinforcement learning beyond strictly epochal environments 2021 ·Quantum Machine Intelligence ·Arne Hamann, Vedran Dunjko, Sabine Wölk	1
16	Skill Learning by Autonomous Robotic Playing Using Active Learning and Exploratory Behavior Composition 2020 ·Frontiers in Robotics and AI ·(unknown), Vedran Dunjko, Hans J. Briegel, Justus Piater	8
17	Machine learning & artificial intelligence in the quantum domain: a review of recent progress breakdown → 2018 ·Reports on Progress in Physics ·Vedran Dunjko, Hans J. Briegel	593
18	Enhanced learning for agents in quantum-accessible environments. 2016 ·The European Symposium on Artificial Neural Networks ·Jacob M. Taylor, Hans J. Briegel, Vedran Dunjko	1
19	Optical realisation of Quantum Digital Signatures without quantum memory 2013 ·arXiv (Cornell University) ·Robert J. Collins, Ross Donaldson, Vedran Dunjko, Petros Wallden, (unknown), Erika Andersson, John Jeffers, Gerald S. Buller	1
20	Universal Blind Quantum Computing with Coherent States 2011 ·arXiv (Cornell University) ·Vedran Dunjko, Elham Kashefi, Anthony Leverrier	3

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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