Ivan Šupić
Impact in
- Artificial Intelligence top 1%
- Quantum Information and Cryptography
- Quantum Computing Algorithms and Architecture
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- Quantum Mechanics and Applications
- Quantum many-body systems
Papers in
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- Quantum Information and Cryptography 34
-
- Quantum Mechanics and Applications 13
- Co-authors
- Joseph Bowles (6 shared papers)Daniel Cavalcanti (11 shared papers)Antonio Acín (8 shared papers)Paul Skrzypczyk (8 shared papers)Remigiusz Augusiak (4 shared papers)Matty J. Hoban (3 shared papers)Nicolas Brunner (5 shared papers)Andrea Coladangelo (1 shared paper)
- Journals
- Physical Review Letters (9 papers)Physical Review A (6 papers)Quantum (5 papers)PRX Quantum (4 papers)New Journal of Physics (4 papers)
- Partner nations
- SpainSwitzerlandFrance
In The Last Decade
Ivan Šupić
34 papers receiving 1.2k citations
Peers
Comparison fields: 5 of 15
- Artificial Intelligence 1.1k
- Atomic and Molecular Physics, and Optics 505
- Statistical and Nonlinear Physics 9
- Surfaces, Coatings and Films 3
- Mathematical Physics 4
Countries citing papers authored by Ivan Šupić
This map shows the geographic impact of Ivan Šupić'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 Ivan Šupić with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Ivan Šupić more than expected).
Fields of papers citing papers by Ivan Šupić
This network shows the impact of papers produced by Ivan Šupić. 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 Ivan Šupić. The network helps show where Ivan Šupić may publish in the future.
Co-authors
The 25 scholars most cited alongside Ivan Šupić, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
Showing the 20 most-cited of 37 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | 2020 | 246 | |
| 2 | 2019 | 98 | |
| 3 | 2016 | 64 | |
| 4 | 2018 | 63 | |
| 5 | 2018 | 62 | |
| 6 | 2017 | 58 | |
| 7 | 2020 | 56 | |
| 8 | 2020 | 54 | |
| 9 | 2016 | 53 | |
| 10 | Self-testing through EPR-steering | 2016 | 51 |
| 11 | 2018 | 47 | |
| 12 | 2021 | 43 | |
| 13 | 2019 | 42 | |
| 14 | 2023 | 36 | |
| 15 | Maximal nonlocality from maximal entanglement and mutually unbiased bases, and self-testing of two-qutrit quantum systems | 2019 | 33 |
| 16 | 2017 | 32 | |
| 17 | 2020 | 31 | |
| 18 | 2020 | 24 | |
| 19 | 2022 | 24 | |
| 20 | 2021 | 22 |
About Ivan Šupić
Ivan Šupić is a scholar working on Artificial Intelligence, Atomic and Molecular Physics, and Optics, Biophysics, Nuclear Energy and Engineering and Waste Management and Disposal, having authored 37 papers that have together received 1.2k indexed citations. Recurring topics across this work include Quantum Information and Cryptography (34 papers), Quantum Mechanics and Applications (13 papers) and Electron Spin Resonance Studies (1 paper). The work is most often cited by research in Artificial Intelligence (1.1k citations), Atomic and Molecular Physics, and Optics (505 citations), Statistical and Nonlinear Physics (9 citations), Surfaces, Coatings and Films (3 citations) and Mathematical Physics (4 citations). Ivan Šupić has collaborated with scholars based in Spain, Switzerland and France. Frequent co-authors include Joseph Bowles, Daniel Cavalcanti, Antonio Acín, Paul Skrzypczyk, Remigiusz Augusiak, Matty J. Hoban, Nicolas Brunner, Andrea Coladangelo, Alexia Salavrakos and Jędrzej Kaniewski. Their work appears in journals such as Physical Review Letters, Physical Review A, Quantum, PRX Quantum and New Journal of Physics.
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.