Matthias Degroote
- Artificial Intelligence top 1%
- Quantum Computing Algorithms and Architecture 17
- Quantum Information and Cryptography 11
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- Quantum and electron transport phenomena 8
- Advanced Chemical Physics Studies 7
- Spectroscopy and Quantum Chemical Studies 5
- Quantum many-body systems 3
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- Machine Learning in Materials Science 3
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- Protein Structure and Dynamics 2
- Co-authors
- Alán Aspuru‐GuzikAbhinav AnandThi Ha KyawJakob S. KottmannSukin SimL. C. KwekHermanni HeimonenTobias Haug
- Cited by
- Artificial IntelligenceAtomic and Molecular Physics, and OpticsComputational Theory and Mathematics
- Journals
- Proceedings of the National Academy of Sciences (1 paper)Physical Review Letters (1 paper)The Journal of Chemical Physics (2 papers)
- Partner nations
- United StatesGermanyCanada
In The Last Decade
Matthias Degroote
26 papers receiving 1.4k citations
Hit Papers
Peers
Comparison fields: 5 of 63
- Artificial Intelligence 1.1k
- Atomic and Molecular Physics, and Optics 726
- Computational Theory and Mathematics 208
- Computational Mathematics 3
- Statistical and Nonlinear Physics 40
Countries citing papers authored by Matthias Degroote
This map shows the geographic impact of Matthias Degroote'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 Matthias Degroote with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Matthias Degroote more than expected).
Fields of papers citing papers by Matthias Degroote
This network shows the impact of papers produced by Matthias Degroote. 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 Matthias Degroote. The network helps show where Matthias Degroote may publish in the future.
Co-authorship network
The 25 scholars most cited alongside Matthias Degroote, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | 2025 | 2 | |
| 2 | 2024 | 8 | |
| 3 | 2024 | 47 | |
| 4 | 2024 | 10 | |
| 5 | 2024 | 3 | |
| 6 | 2024 | 5 | |
| 7 | 2024 | 4 | |
| 8 | 2023 | 10 | |
| 9 | 2023 | 11 | |
| 10 | 2023 | 9 | |
| 11 | Noisy intermediate-scale quantum algorithmsbreakdown → | 2022 | 955 |
| 12 | 2022 | 37 | |
| 13 | 2022 | 65 | |
| 14 | 2022 | 2 | |
| 15 | 2021 | 38 | |
| 16 | 2021 | 12 | |
| 17 | 2021 | 9 | |
| 18 | 2016 | 45 | |
| 19 | 2011 | 16 | |
| 20 | 2010 | 2 |
About Matthias Degroote
Matthias Degroote is a scholar working on Atomic and Molecular Physics, and Optics, Artificial Intelligence and Surfaces, Coatings and Films, having authored 26 papers that have together received 1.4k indexed citations. Recurring topics across this work include Quantum Computing Algorithms and Architecture (17 papers), Quantum Information and Cryptography (11 papers), Quantum and electron transport phenomena (8 papers), Advanced Chemical Physics Studies (7 papers), Spectroscopy and Quantum Chemical Studies (5 papers), Machine Learning in Materials Science (3 papers), Quantum many-body systems (3 papers) and Protein Structure and Dynamics (2 papers). The work is most often cited by research in Artificial Intelligence (1.1k citations), Atomic and Molecular Physics, and Optics (726 citations) and Computational Theory and Mathematics (208 citations). Matthias Degroote has collaborated with scholars based in United States, Germany and Canada. Frequent co-authors include Alán Aspuru‐Guzik, Abhinav Anand, Thi Ha Kyaw, Jakob S. Kottmann, Sukin Sim, L. C. Kwek, Hermanni Heimonen, Tobias Haug, Alba Cervera-Lierta and Kishor Bharti. Their work appears in journals such as Proceedings of the National Academy of Sciences, Physical Review Letters and The Journal of Chemical 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.