Daniel J. Egger
- Artificial Intelligence top 1%
- Quantum Computing Algorithms and Architecture 30
- Quantum Information and Cryptography 28
- Stochastic Gradient Optimization Techniques 4
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- Quantum and electron transport phenomena 13
- Quantum Mechanics and Applications 4
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- Particle Accelerators and Free-Electron Lasers 9
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- Particle Detector Development and Performance 7
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- Particle accelerators and beam dynamics 4
Daniel J. Egger
50 papers receiving 1.2k citations
Peers
Comparison fields: 5 of 93
- Artificial Intelligence 987
- Atomic and Molecular Physics, and Optics 620
- Computational Theory and Mathematics 181
- Computational Mathematics 3
- Hardware and Architecture 27
Countries citing papers authored by Daniel J. Egger
This map shows the geographic impact of Daniel J. Egger'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 Daniel J. Egger with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Daniel J. Egger more than expected).
Fields of papers citing papers by Daniel J. Egger
This network shows the impact of papers produced by Daniel J. Egger. 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 Daniel J. Egger. The network helps show where Daniel J. Egger may publish in the future.
Co-authorship network
The 25 scholars most cited alongside Daniel J. Egger, 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 | 0 | |
| 2 | 2025 | 4 | |
| 3 | 2024 | 9 | |
| 4 | 2024 | 8 | |
| 5 | 2024 | 0 | |
| 6 | 2024 | 0 | |
| 7 | 2024 | 22 | |
| 8 | 2024 | 7 | |
| 9 | 2023 | 1 | |
| 10 | 2023 | 13 | |
| 11 | 2023 | 16 | |
| 12 | 2023 | 9 | |
| 13 | 2023 | 27 | |
| 14 | 2022 | 2 | |
| 15 | 2021 | 151 | |
| 16 | 2019 | 11 | |
| 17 | 2019 | 5 | |
| 18 | 2014 | 83 | |
| 19 | 2013 | 37 | |
| 20 | 2006 | 26 |
About Daniel J. Egger
Daniel J. Egger is a scholar working on Artificial Intelligence, Atomic and Molecular Physics, and Optics, Nuclear and High Energy Physics, Computational Theory and Mathematics and Process Chemistry and Technology, having authored 55 papers that have together received 1.2k indexed citations. Recurring topics across this work include Quantum Computing Algorithms and Architecture (30 papers), Quantum Information and Cryptography (28 papers), Quantum and electron transport phenomena (13 papers), Particle Accelerators and Free-Electron Lasers (9 papers), Particle Detector Development and Performance (7 papers), Particle accelerators and beam dynamics (4 papers), Quantum Mechanics and Applications (4 papers) and Stochastic Gradient Optimization Techniques (4 papers). The work is most often cited by research in Artificial Intelligence (987 citations), Atomic and Molecular Physics, and Optics (620 citations), Computational Theory and Mathematics (181 citations), Computational Mathematics (3 citations) and Hardware and Architecture (27 citations). Daniel J. Egger has collaborated with scholars based in Switzerland, United States and Germany. Frequent co-authors include Stefan Woerner, Frank K. Wilhelm, Jakub Mareček, Ivano Tavernelli, Marc Ganzhorn, Stefan Filipp, Gian Salis, Andreas Fuhrer, Panagiotis Kl. Barkoutsos and Nikolaj Moll. Their work appears in journals such as Physical review. A, Physical Review Research, Quantum, Physical Review A and INFORMS journal on computing.
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.