S. Vallecorsa
- Artificial Intelligence top 5%
- Quantum Computing Algorithms and Architecture 30
- Quantum Information and Cryptography 17
- Computational Physics and Python Applications 12
- Nuclear and High Energy Physics top 10%
- Particle physics theoretical and experimental studies 15
- Particle Detector Development and Performance 11
- High-Energy Particle Collisions Research 5
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- Generative Adversarial Networks and Image Synthesis 9
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- Parallel Computing and Optimization Techniques 6
- Co-authors
- Michele GrossiFederico CarminatiElías F. CombarroAntonio MandarinoCenk TüysüzJosé RanillaF. SánchezPavel Lougovski
- Journals
- Quantum Science and Technology (2 papers)Machine Learning Science and Technology (2 papers)Physical Review Research (2 papers)
- Partner nations
- SwitzerlandSpainUnited States
In The Last Decade
S. Vallecorsa
50 papers receiving 561 citations
Peers
Comparison fields: 5 of 52
- Artificial Intelligence 380
- Nuclear and High Energy Physics 148
- Computational Mathematics 3
- Atomic and Molecular Physics, and Optics 137
- Statistical and Nonlinear Physics 48
Countries citing papers authored by S. Vallecorsa
This map shows the geographic impact of S. Vallecorsa'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 S. Vallecorsa with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites S. Vallecorsa more than expected).
Fields of papers citing papers by S. Vallecorsa
This network shows the impact of papers produced by S. Vallecorsa. 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 S. Vallecorsa. The network helps show where S. Vallecorsa may publish in the future.
Co-authorship network
The 25 scholars most cited alongside S. Vallecorsa, 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 | 1 | |
| 2 | 2024 | 21 | |
| 3 | 2024 | 9 | |
| 4 | 2024 | 16 | |
| 5 | 2024 | 4 | |
| 6 | 2024 | 9 | |
| 7 | 2024 | 6 | |
| 8 | 2024 | 0 | |
| 9 | 2024 | 4 | |
| 10 | 2023 | 24 | |
| 11 | 2023 | 3 | |
| 12 | 2023 | 2 | |
| 13 | 2023 | 11 | |
| 14 | 2023 | 32 | |
| 15 | 2023 | 0 | |
| 16 | 2022 | 1 | |
| 17 | 2021 | 37 | |
| 18 | 2021 | 4 | |
| 19 | 2021 | 1 | |
| 20 | 2021 | 14 |
About S. Vallecorsa
S. Vallecorsa is a scholar working on Nuclear and High Energy Physics, Artificial Intelligence and Hardware and Architecture, having authored 58 papers that have together received 569 indexed citations. Recurring topics across this work include Quantum Computing Algorithms and Architecture (30 papers), Quantum Information and Cryptography (17 papers), Particle physics theoretical and experimental studies (15 papers), Computational Physics and Python Applications (12 papers), Particle Detector Development and Performance (11 papers), Generative Adversarial Networks and Image Synthesis (9 papers), Parallel Computing and Optimization Techniques (6 papers) and High-Energy Particle Collisions Research (5 papers). The work is most often cited by research in Artificial Intelligence (380 citations), Nuclear and High Energy Physics (148 citations) and Computational Mathematics (3 citations). S. Vallecorsa has collaborated with scholars based in Switzerland, Spain and United States. Frequent co-authors include Michele Grossi, Federico Carminati, Elías F. Combarro, Antonio Mandarino, Cenk Tüysüz, José Ranilla, F. Sánchez, Pavel Lougovski, Xi Li and Chen Wu. Their work appears in journals such as Quantum Science and Technology, Machine Learning Science and Technology, Physical Review Research, npj Quantum Information and Physical review. C.
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.