Michele Grossi
- Artificial Intelligence top 5%
- Atomic and Molecular Physics, and Optics
- Computational Theory and Mathematics top 10%
- Nuclear and High Energy Physics
- Electrical and Electronic Engineering
- Co-authors
- S. VallecorsaAntonio MandarinoF. SánchezPavel LougovskiT. PapenbrockAlessandro RoggeroIvano TavernelliC. von Altrock
- Topics
- Quantum Computing Algorithms and Architecture (35 papers)Quantum Information and Cryptography (23 papers)Neural Networks and Reservoir Computing (7 papers)
- Cited by
- Artificial IntelligenceAtomic and Molecular Physics, and OpticsNuclear and High Energy Physics
- Journals
- SHILAP Revista de lepidopterologíaScientific ReportsJournal of High Energy Physics
- Partner nations
- SwitzerlandItalySpain
In The Last Decade
Michele Grossi
44 papers receiving 397 citations
Peers
Comparison fields: 5 of 46
- Artificial Intelligence 319
- Atomic and Molecular Physics, and Optics 160
- Computational Theory and Mathematics 43
- Nuclear and High Energy Physics 43
- Electrical and Electronic Engineering 36
Countries citing papers authored by Michele Grossi
This map shows the geographic impact of Michele Grossi'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 Michele Grossi with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Michele Grossi more than expected).
Fields of papers citing papers by Michele Grossi
This network shows the impact of papers produced by Michele Grossi. 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 Michele Grossi. The network helps show where Michele Grossi may publish in the future.
Co-authorship network of co-authors of Michele Grossi
This figure shows the co-authorship network connecting the top 25 collaborators of Michele Grossi. A scholar is included among the top collaborators of Michele Grossi 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 Michele Grossi. Michele Grossi is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 1 | |
| 2 | 7 | |
| 3 | 21 | |
| 4 | 11 | |
| 5 | 9 | |
| 6 | 16 | |
| 7 | 4 | |
| 8 | 9 | |
| 9 | 6 | |
| 10 | 4 | |
| 11 | 3 | |
| 12 | 0 | |
| 13 | 4 | |
| 14 | 24 | |
| 15 | 16 | |
| 16 | 3 | |
| 17 | 13 | |
| 18 | 11 | |
| 19 | 5 | |
| 20 | 1 |
About Michele Grossi
Michele Grossi is a scholar working on Artificial Intelligence, Atomic and Molecular Physics, and Optics and Nuclear and High Energy Physics, having authored 47 papers that have together received 412 indexed citations. Recurring topics across this work include Quantum Computing Algorithms and Architecture (35 papers), Quantum Information and Cryptography (23 papers) and Neural Networks and Reservoir Computing (7 papers). The work is most often cited by research in Artificial Intelligence (319 citations), Atomic and Molecular Physics, and Optics (160 citations) and Nuclear and High Energy Physics (43 citations). Michele Grossi has collaborated with scholars based in Switzerland, Italy and Spain. Frequent co-authors include S. Vallecorsa, Antonio Mandarino, F. Sánchez, Pavel Lougovski, T. Papenbrock, Alessandro Roggero, Ivano Tavernelli, C. von Altrock, Francesco Tacchino and Giovanni Pelliccioli. Their work appears in journals such as SHILAP Revista de lepidopterología, Scientific Reports and Journal of High Energy 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.