Fabio Dell’Anno
- Atomic and Molecular Physics, and Optics top 5%
- Artificial Intelligence top 2%
- Nuclear and High Energy Physics top 10%
- Statistical and Nonlinear Physics top 10%
- Electrical and Electronic Engineering
- Co-authors
- Fabrizio IlluminatiSilvio De SienaMassimo BlasoneGerardo AdessoMarco Di MauroS. M. GiampaoloSimone MontangeroR. De Luca
- Topics
- Quantum Information and Cryptography (19 papers)Quantum Mechanics and Applications (12 papers)Quantum Computing Algorithms and Architecture (10 papers)
- Partner nations
- ItalyUnited KingdomAustria
In The Last Decade
Fabio Dell’Anno
24 papers receiving 849 citations
Peers
Comparison fields: 5 of 40
- Atomic and Molecular Physics, and Optics 785
- Artificial Intelligence 728
- Nuclear and High Energy Physics 96
- Statistical and Nonlinear Physics 67
- Electrical and Electronic Engineering 47
Countries citing papers authored by Fabio Dell’Anno
This map shows the geographic impact of Fabio Dell’Anno'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 Fabio Dell’Anno with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Fabio Dell’Anno more than expected).
Fields of papers citing papers by Fabio Dell’Anno
This network shows the impact of papers produced by Fabio Dell’Anno. 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 Fabio Dell’Anno. The network helps show where Fabio Dell’Anno may publish in the future.
Co-authorship network of co-authors of Fabio Dell’Anno
This figure shows the co-authorship network connecting the top 25 collaborators of Fabio Dell’Anno. A scholar is included among the top collaborators of Fabio Dell’Anno 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 Fabio Dell’Anno. Fabio Dell’Anno is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 7 | |
| 2 | 9 | |
| 3 | 29 | |
| 4 | 14 | |
| 5 | 2 | |
| 6 | 6 | |
| 7 | 13 | |
| 8 | 4 | |
| 9 | 66 | |
| 10 | 35 | |
| 11 | 124 | |
| 12 | 3 | |
| 13 | Heisenberg-limited estimation of losses with non-Gaussian states | 1 |
| 14 | 10 | |
| 15 | 58 | |
| 16 | 153 | |
| 17 | 5 | |
| 18 | 7 | |
| 19 | 7 | |
| 20 | 1 |
About Fabio Dell’Anno
Fabio Dell’Anno is a scholar working on Atomic and Molecular Physics, and Optics, Artificial Intelligence and Nuclear and High Energy Physics, having authored 24 papers that have together received 892 indexed citations. Recurring topics across this work include Quantum Information and Cryptography (19 papers), Quantum Mechanics and Applications (12 papers) and Quantum Computing Algorithms and Architecture (10 papers). The work is most often cited by research in Atomic and Molecular Physics, and Optics (785 citations), Acoustics and Ultrasonics (21 citations) and Artificial Intelligence (728 citations). Fabio Dell’Anno has collaborated with scholars based in Italy, United Kingdom and Austria. Frequent co-authors include Fabrizio Illuminati, Silvio De Siena, Massimo Blasone, Gerardo Adesso, Marco Di Mauro, S. M. Giampaolo, Simone Montangero, R. De Luca, A. Porzio and S. Solimeno. Their work appears in journals such as Physical Review B, Physics Reports and Physical Review A.
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.