Gerardo A. Paz-Silva
- Artificial Intelligence top 2%
- Quantum Information and Cryptography 20
- Quantum Computing Algorithms and Architecture 15
- Neural Networks and Reservoir Computing 2
-
- Quantum and electron transport phenomena 7
- Quantum Mechanics and Applications 5
- Quantum optics and atomic interactions 4
- Spectroscopy and Quantum Chemical Studies 3
-
- Quantum-Dot Cellular Automata 2
- Co-authors
- Lorenza ViolaLeigh NorrisDaniel A. LidarA. T. RezakhaniJason DominyJason TwamleyTameem AlbashWalter Vinci
- Cited by
- Artificial IntelligenceAtomic and Molecular Physics, and OpticsStatistical and Nonlinear Physics
- Journals
- Physical Review Letters (5 papers)Applied Physics Letters (1 paper)Scientific Reports (1 paper)
- Partner nations
- AustraliaUnited StatesColombia
In The Last Decade
Gerardo A. Paz-Silva
24 papers receiving 565 citations
Peers
Comparison fields: 5 of 37
- Artificial Intelligence 487
- Atomic and Molecular Physics, and Optics 435
- Statistical and Nonlinear Physics 49
- Computational Mathematics 2
- Acoustics and Ultrasonics 2
Countries citing papers authored by Gerardo A. Paz-Silva
This map shows the geographic impact of Gerardo A. Paz-Silva'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 Gerardo A. Paz-Silva with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Gerardo A. Paz-Silva more than expected).
Fields of papers citing papers by Gerardo A. Paz-Silva
This network shows the impact of papers produced by Gerardo A. Paz-Silva. 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 Gerardo A. Paz-Silva. The network helps show where Gerardo A. Paz-Silva may publish in the future.
Co-authorship network
The 25 scholars most cited alongside Gerardo A. Paz-Silva, 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 | 2023 | 2 | |
| 3 | 2023 | 8 | |
| 4 | 2023 | 6 | |
| 5 | 2020 | 24 | |
| 6 | 2019 | 11 | |
| 7 | 2016 | 30 | |
| 8 | 2016 | 91 | |
| 9 | 2015 | 62 | |
| 10 | 2014 | 65 | |
| 11 | 2013 | 25 | |
| 12 | 2012 | 85 | |
| 13 | 2012 | 6 | |
| 14 | 2011 | 3 | |
| 15 | 2010 | 22 | |
| 16 | 2009 | 18 | |
| 17 | 2009 | 2 | |
| 18 | 2009 | 3 | |
| 19 | 2007 | 1 | |
| 20 | 2007 | 3 |
About Gerardo A. Paz-Silva
Gerardo A. Paz-Silva is a scholar working on Artificial Intelligence, Atomic and Molecular Physics, and Optics and Computational Theory and Mathematics, having authored 24 papers that have together received 573 indexed citations. Recurring topics across this work include Quantum Information and Cryptography (20 papers), Quantum Computing Algorithms and Architecture (15 papers), Quantum and electron transport phenomena (7 papers), Quantum Mechanics and Applications (5 papers), Quantum optics and atomic interactions (4 papers), Spectroscopy and Quantum Chemical Studies (3 papers), Neural Networks and Reservoir Computing (2 papers) and Quantum-Dot Cellular Automata (2 papers). The work is most often cited by research in Artificial Intelligence (487 citations), Atomic and Molecular Physics, and Optics (435 citations) and Statistical and Nonlinear Physics (49 citations). Gerardo A. Paz-Silva has collaborated with scholars based in Australia, United States and Colombia. Frequent co-authors include Lorenza Viola, Leigh Norris, Daniel A. Lidar, A. T. Rezakhani, Jason Dominy, Jason Twamley, Tameem Albash, Walter Vinci, Itay Hen and Akram Youssry. Their work appears in journals such as Physical Review Letters, Applied Physics Letters and Scientific Reports.
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.