Abhinav Anand
- Artificial Intelligence top 1%
- Atomic and Molecular Physics, and Optics top 5%
- Computational Theory and Mathematics top 5%
- Electrical and Electronic Engineering
- Materials Chemistry
- Co-authors
- Alán Aspuru‐GuzikMatthias DegrooteAlba Cervera-LiertaSukin SimThi Ha KyawJakob S. KottmannKishor BhartiHermanni Heimonen
- Topics
- Quantum Computing Algorithms and Architecture (8 papers)Quantum Information and Cryptography (6 papers)Crystallization and Solubility Studies (2 papers)
- Cited by
- Artificial IntelligenceAtomic and Molecular Physics, and OpticsComputational Theory and Mathematics
- Partner nations
- CanadaUnited StatesIndia
In The Last Decade
Abhinav Anand
11 papers receiving 1.0k citations
Hit Papers
Peers
Comparison fields: 5 of 64
- Artificial Intelligence 916
- Atomic and Molecular Physics, and Optics 474
- Computational Theory and Mathematics 169
- Electrical and Electronic Engineering 93
- Materials Chemistry 47
Countries citing papers authored by Abhinav Anand
This map shows the geographic impact of Abhinav Anand'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 Abhinav Anand with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Abhinav Anand more than expected).
Fields of papers citing papers by Abhinav Anand
This network shows the impact of papers produced by Abhinav Anand. 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 Abhinav Anand. The network helps show where Abhinav Anand may publish in the future.
Co-authorship network of co-authors of Abhinav Anand
This figure shows the co-authorship network connecting the top 25 collaborators of Abhinav Anand. A scholar is included among the top collaborators of Abhinav Anand 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 Abhinav Anand. Abhinav Anand is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 2 | |
| 2 | 0 | |
| 3 | 2 | |
| 4 | 4 | |
| 5 | Noisy intermediate-scale quantum algorithmsbreakdown → | 955 |
| 6 | 5 | |
| 7 | 12 | |
| 8 | 26 | |
| 9 | 7 | |
| 10 | 13 | |
| 11 | 5 | |
| 12 | 3 |
About Abhinav Anand
Abhinav Anand is a scholar working on Artificial Intelligence, Computational Theory and Mathematics and Atomic and Molecular Physics, and Optics, having authored 12 papers that have together received 1.0k indexed citations. Recurring topics across this work include Quantum Computing Algorithms and Architecture (8 papers), Quantum Information and Cryptography (6 papers) and Crystallization and Solubility Studies (2 papers). The work is most often cited by research in Artificial Intelligence (916 citations), Atomic and Molecular Physics, and Optics (474 citations) and Computational Theory and Mathematics (169 citations). Abhinav Anand has collaborated with scholars based in Canada, United States and India. Frequent co-authors include Alán Aspuru‐Guzik, Matthias Degroote, Alba Cervera-Lierta, Sukin Sim, Thi Ha Kyaw, Jakob S. Kottmann, Kishor Bharti, Hermanni Heimonen, L. C. Kwek and Wai‐Keong Mok. Their work appears in journals such as Reviews of Modern Physics, The Journal of Physical Chemistry B and Journal of Molecular Liquids.
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.