Nathan Wiebe
- Artificial Intelligence top 1%
- Atomic and Molecular Physics, and Optics top 5%
- Computational Theory and Mathematics top 2%
- Electrical and Electronic Engineering
- Statistical and Nonlinear Physics top 10%
- Co-authors
- Andrew M. ChildsSeth LloydDaniel BraunRyan BabbushJarrod R. McCleanDominic W. BerryCraig GidneyWilliam J. Huggins
- Topics
- Quantum Computing Algorithms and Architecture (20 papers)Quantum Information and Cryptography (17 papers)Quantum and electron transport phenomena (12 papers)
- Cited by
- Artificial IntelligenceAtomic and Molecular Physics, and OpticsComputational Theory and Mathematics
- Partner nations
- United StatesCanadaAustralia
In The Last Decade
Nathan Wiebe
25 papers receiving 1.5k citations
Hit Papers
Peers
Comparison fields: 5 of 65
- Artificial Intelligence 1.4k
- Atomic and Molecular Physics, and Optics 826
- Computational Theory and Mathematics 312
- Electrical and Electronic Engineering 117
- Statistical and Nonlinear Physics 56
Countries citing papers authored by Nathan Wiebe
This map shows the geographic impact of Nathan Wiebe'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 Nathan Wiebe with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Nathan Wiebe more than expected).
Fields of papers citing papers by Nathan Wiebe
This network shows the impact of papers produced by Nathan Wiebe. 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 Nathan Wiebe. The network helps show where Nathan Wiebe may publish in the future.
Co-authorship network of co-authors of Nathan Wiebe
This figure shows the co-authorship network connecting the top 25 collaborators of Nathan Wiebe. A scholar is included among the top collaborators of Nathan Wiebe 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 Nathan Wiebe. Nathan Wiebe is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 5 | |
| 2 | 9 | |
| 3 | 2 | |
| 4 | 2 | |
| 5 | 9 | |
| 6 | 13 | |
| 7 | 24 | |
| 8 | 23 | |
| 9 | 2 | |
| 10 | 4 | |
| 11 | 30 | |
| 12 | Even More Efficient Quantum Computations of Chemistry Through Tensor Hypercontractionbreakdown → | 189 |
| 13 | 54 | |
| 14 | 4 | |
| 15 | 104 | |
| 16 | 226 | |
| 17 | 12 | |
| 18 | Quantum Algorithm for Data Fittingbreakdown → | 320 |
| 19 | 223 | |
| 20 | 0 |
About Nathan Wiebe
Nathan Wiebe is a scholar working on Artificial Intelligence, Atomic and Molecular Physics, and Optics and Hardware and Architecture, having authored 26 papers that have together received 1.6k indexed citations. Recurring topics across this work include Quantum Computing Algorithms and Architecture (20 papers), Quantum Information and Cryptography (17 papers) and Quantum and electron transport phenomena (12 papers). The work is most often cited by research in Artificial Intelligence (1.4k citations), Atomic and Molecular Physics, and Optics (826 citations) and Computational Theory and Mathematics (312 citations). Nathan Wiebe has collaborated with scholars based in United States, Canada and Australia. Frequent co-authors include Andrew M. Childs, Seth Lloyd, Daniel Braun, Ryan Babbush, Jarrod R. McClean, Dominic W. Berry, Craig Gidney, William J. Huggins, Austin G. Fowler and Hartmut Neven. Their work appears in journals such as Physical Review Letters, New Journal of Physics and Journal of Mathematical 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.