Andrew Sornborger

3.1k total citations · 1 hit paper
94 papers, 1.8k citations indexed

About

Andrew Sornborger is a scholar working on Artificial Intelligence, Cognitive Neuroscience and Electrical and Electronic Engineering. According to data from OpenAlex, Andrew Sornborger has authored 94 papers receiving a total of 1.8k indexed citations (citations by other indexed papers that have themselves been cited), including 35 papers in Artificial Intelligence, 28 papers in Cognitive Neuroscience and 19 papers in Electrical and Electronic Engineering. Recurrent topics in Andrew Sornborger's work include Neural dynamics and brain function (27 papers), Quantum Computing Algorithms and Architecture (23 papers) and Quantum Information and Cryptography (22 papers). Andrew Sornborger is often cited by papers focused on Neural dynamics and brain function (27 papers), Quantum Computing Algorithms and Architecture (23 papers) and Quantum Information and Cryptography (22 papers). Andrew Sornborger collaborates with scholars based in United States, United Kingdom and Switzerland. Andrew Sornborger's co-authors include Patrick J. Coles, Łukasz Cincio, Zoë Holmes, Robert Brandenberger, Viatcheslav Mukhanov, C. Matthias, Hsin-Yuan Huang, M. Cerezo, Kunal Sharma and Sumeet Khatri and has published in prestigious journals such as Physical Review Letters, Nature Communications and SHILAP Revista de lepidopterología.

In The Last Decade

Andrew Sornborger

88 papers receiving 1.8k citations

Hit Papers

Generalization in quantum machine learning from few train... 2022 2026 2023 2024 2022 50 100 150 200

Peers

Andrew Sornborger
Comparison fields: 5 of 131
  • Artificial Intelligence 953
  • Atomic and Molecular Physics, and Optics 492
  • Astronomy and Astrophysics 299
  • Nuclear and High Energy Physics 286
  • Cognitive Neuroscience 199
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Citations per field, relative to Andrew Sornborger
Andrew Sornborger · 1×
Citations per year, relative to Andrew Sornborger
Andrew Sornborger · 1×

Countries citing papers authored by Andrew Sornborger

Since Specialization
Citations

This map shows the geographic impact of Andrew Sornborger'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 Andrew Sornborger with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Andrew Sornborger more than expected).

Fields of papers citing papers by Andrew Sornborger

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Andrew Sornborger. 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 Andrew Sornborger. The network helps show where Andrew Sornborger may publish in the future.

Co-authorship network of co-authors of Andrew Sornborger

This figure shows the co-authorship network connecting the top 25 collaborators of Andrew Sornborger. A scholar is included among the top collaborators of Andrew Sornborger 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 Andrew Sornborger. Andrew Sornborger is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
# Work Indexed citations
1 4
2 13
3 4
4 19
5 26
6 1
7
Generalization in quantum machine learning from few training data breakdown →
244
8 20
9 37
10 15
11 10
12 1
13 1
14 20
15 4
16 3
17 52
18 16
19 3
20
Persistence Amplitudes from Numerical Quantum Gravity
3

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.

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