Michael Mc Gettrick

405 citations
12 papers · 264 · h-index 6

Impact in

Papers in

Michael Mc Gettrick

11 papers receiving 245 citations

Peers

Michael Mc Gettrick
Comparison fields: 5 of 41
  • Computational Theory and Mathematics 133
  • Hardware and Architecture 45
  • Artificial Intelligence 140
  • Industrial and Manufacturing Engineering 24
  • Computer Networks and Communications 44
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Jun Gu Taiwan
Ruslan Shaydulin United States
Rishi Saket United States
Florian Neukart Netherlands
Krzysztof Diks Poland
Amitabh Chaudhary United States
Jennifer Paykin United States
L. Csanky United States
Sourav Chakraborty United States
Thomas Thierauf Germany
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Citations per field
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Citations per year

Countries citing papers authored by Michael Mc Gettrick

Since Specialization
Citations

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

Fields of papers citing papers by Michael Mc Gettrick

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Michael Mc Gettrick. 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 Michael Mc Gettrick. The network helps show where Michael Mc Gettrick may publish in the future.

Co-authors

The 20 scholars most cited alongside Michael Mc Gettrick, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Michael Mc Gettrick Line = papers co-authored together Michael Mc Gettrick links everyone, so they are left out of the graph.

All Works

12 of 12 papers shown
#Work
1 1998118
2 201165
3 201138
4 201614
5 201412
6 20165
7 20204
8 20133
9 20202
10
Quantum walks on two kinds of two-dimensional models
20181
11 20041
12 20201

About Michael Mc Gettrick

Michael Mc Gettrick is a scholar working on Artificial Intelligence, Atomic and Molecular Physics, and Optics, Computational Theory and Mathematics, Information Systems and Numerical Analysis, having authored 12 papers that have together received 264 indexed citations. Recurring topics across this work include Quantum Computing Algorithms and Architecture (9 papers), Quantum Information and Cryptography (7 papers), Quantum-Dot Cellular Automata (4 papers), Quantum and electron transport phenomena (3 papers), Cloud Computing and Resource Management (2 papers), Quantum Mechanics and Applications (2 papers), Cryptography and Residue Arithmetic (1 paper) and Parallel Computing and Optimization Techniques (1 paper). The work is most often cited by research in Computational Theory and Mathematics (133 citations), Hardware and Architecture (45 citations), Artificial Intelligence (140 citations), Industrial and Manufacturing Engineering (24 citations) and Computer Networks and Communications (44 citations). Michael Mc Gettrick has collaborated with scholars based in Ireland, China and France. Frequent co-authors include Carlo D. Franco, Thomas Busch, Guy Cohen, Stéphane Gaubert, Jean-Pierre Quadrat, Jarosław Adam Miszczak, Qiaoyan Wen, Fei Gao, Dan Li and Juan Xu. Their work appears in journals such as Physical review. A, Physical Review A, Physica A Statistical Mechanics and its Applications, Physical Review Letters and Public Transport.

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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