Jay Lawrence

484 citations
12 papers · 332 · h-index 10

Impact in

Papers in

Jay Lawrence

12 papers receiving 324 citations

Peers

Jay Lawrence
Comparison fields: 5 of 35
  • Atomic and Molecular Physics, and Optics 244
  • Artificial Intelligence 253
  • Discrete Mathematics and Combinatorics 11
  • Statistical and Nonlinear Physics 38
  • Computational Theory and Mathematics 38
Replace J. L. Romero with:
J. L. Romero Mexico
Cihan Okay Canada
Chun-Hong Zhang China
Martin Fraas Switzerland
Mladen Pavičić Croatia
Robert Zeier Germany
А. М. Чеботарев Russia
Lech Jakóbczyk Poland
Brent Doran United States
Samuel Colin France
Jay Lawrence relative to J. L. Romero Mexico J. L. Romero's profile →
Citations per field
00.5×1.5×2.2×
J. L. Romero · 1×
Citations per year

Countries citing papers authored by Jay Lawrence

Since Specialization
Citations

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

Fields of papers citing papers by Jay Lawrence

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 14 scholars most cited alongside Jay Lawrence, 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 Jay Lawrence Line = papers co-authored together Jay Lawrence links everyone, so they are left out of the graph.

All Works

12 of 12 papers shown
#Work
1 2002122
2 201879
3 200439
4 201420
5 200215
6 201114
7 201711
8 202310
9 200210
10 20159
11
Mutually unbiased bases and trinary operator sets for N qutrits (10 pages)
20042
12 20231

About Jay Lawrence

Jay Lawrence is a scholar working on Atomic and Molecular Physics, and Optics, Artificial Intelligence, Statistical and Nonlinear Physics, Computer Networks and Communications and Geometry and Topology, having authored 12 papers that have together received 332 indexed citations. Recurring topics across this work include Quantum Information and Cryptography (10 papers), Quantum Mechanics and Applications (9 papers), Quantum Computing Algorithms and Architecture (6 papers), Advanced Thermodynamics and Statistical Mechanics (3 papers), Nonlinear Dynamics and Pattern Formation (1 paper), Quantum optics and atomic interactions (1 paper), Graph theory and applications (1 paper) and Spectroscopy and Quantum Chemical Studies (1 paper). The work is most often cited by research in Atomic and Molecular Physics, and Optics (244 citations), Artificial Intelligence (253 citations), Discrete Mathematics and Combinatorics (11 citations), Statistical and Nonlinear Physics (38 citations) and Computational Theory and Mathematics (38 citations). Jay Lawrence has collaborated with scholars based in United States, Austria and Mexico. Frequent co-authors include Časlav Brukner, Anton Zeilinger, Leonidas E. Ocola, David A. Czaplewski, Jens Koch, Ravi Naik, Nelson L. C. Leung, Nathan Earnest, Brian Baker and Yao Lu. Their work appears in journals such as Physical Review A, Journal of Optics B Quantum and Semiclassical Optics, Europhysics Letters (EPL), Physical review. A and Quantum.

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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