Shai Machnes
- Atomic and Molecular Physics, and Optics top 10%
- Artificial Intelligence top 5%
- Electrical and Electronic Engineering
- Statistical and Nonlinear Physics top 10%
- Spectroscopy
- Co-authors
- Elie AssématFrank K. WilhelmDavid J. TannorThomas Schulte‐HerbrüggenS. G. SchirmerSteffen J. GlaserAudrūnas GruslysMartin B. Plenio
- Topics
- Quantum Information and Cryptography (6 papers)Advanced Thermodynamics and Statistical Mechanics (3 papers)Quantum chaos and dynamical systems (3 papers)
- Cited by
- Atomic and Molecular Physics, and OpticsArtificial IntelligenceStatistical and Nonlinear Physics
- Partner nations
- IsraelGermanyUnited Kingdom
In The Last Decade
Shai Machnes
11 papers receiving 452 citations
Peers
Comparison fields: 5 of 38
- Atomic and Molecular Physics, and Optics 380
- Artificial Intelligence 323
- Electrical and Electronic Engineering 65
- Statistical and Nonlinear Physics 59
- Spectroscopy 36
Countries citing papers authored by Shai Machnes
This map shows the geographic impact of Shai Machnes'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 Shai Machnes with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Shai Machnes more than expected).
Fields of papers citing papers by Shai Machnes
This network shows the impact of papers produced by Shai Machnes. 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 Shai Machnes. The network helps show where Shai Machnes may publish in the future.
Co-authorship network of co-authors of Shai Machnes
This figure shows the co-authorship network connecting the top 25 collaborators of Shai Machnes. A scholar is included among the top collaborators of Shai Machnes 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 Shai Machnes. Shai Machnes is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 0 | |
| 2 | 106 | |
| 3 | 35 | |
| 4 | Gradient Optimization for Analytic conTrols - GOAT | 1 |
| 5 | 9 | |
| 6 | 87 | |
| 7 | 2 | |
| 8 | 172 | |
| 9 | 4 | |
| 10 | 1 | |
| 11 | 29 | |
| 12 | 19 |
About Shai Machnes
Shai Machnes is a scholar working on Statistical and Nonlinear Physics, Atomic and Molecular Physics, and Optics and Artificial Intelligence, having authored 12 papers that have together received 465 indexed citations. Recurring topics across this work include Quantum Information and Cryptography (6 papers), Advanced Thermodynamics and Statistical Mechanics (3 papers) and Quantum chaos and dynamical systems (3 papers). The work is most often cited by research in Atomic and Molecular Physics, and Optics (380 citations), Artificial Intelligence (323 citations) and Statistical and Nonlinear Physics (59 citations). Shai Machnes has collaborated with scholars based in Israel, Germany and United Kingdom. Frequent co-authors include Elie Assémat, Frank K. Wilhelm, David J. Tannor, Thomas Schulte‐Herbrüggen, S. G. Schirmer, Steffen J. Glaser, Audrūnas Gruslys, Martin B. Plenio, Alex Retzker and Javier Cerrillo. Their work appears in journals such as Physical Review Letters, Physical Review A and The Journal of Physical Chemistry A.
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.