Michael J. Gullans
-
- Quantum and electron transport phenomena 27
- Cold Atom Physics and Bose-Einstein Condensates 18
- Quantum many-body systems 14
- Strong Light-Matter Interactions 13
- Quantum optics and atomic interactions 13
- Semiconductor Quantum Structures and Devices 11
- Artificial Intelligence top 0.5%
- Quantum Information and Cryptography 37
- Quantum Computing Algorithms and Architecture 24
- Acoustics and Ultrasonics top 10%
- Computational Mathematics top 10%
Michael J. Gullans
69 papers receiving 3.0k citations
Hit Papers
Peers
Comparison fields: 5 of 58
- Atomic and Molecular Physics, and Optics 2.7k
- Artificial Intelligence 1.6k
- Statistical and Nonlinear Physics 322
- Acoustics and Ultrasonics 23
- Computational Mathematics 14
Countries citing papers authored by Michael J. Gullans
This map shows the geographic impact of Michael J. Gullans'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 J. Gullans with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Michael J. Gullans more than expected).
Fields of papers citing papers by Michael J. Gullans
This network shows the impact of papers produced by Michael J. Gullans. 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 J. Gullans. The network helps show where Michael J. Gullans may publish in the future.
Co-authorship network
The 25 scholars most cited alongside Michael J. Gullans, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | 2025 | 13 | |
| 2 | 2025 | 2 | |
| 3 | 2024 | 39 | |
| 4 | 2024 | 3 | |
| 5 | 2024 | 8 | |
| 6 | 2024 | 8 | |
| 7 | 2023 | 16 | |
| 8 | 2023 | 20 | |
| 9 | 2022 | 1 | |
| 10 | 2022 | 0 | |
| 11 | Two-qubit silicon quantum processor with operation fidelity exceeding 99%breakdown → | 2022 | 196 |
| 12 | 2022 | 94 | |
| 13 | Measurement-induced quantum phases realized in a trapped-ion quantum computerbreakdown → | 2022 | 157 |
| 14 | 2022 | 31 | |
| 15 | 2021 | 5 | |
| 16 | Critical properties of the measurement-induced transition in random quantum circuitsbreakdown → | 2020 | 235 |
| 17 | 2020 | 3 | |
| 18 | 2018 | 98 | |
| 19 | 2017 | 14 | |
| 20 | 2007 | 4 |
About Michael J. Gullans
Michael J. Gullans is a scholar working on Atomic and Molecular Physics, and Optics, Artificial Intelligence and Computational Theory and Mathematics, having authored 73 papers that have together received 3.1k indexed citations. Recurring topics across this work include Quantum Information and Cryptography (37 papers), Quantum and electron transport phenomena (27 papers), Quantum Computing Algorithms and Architecture (24 papers), Cold Atom Physics and Bose-Einstein Condensates (18 papers), Quantum many-body systems (14 papers), Strong Light-Matter Interactions (13 papers), Quantum optics and atomic interactions (13 papers) and Semiconductor Quantum Structures and Devices (11 papers). The work is most often cited by research in Atomic and Molecular Physics, and Optics (2.7k citations), Artificial Intelligence (1.6k citations) and Statistical and Nonlinear Physics (322 citations). Michael J. Gullans has collaborated with scholars based in United States, Germany and Canada. Frequent co-authors include David A. Huse, J. R. Petta, Mikhail D. Lukin, Sarang Gopalakrishnan, Vladan Vuletić, Alexey V. Gorshkov, Jeff D. Thompson, Adam Mills, Justin H. Wilson and Aidan Zabalo. Their work appears in journals such as Science, Physical Review Letters and Nature Communications.
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.