Gabriel Perdue

3.5k total citations
30 papers, 290 citations indexed

About

Gabriel Perdue is a scholar working on Artificial Intelligence, Nuclear and High Energy Physics and Atomic and Molecular Physics, and Optics. According to data from OpenAlex, Gabriel Perdue has authored 30 papers receiving a total of 290 indexed citations (citations by other indexed papers that have themselves been cited), including 16 papers in Artificial Intelligence, 10 papers in Nuclear and High Energy Physics and 8 papers in Atomic and Molecular Physics, and Optics. Recurrent topics in Gabriel Perdue's work include Quantum Computing Algorithms and Architecture (9 papers), Particle physics theoretical and experimental studies (8 papers) and Quantum Information and Cryptography (6 papers). Gabriel Perdue is often cited by papers focused on Quantum Computing Algorithms and Architecture (9 papers), Particle physics theoretical and experimental studies (8 papers) and Quantum Information and Cryptography (6 papers). Gabriel Perdue collaborates with scholars based in United States, Canada and Italy. Gabriel Perdue's co-authors include Andy C. Y. Li, Rajan Gupta, J. Carlson, Alessandro Roggero, Steven R. Young, Travis Johnston, Robert M. Patton, Sandeep Madireddy, B. Nord and Thomas E. Potok and has published in prestigious journals such as Monthly Notices of the Royal Astronomical Society, Physical review. D and Nuclear Instruments and Methods in Physics Research Section A Accelerators Spectrometers Detectors and Associated Equipment.

In The Last Decade

Gabriel Perdue

24 papers receiving 278 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Gabriel Perdue United States 10 154 92 65 42 37 30 290
Jean-Roch Vlimant United States 13 272 1.8× 55 0.6× 219 3.4× 36 0.9× 44 1.2× 39 502
M.D. Galanis Greece 10 77 0.5× 63 0.7× 243 3.7× 44 1.0× 62 1.7× 34 492
M. Stipčević Croatia 14 184 1.2× 183 2.0× 80 1.2× 85 2.0× 65 1.8× 42 452
Johann Brehmer United States 12 149 1.0× 21 0.2× 454 7.0× 30 0.7× 24 0.6× 23 600
Gustavo Cancelo United States 11 81 0.5× 78 0.8× 193 3.0× 300 7.1× 10 0.3× 56 487
W. Gässler Germany 8 96 0.6× 48 0.5× 24 0.4× 19 0.5× 35 0.9× 40 350
Giuseppe Genovese Switzerland 10 106 0.7× 40 0.4× 21 0.3× 23 0.5× 62 1.7× 21 269
Eric Metodiev United States 10 248 1.6× 103 1.1× 436 6.7× 20 0.5× 17 0.5× 18 606
Alexander Zlokapa United States 6 134 0.9× 75 0.8× 40 0.6× 16 0.4× 15 0.4× 11 214
Won-Ho Kye South Korea 14 92 0.6× 82 0.9× 15 0.2× 45 1.1× 41 1.1× 32 579

Countries citing papers authored by Gabriel Perdue

Since Specialization
Citations

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

Fields of papers citing papers by Gabriel Perdue

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Gabriel Perdue

This figure shows the co-authorship network connecting the top 25 collaborators of Gabriel Perdue. A scholar is included among the top collaborators of Gabriel Perdue 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 Gabriel Perdue. Gabriel Perdue 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
1.
Guglielmo, Giuseppe Di, Botao Du, Akash Dixit, et al.. (2025). End-to-End Workflow for Machine-Learning-Based Qubit Readout With QICK and hls4ml. IEEE Transactions on Quantum Engineering. 6. 1–10.
2.
Roy, Tanay, Joshua Job, Namit Anand, et al.. (2025). Benchmarking the performance of a high-Q cavity qudit using random unitaries. Quantum Science and Technology. 10(2). 25062–25062. 2 indexed citations
3.
Bhat, Mohsin Ahmad, Marco Russo, Luca P. Carloni, et al.. (2025). Machine learning for arbitrary single-qubit rotations on an embedded device. Quantum Machine Intelligence. 7(1).
4.
Hobbs, T. J., et al.. (2024). Confinement and Kink Entanglement Asymmetry on a Quantum Ising Chain. Quantum. 8. 1462–1462. 20 indexed citations
5.
Pedro, K., et al.. (2023). DeepAstroUDA: semi-supervised universal domain adaptation for cross-survey galaxy morphology classification and anomaly detection. Machine Learning Science and Technology. 4(2). 25013–25013. 8 indexed citations
6.
Li, Andy C. Y., M. Sohaib Alam, Thomas Iadecola, et al.. (2023). Benchmarking variational quantum eigensolvers for the square-octagon-lattice Kitaev model. Physical Review Research. 5(3). 14 indexed citations
7.
Guglielmo, Giuseppe Di, et al.. (2023). Neural network accelerator for quantum control. OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information). 1 indexed citations
8.
Pedro, K., et al.. (2023). Semi-Supervised Domain Adaptation for Cross-Survey Galaxy Morphology Classification and Anomaly Detection. OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information). 1 indexed citations
9.
Peters, Evan, Andy C. Y. Li, & Gabriel Perdue. (2023). Perturbative readout-error mitigation for near-term quantum computers. Physical review. A. 107(6). 2 indexed citations
10.
Snyder, Gregory F., Javier Sánchez, Gabriel Perdue, et al.. (2022). DeepAdversaries: examining the robustness of deep learning models for galaxy morphology classification. Machine Learning Science and Technology. 3(3). 35007–35007. 14 indexed citations
11.
Vidal, Júlia Tena, C. Andreopoulos, Adi Ashkenazi, et al.. (2021). Neutrino-nucleon cross-section model tuning in GENIE v3. arXiv (Cornell University). 22 indexed citations
12.
Perdue, Gabriel, et al.. (2021). DeepMerge – II. Building robust deep learning algorithms for merging galaxy identification across domains. Monthly Notices of the Royal Astronomical Society. 506(1). 677–691. 31 indexed citations
13.
Herwig, T. C., et al.. (2020). Real-time Artificial Intelligence for Accelerator Control: A Study at the Fermilab Booster. arXiv (Cornell University). 28 indexed citations
14.
Perdue, Gabriel, et al.. (2017). Vertex reconstruction of neutrino interactions using deep learning. OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information). 2275–2281. 6 indexed citations
15.
Genser, K., Robert Hatcher, M.H. Kelsey, et al.. (2017). A Software Toolkit to Study Systematic Uncertainties of the Physics Models of the Geant4 Simulation Package. Journal of Physics Conference Series. 898. 42052–42052. 1 indexed citations
16.
Young, Steven R., Derek Rose, Travis Johnston, et al.. (2017). Evolving Deep Networks Using HPC. OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information). 1–7. 30 indexed citations
17.
Fuess, S., O. Gutsche, M. Kirby, et al.. (2015). Fermilab Computing at the Intensity Frontier. Journal of Physics Conference Series. 664(3). 32012–32012. 1 indexed citations
18.
Perdue, Gabriel. (2013). MINERνA status and event reconstruction. Journal of Physics Conference Series. 408. 12037–12037.
19.
Nix, J.R., et al.. (2010). Blind background prediction using a bifurcated analysis scheme. Nuclear Instruments and Methods in Physics Research Section A Accelerators Spectrometers Detectors and Associated Equipment. 615(2). 223–229.
20.
Daft, Richard L., et al.. (1998). CREATING A NEW FUTURE FOR PUBLIC TRANSPORTATION: TCRP'S STRATEGIC ROAD MAP. 1 indexed citations

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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