Ryan Sweke

1.7k total citations · 1 hit paper
19 papers, 879 citations indexed

About

Ryan Sweke is a scholar working on Artificial Intelligence, Atomic and Molecular Physics, and Optics and Computational Theory and Mathematics. According to data from OpenAlex, Ryan Sweke has authored 19 papers receiving a total of 879 indexed citations (citations by other indexed papers that have themselves been cited), including 16 papers in Artificial Intelligence, 9 papers in Atomic and Molecular Physics, and Optics and 3 papers in Computational Theory and Mathematics. Recurrent topics in Ryan Sweke's work include Quantum Computing Algorithms and Architecture (15 papers), Quantum Information and Cryptography (11 papers) and Machine Learning and Algorithms (4 papers). Ryan Sweke is often cited by papers focused on Quantum Computing Algorithms and Architecture (15 papers), Quantum Information and Cryptography (11 papers) and Machine Learning and Algorithms (4 papers). Ryan Sweke collaborates with scholars based in Germany, South Africa and United States. Ryan Sweke's co-authors include Johannes Jakob Meyer, Maria Schuld, Jens Eisert, Ilya Sinayskiy, Francesco Petruccione, Jean‐Pierre Seifert, Dominik Hangleiter, C. Matthias, Elies Gil-Fuster and Denis Bernard and has published in prestigious journals such as Physical Review Letters, Physical Review A and Journal of Physics B Atomic Molecular and Optical Physics.

In The Last Decade

Ryan Sweke

18 papers receiving 840 citations

Hit Papers

Effect of data encoding on the expressive power of variat... 2021 2026 2022 2024 2021 100 200 300
What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

  1. Effect of data encoding on the expressive power of variational quantum-machine-learning models
    2021 376 citations Maria Schuld, Ryan Sweke et al. Physical review. A profile →

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Ryan Sweke Germany 11 795 326 122 80 37 19 879
Johannes Jakob Meyer Germany 9 885 1.1× 332 1.0× 148 1.2× 88 1.1× 48 1.3× 16 972
Marcello Benedetti United Kingdom 11 875 1.1× 291 0.9× 179 1.5× 107 1.3× 40 1.1× 20 955
Akira Sone United States 11 807 1.0× 396 1.2× 139 1.1× 76 0.9× 34 0.9× 40 889
Kunal Sharma United States 13 1.1k 1.3× 460 1.4× 168 1.4× 109 1.4× 54 1.5× 33 1.2k
Xiaogang Qiang China 11 617 0.8× 378 1.2× 87 0.7× 399 5.0× 27 0.7× 28 833
Abhinav Anand Canada 6 916 1.2× 474 1.5× 169 1.4× 93 1.2× 47 1.3× 12 1.0k
Sukin Sim United States 6 917 1.2× 489 1.5× 166 1.4× 91 1.1× 43 1.2× 11 1.0k
Hermanni Heimonen Singapore 5 888 1.1× 492 1.5× 153 1.3× 84 1.1× 37 1.0× 6 1.0k
Wai‐Keong Mok Singapore 7 949 1.2× 541 1.7× 156 1.3× 103 1.3× 33 0.9× 17 1.1k
Kishor Bharti Singapore 9 1.1k 1.4× 628 1.9× 186 1.5× 88 1.1× 33 0.9× 26 1.2k

Countries citing papers authored by Ryan Sweke

Since Specialization
Citations

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

Fields of papers citing papers by Ryan Sweke

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Ryan Sweke

This figure shows the co-authorship network connecting the top 25 collaborators of Ryan Sweke. A scholar is included among the top collaborators of Ryan Sweke 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 Ryan Sweke. Ryan Sweke is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

19 of 19 papers shown
1.
Sweke, Ryan, et al.. (2025). On the average-case complexity of learning output distributions of quantum circuits. Quantum. 9. 1883–1883. 1 indexed citations
2.
Sweke, Ryan, et al.. (2025). Potential and limitations of random Fourier features for dequantizing quantum machine learning. Quantum. 9. 1640–1640. 4 indexed citations
3.
Haferkamp, Jonas, Yihui Quek, Dominik Hangleiter, et al.. (2023). One T Gate Makes Distribution Learning Hard. Physical Review Letters. 130(24). 240602–240602. 20 indexed citations
4.
Sweke, Ryan, et al.. (2023). Superpolynomial quantum-classical separation for density modeling. Physical review. A. 107(4). 10 indexed citations
5.
Sweke, Ryan, et al.. (2022). Transparent reporting of research-related greenhouse gas emissions through the scientific CO$_2$nduct initiative. arXiv (Cornell University). 8 indexed citations
6.
7.
Schuld, Maria, Ryan Sweke, & Johannes Jakob Meyer. (2021). Effect of data encoding on the expressive power of variational quantum-machine-learning models. Physical review. A. 103(3). 376 indexed citations breakdown →
8.
Matthias, C., Elies Gil-Fuster, Johannes Jakob Meyer, Jens Eisert, & Ryan Sweke. (2021). Encoding-dependent generalization bounds for parametrized quantum circuits. arXiv (Cornell University). 64 indexed citations
9.
Sweke, Ryan, Jean‐Pierre Seifert, Dominik Hangleiter, & Jens Eisert. (2021). On the Quantum versus Classical Learnability of Discrete Distributions. Quantum. 5. 417–417. 52 indexed citations
10.
Sweke, Ryan, et al.. (2020). Stochastic Gradient Descent for Hybrid Quantum-Classical Optimization. Bulletin of the American Physical Society. 5 indexed citations
11.
Sweke, Ryan, et al.. (2020). Stochastic gradient descent for hybrid quantum-classical optimization. Refubium (Universitätsbibliothek der Freien Universität Berlin). 148 indexed citations
12.
Sweke, Ryan, et al.. (2019). Expressive power of tensor-network factorizations for probabilistic modeling. MPG.PuRe (Max Planck Society). 32. 1496–1508. 22 indexed citations
13.
Sweke, Ryan, Jens Eisert, & Michael Kästner. (2019). Lieb–Robinson bounds for open quantum systems with long-ranged interactions. Refubium (Universitätsbibliothek der Freien Universität Berlin). 14 indexed citations
14.
Sweke, Ryan, Markus S. Kesselring, Evert van Nieuwenburg, & Jens Eisert. (2018). Reinforcement Learning Decoders for Fault-Tolerant Quantum Computation. Refubium (Universitätsbibliothek der Freien Universität Berlin). 2019. 8 indexed citations
15.
Sweke, Ryan, Mikel Sanz, Ilya Sinayskiy, Francesco Petruccione, & E. Solano. (2016). Digital quantum simulation of many-body non-Markovian dynamics. Physical review. A. 94(2). 33 indexed citations
16.
Sweke, Ryan, Ilya Sinayskiy, Denis Bernard, & Francesco Petruccione. (2015). Universal simulation of Markovian open quantum systems. Physical Review A. 91(6). 49 indexed citations
17.
Sweke, Ryan, Ilya Sinayskiy, & Francesco Petruccione. (2014). Simulation of single-qubit open quantum systems. Physical Review A. 90(2). 23 indexed citations
18.
Sweke, Ryan, Ilya Sinayskiy, & Francesco Petruccione. (2013). Dissipative preparation of largeWstates in optical cavities. Physical Review A. 87(4). 33 indexed citations
19.
Sweke, Ryan, Ilya Sinayskiy, & Francesco Petruccione. (2013). Dissipative preparation of generalized Bell states. Journal of Physics B Atomic Molecular and Optical Physics. 46(10). 104004–104004. 9 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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