Xingyao Wu

602 total citations
11 papers, 316 citations indexed

About

Xingyao Wu is a scholar working on Artificial Intelligence, Atomic and Molecular Physics, and Optics and Statistical and Nonlinear Physics. According to data from OpenAlex, Xingyao Wu has authored 11 papers receiving a total of 316 indexed citations (citations by other indexed papers that have themselves been cited), including 10 papers in Artificial Intelligence, 9 papers in Atomic and Molecular Physics, and Optics and 2 papers in Statistical and Nonlinear Physics. Recurrent topics in Xingyao Wu's work include Quantum Information and Cryptography (8 papers), Quantum Computing Algorithms and Architecture (7 papers) and Quantum Mechanics and Applications (6 papers). Xingyao Wu is often cited by papers focused on Quantum Information and Cryptography (8 papers), Quantum Computing Algorithms and Architecture (7 papers) and Quantum Mechanics and Applications (6 papers). Xingyao Wu collaborates with scholars based in Singapore, France and United States. Xingyao Wu's co-authors include Valerio Scarani, Jean-Daniel Bancal, Yu Cai, Justyna P. Zwolak, Jacob M. Taylor, Matthew McKague, Koon Tong Goh, Tamás Vértesi, Jędrzej Kaniewski and Yeong-Cherng Liang and has published in prestigious journals such as PLoS ONE, Physical Review A and New Journal of Physics.

In The Last Decade

Xingyao Wu

11 papers receiving 310 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Xingyao Wu Singapore	8	265	250	41	27	14	11	316
Kai Redeker Germany	4	407 1.5×	390 1.6×	39 1.0×	30 1.1×	7 0.5×	6	464
Kenneth Goodenough United States	8	225 0.8×	245 1.0×	16 0.4×	14 0.5×	27 1.9×	17	289
Koon Tong Goh Singapore	9	340 1.3×	342 1.4×	10 0.2×	29 1.1×	3 0.2×	15	374
Jonathan Allcock China	10	224 0.8×	305 1.2×	15 0.4×	59 2.2×	16 1.1×	20	374
Michael Ditty Canada	8	205 0.8×	202 0.8×	21 0.5×	27 1.0×	10 0.7×	9	301
Tom Manovitz Israel	10	391 1.5×	350 1.4×	53 1.3×	20 0.7×	15 1.1×	14	536
Pan‐Yu Hou China	11	318 1.2×	277 1.1×	34 0.8×	38 1.4×	22 1.6×	30	370
M. Ardehali Japan	5	661 2.5×	660 2.6×	48 1.2×	36 1.3×	5 0.4×	20	725
Christa Flühmann Switzerland	7	246 0.9×	262 1.0×	36 0.9×	12 0.4×	9 0.6×	8	307
Alireza Seif United States	12	313 1.2×	348 1.4×	31 0.8×	43 1.6×	10 0.7×	28	467

Countries citing papers authored by Xingyao Wu

Since Specialization

Citations

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

Fields of papers citing papers by Xingyao Wu

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Xingyao Wu

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

All Works

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11 of 11 papers shown

1.

Du, Yuxuan, Qian Yang, Xingyao Wu, & Dacheng Tao. (2022). A Distributed Learning Scheme for Variational Quantum Algorithms. IEEE Transactions on Quantum Engineering. 3. 1–16. 13 indexed citations

2.

Zwolak, Justyna P., et al.. (2019). Machine learning techniques for state recognition and auto-tuning in quantum dots. npj Quantum Information. 5(1). 61 indexed citations

3.

Goh, Koon Tong, Jędrzej Kaniewski, Elie Wolfe, et al.. (2018). Geometry of the set of quantum correlations. Physical review. A. 97(2). 72 indexed citations

4.

Zwolak, Justyna P., et al.. (2018). QFlow lite dataset: A machine-learning approach to the charge states in quantum dot experiments. PLoS ONE. 13(10). e0205844–e0205844. 21 indexed citations

5.

Dunjko, Vedran, Yi-Kai Liu, Xingyao Wu, & Jacob M. Taylor. (2017). Super-polynomial and exponential improvements for quantum-enhanced reinforcement learning. 2 indexed citations

6.

Wu, Xingyao, et al.. (2016). All the self-testings of the singlet for two binary measurements. New Journal of Physics. 18(2). 25021–25021. 47 indexed citations

7.

Goh, Koon Tong, et al.. (2016). Nonlocal games and optimal steering at the boundary of the quantum set. Physical review. A. 94(2). 8 indexed citations

8.

Wu, Xingyao, Jean-Daniel Bancal, Matthew McKague, & Valerio Scarani. (2016). Device-independent parallel self-testing of two singlets. Physical review. A. 93(6). 40 indexed citations

9.

Cai, Yu, et al.. (2014). Maximal tree size of few-qubit states. Physical Review A. 89(6). 1 indexed citations

10.

Wu, Xingyao, et al.. (2014). Robust self-testing of the three-qubitWstate. Physical Review A. 90(4). 50 indexed citations

11.

Cai, Yu, et al.. (2013). Tree-size complexity of multiqubit states. Physical Review A. 88(1). 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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