Shi-Ju Ran

1.3k total citations
48 papers, 772 citations indexed

About

Shi-Ju Ran is a scholar working on Atomic and Molecular Physics, and Optics, Artificial Intelligence and Condensed Matter Physics. According to data from OpenAlex, Shi-Ju Ran has authored 48 papers receiving a total of 772 indexed citations (citations by other indexed papers that have themselves been cited), including 40 papers in Atomic and Molecular Physics, and Optics, 21 papers in Artificial Intelligence and 17 papers in Condensed Matter Physics. Recurrent topics in Shi-Ju Ran's work include Quantum many-body systems (37 papers), Quantum Computing Algorithms and Architecture (14 papers) and Physics of Superconductivity and Magnetism (13 papers). Shi-Ju Ran is often cited by papers focused on Quantum many-body systems (37 papers), Quantum Computing Algorithms and Architecture (14 papers) and Physics of Superconductivity and Magnetism (13 papers). Shi-Ju Ran collaborates with scholars based in China, Spain and Germany. Shi-Ju Ran's co-authors include Gang Su, Maciej Lewenstein, Bin Xi, Peng Cheng, Wei Li, Emanuele Tirrito, Matteo Rizzi, A. Bermúdez, Luca Tagliacozzo and Shou-Shu Gong and has published in prestigious journals such as Physical Review Letters, SHILAP Revista de lepidopterología and Physical Review B.

In The Last Decade

Shi-Ju Ran

45 papers receiving 753 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Shi-Ju Ran China	15	581	301	282	90	82	48	772
Piotr Czarnik Poland	17	625 1.1×	373 1.2×	355 1.3×	77 0.9×	31 0.4×	24	826
Michael Lubasch Germany	16	597 1.0×	546 1.8×	138 0.5×	153 1.7×	83 1.0×	24	890
M. M. Wolf Germany	6	895 1.5×	488 1.6×	274 1.0×	95 1.1×	52 0.6×	7	975
Johnnie Gray United States	9	406 0.7×	365 1.2×	55 0.2×	78 0.9×	45 0.5×	16	550
Martin Ganahl Austria	16	588 1.0×	129 0.4×	272 1.0×	94 1.0×	12 0.1×	26	648
Jin-Guo Liu China	8	285 0.5×	338 1.1×	97 0.3×	51 0.6×	47 0.6×	14	530
Eugene Dumitrescu United States	15	769 1.3×	674 2.2×	198 0.7×	39 0.4×	12 0.1×	38	1.1k
Dries Sels United States	19	1.0k 1.8×	535 1.8×	199 0.7×	382 4.2×	25 0.3×	61	1.2k
Iztok Pižorn Slovenia	12	1.0k 1.7×	295 1.0×	359 1.3×	291 3.2×	71 0.9×	14	1.1k
Pietro Silvi Italy	14	842 1.4×	328 1.1×	245 0.9×	188 2.1×	29 0.4×	32	936

Countries citing papers authored by Shi-Ju Ran

Since Specialization

Citations

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

Fields of papers citing papers by Shi-Ju Ran

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Shi-Ju Ran. 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 Shi-Ju Ran. The network helps show where Shi-Ju Ran may publish in the future.

Co-authorship network of co-authors of Shi-Ju Ran

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

All Works

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20 of 20 papers shown

Li, Ke, et al.. (2025). Compressing Neural Networks Using Tensor Networks with Exponentially Fewer Variational Parameters. SHILAP Revista de lepidopterología. 4.

Ran, Shi-Ju, et al.. (2024). Universal Scaling Laws in Quantum-Probabilistic Machine Learning by Tensor Network: Toward Interpreting Representation and Generalization Powers. Chinese Physics Letters. 41(12). 120701–120701.

Lewenstein, Maciej, et al.. (2024). Eigenstate thermalization and its breakdown in quantum spin chains with inhomogeneous interactions. Physical review. B.. 109(4). 4 indexed citations

Zhang, Guo‐Feng, et al.. (2023). Boundary-induced singularity in strongly-correlated quantum systems at finite temperature. Quantum Science and Technology. 9(1). 15008–15008. 1 indexed citations

Ran, Shi-Ju, et al.. (2023). Nature of the 1/9-magnetization plateau in the spin-12 kagome Heisenberg antiferromagnet. Physical review. B.. 107(22). 11 indexed citations

Ran, Shi-Ju, et al.. (2023). Quantum compiling with a variational instruction set for accurate and fast quantum computing. Physical Review Research. 5(2). 2 indexed citations

Lü, Ying & Shi-Ju Ran. (2023). Many-body control with reinforcement learning and tensor networks. Nature Machine Intelligence. 5(10). 1058–1059. 2 indexed citations

Ran, Shi-Ju, et al.. (2023). Tensor Network Efficiently Representing Schmidt Decomposition of Quantum Many-Body States. Physical Review Letters. 131(2). 20403–20403.

Xi, Bin, et al.. (2021). Predicting Quantum Potentials by Deep Neural Network and Metropolis Sampling. SHILAP Revista de lepidopterología. 3 indexed citations

10.

Ran, Shi-Ju, et al.. (2021). Preparation of Many-body Ground States by Time Evolution with Variational Microscopic Magnetic Fields and Incomplete Interactions. arXiv (Cornell University). 3 indexed citations

11.

Li, Wenjun, et al.. (2021). Entanglement-Based Feature Extraction by Tensor Network Machine Learning. Frontiers in Applied Mathematics and Statistics. 7. 20 indexed citations

12.

Ran, Shi-Ju. (2020). Encoding of matrix product states into quantum circuits of one- and two-qubit gates. Physical review. A. 101(3). 73 indexed citations

13.

Ran, Shi-Ju, et al.. (2020). Tangent-space gradient optimization of tensor network for machine learning. Physical review. E. 102(1). 12152–12152. 7 indexed citations

14.

Liu, Yuhan, Xiao Zhang, Maciej Lewenstein, & Shi-Ju Ran. (2018). Learning architectures based on quantum entanglement: a simple matrix product state algorithm for image recognition. arXiv (Cornell University). 3 indexed citations

15.

Liu, Ding, et al.. (2017). Machine Learning by Two-Dimensional Hierarchical Tensor Networks: A Quantum Information Theoretic Perspective on Deep Architectures. arXiv (Cornell University). 7 indexed citations

16.

Ran, Shi-Ju, et al.. (2017). Few-body systems capture many-body physics: Tensor network approach. Physical review. B.. 96(15). 13 indexed citations

17.

Ran, Shi-Ju. (2016). Ab initiooptimization principle for the ground states of translationally invariant strongly correlated quantum lattice models. Physical review. E. 93(5). 53310–53310. 9 indexed citations

18.

Ran, Shi-Ju, Bin Xi, Tao Liu, & Gang Su. (2013). Theory of network contractor dynamics for exploring thermodynamic properties of two-dimensional quantum lattice models. Physical Review B. 88(6). 31 indexed citations

19.

Xin, Yan, Wei Li, Yang Zhao, Shi-Ju Ran, & Gang Su. (2012). Phase diagrams, distinct conformal anomalies, and thermodynamics of spin-1 bond-alternating Heisenberg antiferromagnetic chain in magnetic fields. Physical Review B. 85(13). 21 indexed citations

20.

Li, Wei, Shi-Ju Ran, Shou-Shu Gong, et al.. (2011). Linearized Tensor Renormalization Group Algorithm for the Calculation of Thermodynamic Properties of Quantum Lattice Models. Physical Review Letters. 106(12). 127202–127202. 71 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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