万博max官网手机版 - welcome首页

 

 冉仕举         副教授

	所属学科	理论物理、量子计算、人工智能
	研究方向	张量网络理论与算法、强关联数值计算、量子多体模拟、机器学习解决量子物理问题、基于机器学习的量子编程、量子机器学习、多线性代数
	招生方向	理论物理、量子机器学习
	联系方式	sjran@cnu.edu.cn

 

 

万博max官网手机版副教授，主要研究量子多体物理、张量网络理论与方法、量子信息与量子计算、量子机器学习；主讲本科生专业课“电动力学”；在PRL、PRB等发表论文48篇，其中2018年至今发表第一或通信作者论文31篇，提出的树状幺正张量网络机器学习相关论文获国际高被引奖；受邀于Science子刊Intelligent Computing与Nature子刊Nat. Mach. Intell.发表量子机器学习方面的综述与观点论文；以第一作者于Springer出版社出版英文专著《Tensor Network Contractions》，系统介绍了张量网络这一量子多体物理方法并总结了作者的相关研究成果，该书获《zbMath（数学文摘）》与《Mathematical Reviews（数学评论）》正面评述；出版中文独著《张量网络》，该书为全国首部张量网络、量子物理与机器学习交叉方向的著作，获批2022年度国家出版基金；获批国家发明专利一项；担任国家自然科学基金委青年项目负责人、重点项目主要参与人，北京市自然科学基金委面上项目、教委一般项目负责人；PRL、Nat. Mach. Intell.等期刊审稿人，多次在国际学术会议作邀请报告；曾任Q1\Q2区SCI期刊客座编辑、机器学习国际会议委员会成员、欧盟学术委员会项目评审人；获北京市海外人才计划、北京市优秀指导教师。

 

 

2006年9月-2010年7月，北京师范大学物理学系，本科；

2010年9月-2015年7月，中国科学院大学物理科学学院，博士（导师：苏刚教授）；

2014年7月，德国慕尼黑大学，访问博士生；

2015年7月-2018年7月，西班牙光子科学研究所（ICFO），博士后研究员（合作导师：Maciej Lewenstein教授）；

2016年，获Fundacio-Catalunya独立博士后研究员fellowship；

2017年9月，德国马克思-普朗克量子光学研究所，访问学者；

2018年6月，德国美因茨大学，访问学者；

2018年7月-11月，中国科学院大学，访问学者；

2018年11月-今，万博max官网手机版，副教授

 

 

Shi-Ju Ran*, Emanuele Tirrito, Cheng Peng, Xi Chen, Gang Su, and Maciej Lewenstein, “Tensor Network Contractions”, Lecture Notes in Physics, Springer, Cham (2020).

ISSN: 0075-8450; ISBN: 978-3-030-34488-7

DOI: https://doi.org/10.1007/978-3-030-34489-4

该专著系统性地介绍了张量网络方法，总结了作者在该领域的一系列创新成果，据出版社官方数据，从2020年2月出版至今全球下载量达9万余次。

冉仕举，《张量网络》，首都师范大学出版社，北京(2022)

ISBN:978-7-5656-7150-0

本书为首部张量网络方面的中文专著，针对张量网络在量子物理、应用数学、计算机科学等多个领域的高交叉性，从张量的基本定义出发，循序渐进地介绍了量子物理与统计基础、张量网络基础，到最新的量子物理方法与张量网络机器学习方法，旨在为物理专业的本科生、研究生、学者，以及在非量子物理领域的学者与读者提供一个系统了解张量网络的途径。该书获批2022年国家出版基金。

 

 

基于张量网络的量子多体数值计算方法；

“Less can be as different as more”的量子纠缠模拟方法；

高效、可解释张量网络机器学习方法；

AI for physics中的逆问题与泛化能力；

高量子比特数的量子计算线路设计与实现。

 

 

1、 Ding-Zu Wang, Guo-Feng Zhang*, Maciej Lewenstein*, and Shi-Ju Ran*, Boundary-induced singularity in strongly-correlated quantum systems at finite temperature, Quantum Sci. Technol. 9, 015008 (2024).

2、 Shi-Ju Ran* and Gang Su*, Tensor network for interpretable and efficient quantum-inspired machine learning (accepted by Intelligent Computing, 2023).

3、 Ying Lu and Shi-Ju Ran*, Many-body control with reinforcement learning and tensor networks, Nat. Mach. Intell., News&Views (4 October 2023).

4、 Peng-Fei Zhou, Ying Lu, Jia-Hao Wang, and Shi-Ju Ran*, Tensor Network Efficiently Representing Schmidt Decomposition of Quantum Many-Body States, Phys. Rev. Lett. 131, 020403 (13 July 2023).

5、 Da-Zhi Fang, Ning Xi, Shi-Ju Ran*, and Gang Su*, Nature of the 1/9-magnetization plateau in the spin-1/2 kagome Heisenberg antiferromagnet, Phys. Rev. B 107, L220401 (13 June 2023).

6、 Ye-Ming Meng, Jing Zhang, Peng Zhang, Chao Gao*, and Shi-Ju Ran*, Residual matrix product state for machine learning, SciPost Phys. 14, 142 (02 June 2023).

7、 Ying Lu, Peng-Fei Zhou, Shao-Ming Fei*, and Shi-Ju Ran*, Quantum compiling with a variational instruction set for accurate and fast quantum computing, Phys. Rev. Research 5, 023096 (12 May 2023).

8、 Shi-Ju Ran*, Qibin Zhao, Peng Zhang, and Chu Guo, Editorial: Tensor network approaches for quantum many-body physics and machine learning, Frontiers in Physics 11, 1170492 (27 February 2023).

9、 Sheng-Chen Bai, Yi-Cheng Tang, and Shi-Ju Ran*, Unsupervised Recognition of Informative Features via Tensor Network Machine Learning and Quantum Entanglement Variations, Chinese Phys. Lett. 39, 100701 (15 September 2022).

10、 Ding-Zu Wang, Guo-Feng Zhang*, Maciej Lewenstein*, and Shi-Ju Ran*, Efficient simulation of quantum many-body thermodynamics by tailoring a zero-temperature tensor network, Phys. Rev. B 105, 155155 (27 April 2022).

11、 Rui Hong, Ya-Xuan Xiao, Jie Hu, An-Chun Ji, and Shi-Ju Ran*, Functional tensor network solving many-body Schrödinger equation, Phys. Rev. B 105, 165116 (12 April 2022).

12、 Wei-Ming Li and Shi-Ju Ran*, Non-Parametric Semi-Supervised Learning in Many-Body Hilbert Space with Rescaled Logarithmic Fidelity, Mathematics 10, 940 (15 March 2022).

13、 Monika Aidelsburger, Luca Barbiero, Alejandro Bermudez, Titas Chanda, Alexandre Dauphin, Daniel González-Cuadra, Przemysław R. Grzybowski, Simon Hands, Fred Jendrzejewski, Johannes Jünemann, Gediminas Juzeliunas, Valentin Kasper, Angelo Piga, Shi-Ju Ran, Matteo Rizzi, Germán Sierra, Luca Tagliacozzo, Emanuele Tirrito, Torsten V. Zache, Jakub Zakrzewski, Erez Zohar, and Maciej Lewenstein*, Cold atoms meet lattice gauge theory, Phil. Trans. R. Soc. A 380, 20210064 (20 December 2021).

14、 Ying Lu, Yue-Min Li, Peng-Fei Zhou, and Shi-Ju Ran*, Preparation of Many-body Ground States by Time Evolution with Variational Microscopic Magnetic Fields and Incomplete Interactions, Phys. Rev. A 104, 052413 (11 November 2021).

15、 Kunkun Wang, Lei Xiao, Wei Yi*, Shi-Ju Ran*, and Peng Xue*, Experimental realization of a quantum image classifier via tensor-network-based machine learning, Photon. Res. 9, 2332-2340 (8 November 2021) (Editor’s pick).

16、 Peng-Fei Zhou, Rui Hong, and Shi-Ju Ran*, Automatically differentiable quantum circuit for many-qubit state preparation, Phys. Rev. A 104, 042601 (1 October 2021).

17、 Xinran Ma, Z. C. Tu, and Shi-Ju Ran*, Deep Learning Quantum States for Hamiltonian Estimation, Chin. Phys. Lett. (Express Letter) 38 (11), 110301 (11 October 2021) (封面文章).

18、 Rui Hong, Peng-Fei Zhou, Bin Xi, Jie Hu, An-Chun Ji, and Shi-Ju Ran*, Predicting quantum potentials by deep neural network and metropolis sampling, SciPost Physics Core 4, 022 (13 September 2021).

19、 Yuhan Liu, Wen-Jun Li, Xiao Zhang, Maciej Lewenstein, Gang Su*, and Shi-Ju Ran*, Entanglement-Based Feature Extraction by Tensor Network Machine Learning, Front. Appl. Math. Stat. 7, 716044 (06 August 2021).

20、 Yuan Yang, Zheng-Zhi Sun, Shi-Ju Ran*, and Gang Su*, Visualizing quantum phases and identifying quantum phase transitions by nonlinear dimensional reduction, Phys. Rev. B 103, 075106 (2 February 2021).

21、 Yuan Yang, Zhengchuan Wang*, Shi-Ju Ran*, and Gang Su*, Phase identification in many-body systems by virtual configuration binarization, Phys. Rev. E 103, 013313 (22 January 2021).

22、 Shi-Ju Ran*, Zheng-Zhi Sun, Shao-Ming Fei, Gang Su, and Maciej Lewenstein, Tensor network compressed sensing with unsupervised machine learning, Phys. Rev. Research 2, 033293 (24 August 2020).

23、 Zheng-Zhi Sun, Shi-Ju Ran*, and Gang Su*, Tangent-Space Gradient Optimization of Tensor Network for Machine Learning, Phys. Rev. E 102, 012152 (30 July 2020).

24、 Shi-Ju Ran, Encoding of matrix product states into quantum circuits of one- and two-qubit gates, Phys. Rev. A 101, 032310 (9 March, 2020).

25、 Zheng-Zhi Sun, Cheng Peng, Ding Liu, Shi-Ju Ran*, and Gang Su*, Generative Tensor Network Classification Model for Supervised Machine Learning, Phys. Rev. B 101, 075135 (25 February 2020).

26、 Yuan Yang, Shi-Ju Ran*, Xi Chen, Zheng-zhi Sun, Shou-Shu Gong, Zhengchuan Wang*, and Gang Su*, Reentrance of Topological Phase in Spin-1 Frustrated Heisenberg Chain, Phys. Rev. B 101, 045133, (29 January 2020).

27、 Shi-Ju Ran*, Bin Xi, Cheng Peng, Gang Su, and Maciej Lewenstein, Efficient quantum simulation for thermodynamics of infinite-size many-body systems in arbitrary dimensions, Phys. Rev. B 99, 205132, (20 May 2019).

28、 Ding Liu, Shi-Ju Ran*, Peter Wittek*, Cheng Peng, Rual Blázquez Garca, Gang Su, and Maciej Lewenstein, Machine Learning by Unitary Tensor Network of Hierarchical Tree Structure, New J. Phys. 21, 073059, (30 July 2019).

29、 Xi Chen, Shi-Ju Ran*, Shuo Yang, Maciej Lewenstein, Gang Su*, Noise-tolerant Signature of ZN Topological Orders in Quantum Many-body States, Phys. Rev. B 99, 195101 (1 May 2019).

30、 Shi-Ju Ran*, Cheng Peng, Gang Su, and Maciej Lewenstein, Controlling the phase diagram of finite spin-1/2 chains by tuning the boundary interactions, Phys. Rev. B 98, 085111 (7 August 2018).

31、 Shi-Ju Ran, Wei Li, Shou-Shu Gong, Andreas Weichselbaum, Jan von Delft, and Gang Su*, Emergent spin-1 trimerized valence bond crystal in the spin-1/2 Heisenberg model on the star lattice, Phys. Rev. B 97, 075146 (26 February 2018).

32、 Cheng Peng, Shi-Ju Ran*, Maciej Lewenstein, and Gang Su*, Exotic entanglement scaling of Heisenberg antiferromagnet on honeycomb lattice, Eur. Phys. J. B 91, 258 (22 October 2018).

33、 Xi Chen, Shi-Ju Ran, Tao Liu, Cheng Peng, Yi-Zhen Huang, and Gang Su*, Thermodynamics of spin-1/2 Kagomé Heisenberg antiferromagnet: algebraic paramagnetic liquid and finite-temperature phase diagram, Sci. Bull. 63, 1545–1550 (22 November 2018).

34、 Shi-Ju Ran*, Angelo Piga, Cheng Peng, Gang Su, and Maciej Lewenstein, Few-body systems capture many-body physics: Tensor network approach, Phys. Rev. B 96, 155120 (13 October 2017).

35、 Shi-Ju Ran, Cheng Peng, Wei Li, Maciej Lewenstein, and Gang Su*, Criticality in two-dimensional quantum systems: Tensor network approach, Phys. Rev. B 95, 155114 (10 April 2017).

36、 J. Jünemann, A. Piga, Shi-Ju Ran, M. Lewenstein, M. Rizzi, and A. Bermudez*, Exploring Interacting Topological Insulators with Ultracold Atoms: the Synthetic Creutz-Hubbard Model, Phys. Rev. X 7, 031057 (27 September 2017).

37、 Cheng Peng, Shi-Ju Ran, Tao Liu, Xi Chen, and Gang Su*, Fermionic algebraic quantum spin liquid in an octa-kagome frustrated antiferromagnet, Phys. Rev. B 95, 075140 (22 February 2017).

38、 Emanuele Tirrito, Shi-Ju Ran, Andrew J Ferris, Ian P McCulloch, and Maciej Lewenstein*, Efficient perturbation theory to improve the density matrix renormalization group, Phys. Rev. B 95, 064110 (21 February 2017).

39、 Shi-Ju Ran, Ab-initio optimization principle for the ground states of translationally invariant strongly correlated quantum lattice models, Phys. Rev. E 93, 053310 (27 May 2016).

40、 Meng Wang, Shi-Ju Ran, Tao Liu, Yang Zhao, Qing-Rong Zheng, and Gang Su*, Phase diagram and exotic spin-spin correlations of anisotropic Ising model on the Sierpiński gasket, Eur. Phys. J. B 89, 1-10 (1 February 2016).

41、 Tao Liu, Shi-Ju Ran, Wei Li, Xin Yan, Yang Zhao, and Gang Su*, Featureless quantum spin liquid, 1/3-magnetization plateau state, and exotic thermodynamic properties of the spin-1/2 frustrated Heisenberg antiferromagnet on an infinite Husimi lattice, Phys. Rev. B 89, 054426 (24 February 2014).

42、 Shi-Ju Ran, Bin Xi, Tao Liu, and Gang Su*, Theory of network contractor dynamics for exploring thermodynamic properties of two-dimensional quantum lattice models, Phys. Rev. B 88, 064407 (12 August 2013).

43、 Yang Zhao, Wei Li, Bin Xi, Zhe Zhang, Xin Yan, Shi-Ju Ran, Tao Liu, and Gang Su*, Kosterlitz-Thouless phase transition and re-entrance in an anisotropic three-state Potts model on the generalized kagome lattice, Phys. Rev. E 87, 032151 (22 March 2013).

44、 Yang Zhao, Wei Li, Bin Xi, Shi-Ju Ran, Yuan-Yuan Zhu, Bing-Wu Wang, Song Gao, and Gang Su*, Honeycomb Heisenberg spin ladder: Unusual ground state and thermodynamic properties, Eur. Phys. Lett. 104, 57009 (23 December 2013).

45、 Shi-Ju Ran, Wei Li, Bin Xi, Zhe Zhang, and Gang Su*, Optimized decimation of tensor networks with super-orthogonalization for two-dimensional quantum lattice models, Phys. Rev. B 86, 134429 (26 October 2012).

46、 Xin Yan, Wei Li, Yang Zhao, Shi-Ju Ran, and Gang Su*, Phase diagrams, distinct conformal anomalies, and thermodynamics of spin-1 bond-alternating Heisenberg antiferromagnetic chain in magnetic fields, Phys. Rev. B 85, 134425 (13 April 2012).

47、 Wei Li, Shi-Ju Ran, Shou-Shu Gong, Yang Zhao, Bin Xi, Fei Ye, and Gang Su*, Linearized tensor renormalization group algorithm for the calculation of thermodynamic properties of quantum lattice models, Phys. Rev. Lett. 106, 127202 (22 March 2011).

48、 Wei Li, Shou-Shu Gong, Yang Zhao, Shi-Ju Ran, Song Gao, and Gang Su*, Phase transitions and thermodynamics of the two-dimensional Ising model on a distorted kagome lattice, Phys. Rev. B 82, 134434 (21 October 2010).

 

 

国家发明专利：神经网络的压缩方法、装置、电子设备及存储介质，发明人：冉仕举，卿勇，李珂，周鹏飞（2023年4月26日）

 

 

1、 中文报告：冉仕举，Tensor network for efficient and interpretable machine learning，物理学秋季年会，宁夏大学，银川（2023）

2、 中文报告：冉仕举，张量网络收缩, 暑期多体计算讲习班，闽江学院，福州（2023）

3、 中文报告：冉仕举，Tensor network for efficient and interpretable machine learning，量子智能和量子材料研讨会，鲁东大学，烟台（2023）

4、 英文报告：Shi-Ju Ran, Tensor network for efficient quantum computation and many-body simulation，Chengdu-Chongqing Quantum Workshop - Quantum Sensing: Theory to Applications，ChengDu，China（2023）

5、 中文报告：冉仕举，Efficient simulation of frustrated quantum spin systems by Schmidt tensor network state ansatz，第十七届全国磁学理论会议，河北师范大学，河北（2023）

6、 英文报告：Shi-Ju Ran, Efficient Quantum Computing by Optimal Control of Quantum Many-body Dynamics, The 11th workshop on Quantum Many-Body Computation, Fuzhou, Fujian (2023).

7、 中文报告：冉仕举, Functional Tensor Network Solving Many-body Schrödinger Equation,中国机器学习与应用科学大会, 北京 (2022).

8、 英文报告：Shi-Ju Ran, Functional Tensor Network Solving Many-body Schrödinger Equation, The 10th Workshop on Quantum Many-Body Computation, Jiangsu (2022).

9、 中文报告：冉仕举, 经典、量子，各司其职?, CCF量子计算精英大会, 北京 (2022).

10、 英文报告：Shi-Ju Ran, Deep Learning Quantum States For Hamiltonian Predictions, Workshop II: Tensor Network States and Applications (online), IPAM, USA (2021).

11、 会议论文：Wen-Jun Li, Zheng-Zhi Sun, Ya-Ru Wang, Shi-Ju Ran*, and Gang Su*, Matrix product state for quantum-inspired feature extraction and compressed sensing, Second Workshop on Quantum Tensor Networks in Machine Learning, 35th Conference on Neural Information Processing Systems (2021).

12、 英文报告：Shi-Ju Ran, Simulating Quantum Many-body Systems by Few-body Models at Zero and Finite Temperatures, Workshop on Tensor Networks in Many Body and Lattice Field, TDLI, SJTU, Shanghai (2021).

13、 中文报告：冉仕举, Deep Learning Quantum States For Hamiltonian Predictions第十六届全国磁学会议, 扬州大学 (2021).

14、 中文报告：冉仕举, 量子多体物理中的机器学习新方法, 凝聚态理论前沿暑期讲习班, 河北师范大学 (2021).

15、 中文报告：冉仕举, 量子多体物理中的机器学习新方法, YOCSEF论坛, 天津 (2021).

16、 中文报告：冉仕举, 量子物理与机器学习交融下的新方法, 第三届学术交流月, 北京航空航天大学 (2021).

17、 会议论文：Zheng-Zhi Sun, Shi-Ju Ran*, and Gang Su*, Tangent-space gradient optimization: an efficient update scheme for tensor network machine learning and beyond, First Workshop on Quantum Tensor Networks in Machine Learning, 34th Conference on Neural Information Processing Systems (2020).

18、 会议论文：Ding Liu, Zheng-Zhi Sun, Cheng Peng, Gang Su, and Shi-Ju Ran*, Generative Tensor Network Classification for Supervised Learning, International Workshop on Tensor Network Representations in Machine Learning, the 29th International Joint Conference on Artificial Intelligence (2020).

19、 中文报告：冉仕举, Bayesian tensor network: towards efficient and interpretable probabilistic machine learning，第一届量子物理与智能计算研讨会(online), 天津-北京 (2020).

 

 

已指导毕业三名硕士研究生；

卢迎获2022年度北京市级普通高校优秀本科毕业设计(论文)、2022年首都师范大学“校长奖学金”一等奖、2023年国家奖学金；

白生辰获2023年北京市普通高等学校优秀毕业生；

卿勇等获2022年、2023年本源量子“司南杯”量子计算大赛优胜奖；

周鹏飞获2021年国家奖学金。