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

 

 

　　首都师范大学物理系副教授，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”；发表学术论文39篇；于Springer出版社出版英文专著《Tensor Network Contractions》；担任Q1\Q2区SCI期刊客座编辑，作为会议主席组织第一届量子物理与智能计算研讨会，担任机器学习国际会议委员会成员，多次在国际学术会议作邀请报告；担任国家自然基金委青年项目负责人、重点项目主要参与人、北京市自然基金委面上项目负责人，曾任欧盟学术委员会项目评审，2022年获由英国物理学会出版社颁发的IOP Publishing Top Cited Paper Award。

 

 

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，首都师范大学物理系，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月出版至今下载量达7万余次。

冉仕举，《张量网络》，首都师范大学出版社，北京（预计于2022年9月正式出版）

本书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。

　　　　 

　　 

基于张量网络的量子多体数值计算方法：

针对强关联量子系统数值模拟的“指数级”复杂度，以及量子蒙特卡洛方法在阻挫磁体上面临的“负符号”问题，提出了张量网络超正交化方法[PRB 2012]、网络收缩子动力学方法[PRB 2013]、张量网络自洽编码方法[PRE 2016]、张量网络tailoring方法[PRB 2022]等多个量子多体精确数值模拟方法，成功预测了包括准一维与二维阻挫磁体在内的多种强关联模型及材料的新奇量子物性[PRB 2014/2017/2018/2020, Sci. Bull. 2018]。

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

量子效应的可控实现是发展量子技术的关键任务，而量子层展效应对系统尺寸的高要求（即“more is different”）是将其应用于发展量子技术的主要挑战，针对这一问题，提出量子纠缠模拟方法[PRB 2017/2019]，通过优化小尺寸体系的边界哈密顿量，使其体态呈现无穷大体系的零温或热力学量子性质，包括磁化平台、准粒子激发、量子临界性等，边界哈密顿量也被用于实现体态性质的非平庸调控[PRB 2018, arXiv 2022]。

高效、可解释张量网络机器学习方法：

神经网络等主流机器学习模型的不可解释性会导致低效的“试错性”研究范式、预测的不可控性、高数据依耐性等瓶颈问题。以“高可解释性”与“低数据依耐性”为目标，提出了多层树状张量网络机器学习模型[NJP 2019]、量子概率性分类器[PRB 2019]等新机器学习模型，并结合量子信息论特别是量子纠缠理论，发展了多个开创性的可解释机器学习方法，实现了特征提取[Front. Appl. Math. Stat. 2021]、（量子多体态）压缩感知[PRR 2020]、（希尔伯特空间中的）小样本与半监督学习[Mathematics 2022]、非数据驱动的连续空间多体薛定谔方程求解 [PRB 2022]、非监督图像分割[arXiv 2022]等关键机器学习任务，与实验组合作在光量子计算机实现了张量网络图像分类器[Photon. Res. 2021]。

AI for physics中的逆问题与泛化能力：

AI为物理研究提供了新的范式和挑战，针对物理研究中“数据量低”、“数据分布不平衡”等问题，提出了基于深度神经网络的量子势能[SciPost Phys. Core 2021]、相互作用自旋体系哈密顿量[CPL Express Lett. 2021]与动力学系统拓扑[arXiv 2022]的逆向预测方法，提出了基于数据可视化方法的多体系统量子相识别方法[PRB 2021, PRE 2021]，在上述工作中定量研究了数据的不平衡性对预测结果的影响以及模型的泛化性能。

高量子比特数的量子计算线路设计与实现：

随着量子计算规模的快速增长，高比特数下量子计算的研究与应用严重受制于“指数墙”问题，即随量子比特数增长而指数增长的系统复杂度。针对这一问题，提出了结合张量网络与机器学习的半解析与变分量子线路构建与优化方法，高效实现量子多体态的制备任务[PRA 2020/2021]，提出变分磁场调控优化方法，高效、高保真度地实现量子态制备与量子线路幺正变换[PRA 2021, arXiv 2022].

　　 

　　 

[1] 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).

[2] 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).

[3] 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).

[4] 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).

[5] 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).

[6] 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).

[7] Peng-Fei Zhou, Rui Hong, and Shi-Ju Ran*, Automatically differentiable quantum circuit for many-qubit state preparation, Phys. Rev. A 101, 032310 (1 October 2021).

[8] 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).

[9] 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).

[10] 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).

[11] 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).

[12] 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).

[13] 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).

[14] 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).

[15] 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).

[16] 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).

[17] 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).

[18] 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).

[19] 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).

[20] 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).

[21] 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).

[22] 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).

[23] 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).

[24] 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).

[25] 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).

[26] 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).

[27] 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).

[28] 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).

[29] 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).

[30] 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).

[31] 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).

[32] 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).

　　 

　　 

[1] 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).

[2] 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).

[3] 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).

[4] 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).

[5] 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).

[6] 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).

[7] 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).

[8] 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).

[9] 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).

[10] 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).

[11] 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).

[12] 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).

[13] 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).