Postdoctral Researcher
Department of Physics, University of Hong Kong
Email: cxzh@hku.hk
Website: chx-zh.cc
Research Interest
Strongly correlated quantum systems; Tensor network methods; Hubbard model; Artificial intelligence for quantum matter; Gauge/gravity duality
Education
2022 - 2025
Doctor of Science, Ludwig-Maximilian-Universität München
Research project: Tensor Network and AI Study of the Hubbard Model
2019 - 2021
Master of Science, Ludwig-Maximilian-Universität München
Specialized in Theoretical Physics; Grade: 1.5/1.0
2016 - 2018
Bachelor of Science, The University of Manchester
Specialized in Theoretical Physics; First Class Honours
2014 - 2018
Bachelor of Science, Beijing Normal University
Specialized in Physics / Computer Science; Grade: 87/100
Publications
Frustration-Induced Superconductivity in the t-t′ Hubbard Model
C. Zhang, J.-W. Li, D. Nikolaidou, and J. von Delft
Phys. Rev. Lett. 134, 116502 (2025) · arXiv:2307.14835
Finite-Temperature Study of the Hubbard Model via Enhanced Exponential Tensor Renormalization Group
C. Zhang and J. von Delft
arXiv:2510.25022 (2025)
Interpretable Artificial Intelligence Analysis of Strongly Correlated Electrons
C. Zhang and J. von Delft
arXiv:2510.26864 (2025)
Doctoral Thesis
Tensor Network Methods and AI Study of the Hubbard Model
Supervisor: Prof. Jan von Delft
Tensor-network methods including iPEPS and XTRG were used to study ground-state and finite-temperature properties of the Hubbard model. An interpretable AI algorithm was developed to analyse tensor-network data and provide accurate measurements in quantum-gas microscopes.
Teaching Experience
2022, 2025
Teaching Assistant for Tensor Networks
Master’s-level course taught by Prof. Jan von Delft; weekly exercise sessions and student-project support.
2019
Lecture Series on Gauge/Gravity Duality, SUSTech
Four lectures on gauge/gravity duality, holographic entanglement entropy, and holographic superconductors.
Selected Conferences & Workshops
2025 — APS March Meeting & Global Physics Summit, Anaheim, USA
Tensor Network Simulations of the Fermi-Hubbard Model
2025 — Workshop on Quantum Many-Body Computation, Hangzhou, China
Frustration-Induced Superconductivity in the t-t′ Hubbard Model
2023 — Tensor Network States: Algorithms and Applications, Shanghai, China
Tensor Network (2D) Simulations of the Hubbard Model