Exploring nontrivial topology at quantum criticality in a superconducting processor
Abstract
The discovery of nontrivial topology in quantum critical states has introduced a new paradigm for classifying quantum phase transitions and challenges the conventional belief that topological phases are typically associated with a bulk energy gap. However, realizing and characterizing such topologically nontrivial quantum critical states with large particle numbers remains an outstanding experimental challenge in statistical and condensed matter physics. Programmable quantum processors can directly prepare and manipulate exotic quantum many-body states, offering a powerful path for exploring the physics behind these states. Here, we present an experimental exploration of the critical cluster Ising model by preparing its low-lying critical states on a superconducting processor with up to qubits. We develop an efficient method to probe the boundary -function based on prepared low-energy states, which allows us to uniquely identify the nontrivial topology of the critical systems under study. Furthermore, by adapting the entanglement Hamiltonian tomography technique, we recognize two-fold topological degeneracy in the entanglement spectrum under periodic boundary condition, experimentally verifying the universal bulk-boundary correspondence in topological critical systems. Our results demonstrate the low-lying critical states as useful quantum resources for investigating the interplay between topology and quantum criticality.
Cite
@article{arxiv.2501.04679,
title = {Exploring nontrivial topology at quantum criticality in a superconducting processor},
author = {Ziqi Tan and Ke Wang and Sheng Yang and Fanhao Shen and Feitong Jin and Xuhao Zhu and Yujie Ji and Shibo Xu and Jiachen Chen and Yaozu Wu and Chuanyu Zhang and Yu Gao and Ning Wang and Yiren Zou and Aosai Zhang and Tingting Li and Zehang Bao and Zitian Zhu and Jiarun Zhong and Zhengyi Cui and Yihang Han and Yiyang He and Han Wang and Jianan Yang and Yanzhe Wang and Jiayuan Shen and Gongyu Liu and Zixuan Song and Jinfeng Deng and Hang Dong and Pengfei Zhang and Shao-Kai Jian and Hekang Li and Zhen Wang and Qiujiang Guo and Chao Song and Xue-Jia Yu and H. Wang and Hai-Qing Lin and Fei Wu},
journal= {arXiv preprint arXiv:2501.04679},
year = {2026}
}