Monte Carlo study of the pseudogap and superconductivity emerging from quantum magnetic fluctuations

Weilun Jiang, Yuzhi Liu, Avraham Klein, Yuxuan Wang, Kai Sun, Andrey V. Chubukov, Zi Yang Meng

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14 Scopus citations

Abstract

The origin of the pseudogap behavior, found in many high-Tc superconductors, remains one of the greatest puzzles in condensed matter physics. One possible mechanism is fermionic incoherence, which near a quantum critical point allows pair formation but suppresses superconductivity. Employing quantum Monte Carlo simulations of a model of itinerant fermions coupled to ferromagnetic spin fluctuations, represented by a quantum rotor, we report numerical evidence of pseudogap behavior, emerging from pairing fluctuations in a quantum-critical non-Fermi liquid. Specifically, we observe enhanced pairing fluctuations and a partial gap opening in the fermionic spectrum. However, the system remains non-superconducting until reaching a much lower temperature. In the pseudogap regime the system displays a “gap-filling" rather than “gap-closing" behavior, similar to the one observed in cuprate superconductors. Our results present direct evidence of the pseudogap state, driven by superconducting fluctuations.

Original languageEnglish (US)
Article number2655
JournalNature communications
Volume13
Issue number1
DOIs
StatePublished - Dec 2022
Externally publishedYes

Bibliographical note

Funding Information:
We thank R.M. Fernandes, M.H. Christensen, Y. Schattner, E. Berg, and X. Wang for valuable discussions. W.L.J. thanks Z. Liu for the support of the code, and G. Pan for the helpful suggestions. W.L.J., Y.Z.L. and Z.Y.M. acknowledge support from the RGC of Hong Kong SAR of China (Grant Nos. 17303019, 17301420, 17301721 and AoE/P-701/20), the Strategic Priority Research Program of the Chinese Academy of Sciences (Grant No. XDB33000000), the K. C. Wong Education Foundation (Grant No. GJTD-2020-01) and the Seed Funding “Quantum-Inspired explainable-AI" at the HKU-TCL Joint Research Centre for Artificial Intelligence. We thank the Center for Quantum Simulation Sciences in the Institute of Physics, Chinese Academy of Sciences, the Computational Initiative at the Faculty of Science and the Information Technology Services at the University of Hong Kong and the Tianhe platforms at the National Supercomputer Centers in Tianjin and Guangzhou for their technical support and generous allocation of CPU time. The authors also acknowledge Beijng PARATERA Tech CO.,Ltd.(https://www.paratera.com/) for providing HPC resources that have contributed to the research results reported within this paper. The work by A.V.C. was supported by the Office of Basic Energy Sciences, U.S. Department of Energy, under award DE-SC0014402. A.K. and A.V.C. acknowledge the hospitality of KITP at UCSB, where part of the work has been conducted. The research at KITP is supported by the National Science Foundation under Grant No. NSF PHY-1748958. Y.W. is supported by startup funds at the University of Florida and by NSF under award number DMR-2045871.

Funding Information:
We thank R.M. Fernandes, M.H. Christensen, Y. Schattner, E. Berg, and X. Wang for valuable discussions. W.L.J. thanks Z. Liu for the support of the code, and G. Pan for the helpful suggestions. W.L.J., Y.Z.L. and Z.Y.M. acknowledge support from the RGC of Hong Kong SAR of China (Grant Nos. 17303019, 17301420, 17301721 and AoE/P-701/20), the Strategic Priority Research Program of the Chinese Academy of Sciences (Grant No. XDB33000000), the K. C. Wong Education Foundation (Grant No. GJTD-2020-01) and the Seed Funding “Quantum-Inspired explainable-AI" at the HKU-TCL Joint Research Centre for Artificial Intelligence. We thank the Center for Quantum Simulation Sciences in the Institute of Physics, Chinese Academy of Sciences, the Computational Initiative at the Faculty of Science and the Information Technology Services at the University of Hong Kong and the Tianhe platforms at the National Supercomputer Centers in Tianjin and Guangzhou for their technical support and generous allocation of CPU time. The authors also acknowledge Beijng PARATERA Tech CO.,Ltd.( https://www.paratera.com/ ) for providing HPC resources that have contributed to the research results reported within this paper. The work by A.V.C. was supported by the Office of Basic Energy Sciences, U.S. Department of Energy, under award DE-SC0014402. A.K. and A.V.C. acknowledge the hospitality of KITP at UCSB, where part of the work has been conducted. The research at KITP is supported by the National Science Foundation under Grant No. NSF PHY-1748958. Y.W. is supported by startup funds at the University of Florida and by NSF under award number DMR-2045871.

Publisher Copyright:
© 2022, The Author(s).

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