Identification of non-Fermi liquid fermionic self-energy from quantum Monte Carlo data

Xiao Yan Xu, Avraham Klein, Kai Sun, Andrey V. Chubukov, Zi Yang Meng

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Quantum Monte Carlo (QMC) simulations of correlated electron systems provide unbiased information about system behavior at a quantum critical point (QCP) and can verify or disprove the existing theories of non-Fermi liquid (NFL) behavior at a QCP. However, simulations are carried out at a finite temperature, where quantum critical features are masked by finite-temperature effects. Here, we present a theoretical framework within which it is possible to separate thermal and quantum effects and extract the information about NFL physics at T = 0. We demonstrate our method for a specific example of 2D fermions near an Ising ferromagnetic QCP. We show that one can extract from QMC data the zero-temperature form of fermionic self-energy Σ(ω) even though the leading contribution to the self-energy comes from thermal effects. We find that the frequency dependence of Σ(ω) agrees well with the analytic form obtained within the Eliashberg theory of dynamical quantum criticality, and obeys ω2/3 scaling at low frequencies. Our results open up an avenue for QMC studies of quantum critical metals.

Original languageEnglish (US)
Article number65
Journalnpj Quantum Materials
Issue number1
StatePublished - Dec 1 2020

Bibliographical note

Funding Information:
We thank Subir Sachdev, Max Metlitski, Yuxuan Wang, Yoni Schattner, Erez Berg, and Dmitrii Maslov for insightful discussions on fermionic QCPs and NFL. X.Y.X. also thank Tarun Grover for helpful discussion on related projects. We acknowledge the support from RGC of Hong Kong SAR China through 17303019 and 17301420, MOST through the National Key Research and Development Program (2016YFA0300502). The work by A.K. and A.V.C. was supported by the Office of Basic Energy Sciences, U.S. Department of Energy, under award DE-SC0014402. We also thank the Center for Quantum Simulation Sciences in the Institute of Physics, Chinese Academy of Sciences, the Computational Initiative at the Faculty of Science 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. This research was initiated at the Aspen Center for Physics, supported by NSF PHY-1066293.

Publisher Copyright:
© 2020, The Author(s).

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