arXiv subjects

Shaozhi Li

Publications and source records attributed to Shaozhi Li.

At least 19 recordsLinked to original sources

arXiv2026

Emergent $s+id$ Superconductivity from the Interplay between Electronic Correlations and Electron-Phonon Coupling in $\mathrm{R}_{1-x}\mathrm{Sr}_x\mathrm{NiO}_2$

Recent tunneling measurements on infinite-layer nickelates have revealed spatially varying superconducting symmetries, whose microscopic origin remains unclear. Motivated by this observation, we investigate the interplay between electron correlations and electron-phonon interactions in infinite-layer nickelates by combining first-principles calculations with the fluctuation-exchange-Migdal-Eliashberg theory. Our calculations show that spin fluctuations yield robust $d$-wave superconductivity on the Ni $d_{x^2-y^2}$ orbital, whereas electron-phonon coupling induces $s$-wave pairing on an interstitial orbital, leading to an $s+id$ superconducting state. The emergence of the $s$-wave component is strongly carrier-density dependent: an intermediate electron-phonon coupling of $λ=0.4$ stabilizes the $s+id$ state at $n=0.9$ but not at $n=0.8$. These results imply that local oxygen defects tune the local electron density and form finite-size domains with distinct pairing symmetries, offering a compelling explanation for the spatially inhomogeneous superconducting symmetries observed in experiments.

cond-mat.str-el↗

arXiv2026

Quantum-Inspired Weight-Constrained Neural Network: Reducing Variable Numbers by 100x Compared to Standard Neural Networks

Although quantum machine learning has shown great promise, the practical application of quantum computers remains constrained in the noisy intermediate-scale quantum era. To take advantage of quantum machine learning, we investigate the underlying mathematical principles of these quantum models and find that the quantum neural network with amplitude encoding is equivalent to a weight-constrained neural network. Motived by this discovery, we develop a classical weight-constrained neural network. We find that this approach can reduce the number of variables in a classical neural network by a factor of 135 while preserving its accuracy. In addition, we develop a dropout method to enhance the robustness of quantum machine learning models, which are highly susceptible to adversarial attacks. This technique can also be applied to improve the adversarial robustness of the classical weight-constrained neural network, which is essential for industry applications, such as self-driving vehicles. Our work offers a novel approach to reduce the complexity of large classical neural networks, addressing a critical challenge in machine learning.

quant-ph↗

arXiv2025

Finite-Temperature Thermally-Assisted-Occupation Density Functional Theory, Ab Initio Molecular Dynamics, and Quantum Mechanics/Molecular Mechanics Methods

Recently, thermally-assisted-occupation density functional theory (TAO-DFT) [J.-D. Chai, J. Chem. Phys. 136, 154104 (2012)] has been demonstrated to be an efficient and accurate electronic structure method for studying the ground-state properties of large multi-reference (MR) systems at absolute zero. To explore the thermal equilibrium properties of large MR systems at finite electronic temperatures, in the present work, we propose the finite-temperature (FT) extension of TAO-DFT, denoted as FT-TAO-DFT. Besides, to unlock the dynamical information of large MR systems at finite temperatures, FT-TAO-DFT is combined with ab initio molecular dynamics, leading to FT-TAO-AIMD. In addition, we also develop FT-TAO-DFT-based quantum mechanics/molecular mechanics (QM/MM), denoted as FT-TAO-QM/MM, to provide a cost-effective description of the thermal equilibrium properties of a QM subsystem with MR character embedded in an MM environment at finite temperatures. Moreover, the FT-TAO-DFT, FT-TAO-AIMD, and FT-TAO-QM/MM methods are employed to explore the radical nature and infrared (IR) spectra of n-acenes (n = 2--6), consisting of n linearly fused benzene rings, in vacuum and in an argon (Ar) matrix at finite temperatures. According to our calculations, for n-acenes at 1000 K or below, the electronic temperature effects on the radical nature and IR spectra are very minor, while the nuclear temperature effects on these properties are noticeable. For n-acene in an Ar matrx at absolute zero, the Ar matrix has minimal impact on the radical nature of n-acene, while the co-deposition procedure of n-acene and Ar atoms may affect the IR spectrum of n-acene.

physics.chem-ph↗

arXiv2025

Interplay of Kekulé bond order and lattice instability in $\mathrm{C}_6\mathrm{Li}$

Understanding the interplay between charge order and lattice instability in quantum materials remains a central challenge, as their coexistence often obscures causal relationships. This work introduces $\mathrm{C}_6\mathrm{Li}$ as a novel platform to investigate charge order mediated by two distinct mechanisms. We show that the hybridization between carbon $π$ and lithium $s$ orbitals generates an effective long-range hopping within Li-centered hexagons. This hopping drives a Kekulé bond order, whose structure varies with charge density and the sign of the hopping. This bond order induces a Kekulé lattice distortion via electron-phonon coupling. In the limit where lithium atoms are distant from the graphene layer, a Fermi surface nesting-driven Kekulé bond order emerges, stabilized by the electron-phonon interaction. Our results establish $\mathrm{C}_6\mathrm{Li}$ as a tunable platform for elucidating the causal hierarchy between electronic and structural orders in quantum materials.

cond-mat.str-el↗

arXiv2025

Self-learning QMC: application to the classical Holstein-Spin-Fermion model

To evaluate the effectiveness of machine learning in systems with competing interactions, we developed a self-learning quantum Monte Carlo (SLQMC) method to simulate the phase transition in the classical Holstein-spin-fermion model. In SLQMC, machine learning techniques are employed to approximate the free energy, thereby bypassing the need for exact diagonalization and significantly reducing computational cost. We assess the performance of SLQMC using both linear regression and neural network models. Our results show that both models are capable of capturing the phase transition from the antiferromagnetic state to the charge-density-wave state. However, the sampling efficiency decreases near the AFM-CDW phase transition, which is attributed to the increased mean-squared-error of the machine learning model. Additionally, the sampling efficiency decreases with increasing lattice size. This suppression is due to the increased root-mean-squared-error as the machine learning model is applied to a large lattice and the finite-size effect, wherein the energy gap between the ground state and low-energy excited states decreases as the lattice grows. Our findings highlight the necessity of highly accurate machine learning models to simulate theoretical models with complex, competing microscopic interactions on a large lattice.

cond-mat.str-el↗

arXiv2025

Sampling Electronic Fock States using Determinant Quantum Monte Carlo

Analog quantum simulation based on ultracold atoms in optical lattices has catalyzed significant breakthroughs in the study of quantum many-body systems. These simulations rely on the statistical sampling of electronic Fock states, which are not easily accessible in classical algorithms. In this work, we modify the determinant quantum Monte Carlo by integrating a Fock-state update mechanism alongside the auxiliary field. This method enables efficient sampling of Fock-state configurations. The Fock-state restrictive sampling scheme further enables the pre-selection of multiple ensembles at no additional computational cost, thereby broadening the scope of simulation to more general systems and models. Employing this method, we analyze static correlations of the Hubbard model up to the fourth order and achieve quantitative agreement with cold-atom experiments. The simulations of dynamical spectroscopies of the Hubbard and Kondo-lattice models further demonstrate the reliability and advantage of this method.

cond-mat.quant-gas↗

arXiv2024

Quantum-inspired activation functions and quantum Chebyshev-polynomial network

Driven by the significant advantages offered by quantum computing, research in quantum machine learning has increased in recent years. While quantum speed-up has been demonstrated in some applications of quantum machine learning, a comprehensive understanding of its underlying mechanisms for improved performance remains elusive. Our study address this problem by investigating the functional expressibility of quantum circuits integrated within a convolutional neural network (CNN). Through numerical experiments on the MNIST, Fashion MNIST, and Letter datasets, our hybrid quantum-classical CNN model demonstrates superior feature selection capabilities and substantially reduces the required training steps compared to classical CNNs. Notably, we observe similar performance improvements when incorporating three other quantum-inspired activation functions in classical neural networks, indicating the benefits of adopting quantum-inspired activation functions. Additionally, we developed a hybrid quantum Chebyshev-polynomial network (QCPN) based on the properties of quantum activation functions. We demonstrate that a three-layer QCPN can approximate any continuous function, a feat not achievable by a standard three-layer classical neural network. Our findings suggest that quantum-inspired activation functions can reduce model depth while maintaining high learning capability, making them a promising approach for optimizing large-scale machine-learning models. We also outline future research directions for leveraging quantum advantages in machine learning, aiming to unlock further potential in this rapidly evolving field.

quant-ph↗

arXiv2024

Charge-Density-Wave State in Extremely Overdoped Cuprates Driven by Phonons

Recent resonant x-ray scattering (RXS) experiments revealed a novel charge order in extremely overdoped La$_{2-x}$Sr$_x$CuO$_4$ (LSCO) [Phys. Rev. Lett. 131,116002]. The observed charge order appears around the $(π/3,0)$ wavevector, distinct from the well-known stripe fluctuations near 1/8 doping, and persists from cryogenic temperatures to room temperature. To investigate the origin of this charge order in the overdoped regime, we use determinant quantum Monte Carlo (DQMC) simulations to examine correlated models with various interactions. We demonstrate that this distinctive CDW originates from remnant correlations in extremely overdoped cuprates, with its specific pattern shaped by interactions beyond the Hubbard model, particularly electron-phonon couplings. The persistence of the $(π/3,0)$ wavevector across different doping levels indicates the presence of nonlocal couplings. Our study reveals the significant role of phonons in cuprates, which assist correlated electrons in the formation of unconventional phases.

cond-mat.str-el↗

arXiv2024

A Hybrid Quantum-Classical AI-Based Detection Strategy for Generative Adversarial Network-Based Deepfake Attacks on an Autonomous Vehicle Traffic Sign Classification System

The perception module in autonomous vehicles (AVs) relies heavily on deep learning-based models to detect and identify various objects in their surrounding environment. An AV traffic sign classification system is integral to this module, which helps AVs recognize roadway traffic signs. However, adversarial attacks, in which an attacker modifies or alters the image captured for traffic sign recognition, could lead an AV to misrecognize the traffic signs and cause hazardous consequences. Deepfake presents itself as a promising technology to be used for such adversarial attacks, in which a deepfake traffic sign would replace a real-world traffic sign image before the image is fed to the AV traffic sign classification system. In this study, the authors present how a generative adversarial network-based deepfake attack can be crafted to fool the AV traffic sign classification systems. The authors developed a deepfake traffic sign image detection strategy leveraging hybrid quantum-classical neural networks (NNs). This hybrid approach utilizes amplitude encoding to represent the features of an input traffic sign image using quantum states, which substantially reduces the memory requirement compared to its classical counterparts. The authors evaluated this hybrid deepfake detection approach along with several baseline classical convolutional NNs on real-world and deepfake traffic sign images. The results indicate that the hybrid quantum-classical NNs for deepfake detection could achieve similar or higher performance than the baseline classical convolutional NNs in most cases while requiring less than one-third of the memory required by the shallowest classical convolutional NN considered in this study.

cs.AI↗

arXiv2023

Static and dynamical magnetic properties of the extended Kitaev-Heisenberg model with spin vacancies

Motivated by the potential to suppress the antiferromagnetic long-range order in favor of the long-sought-after Kitaev quantum spin liquid state, we study the effect of spin vacancies in the extended Kitaev-Heisenberg model. In particular, we focus on a realistic model obtained from fitting inelastic neutron scattering on $α$-RuCl$_3$. We observe that the long-range zigzag magnetic ordered state only survives when the doping concentration is smaller than 5\%. Upon further increasing the spin vacancy concentration, the ground state becomes a short-range ordered state at low temperatures. Compared with experiments, our classical solution over-stabilizes the zigzag correlation in the presence of spin vacancies. Our theoretical results provide guidance toward interpreting inelastic neutron scattering experiments on magnetically diluted Kitaev candidate materials.

cond-mat.str-el↗

arXiv2023

Testing Electron-phonon Coupling for the Superconductivity in Kagome Metal $\rm{CsV_3Sb_5}$

In crystalline materials, electron-phonon coupling (EPC) is a ubiquitous many-body interaction that drives conventional Bardeen-Cooper-Schrieffer superconductivity. Recently, in a new kagome metal $\rm{CsV_3Sb_5}$, superconductivity that possibly intertwines with time-reversal and spatial symmetry-breaking orders is observed. Density functional theory calculations predicted weak EPC strength,$λ$, supporting an unconventional pairing mechanism in $\rm{CsV_3Sb_5}$. However, experimental determination of $λ$ is still missing, hindering a microscopic understanding of the intertwined ground state of $\rm{CsV_3Sb_5}$. Here, using 7-eV laser-based angle-resolved photoemission spectroscopy and Eliashberg function analysis, we determine an intermediate $λ$=0.45~0.6 at T=6 K for both Sb 5p and V 3d electronic bands, which can support a conventional superconducting transition temperature on the same magnitude of experimental value in $\rm{CsV_3Sb_5}$. Remarkably, the EPC on the V 3d-band enhances to $λ$~0.75 as the superconducting transition temperature elevated to 4.4 K in $\rm{Cs(V_{0.93}Nb_{0.07})_3Sb_5}$. Our results provide an important clue to understand the pairing mechanism in the Kagome superconductor $\rm{CsV_3Sb_5}$.

cond-mat.supr-con↗

arXiv2023

Magnetic phases of the anisotropic triangular lattice Hubbard model

The Hubbard model on an anisotropic triangular lattice in two dimensions, a fundamental model for frustrated electron physics, displays a wide variety of phases and phase transitions. This work investigates the model using the ladder dual fermion approximation which captures local correlations non-perturbatively but approximates non-local correlations. We find metallic, one-dimensional antiferromagnetic, non-collinear antiferromagnetic, square-lattice antiferromagnetic, and spiral phases but no evidence of collinear antiferromagnetic order in different parts of the phase diagram. Analyzing the spin susceptibility in detail, we see both regions of agreement and of discrepancy with previous work. The case of Cs$_2$CuCl$_4$ is discussed in detail.

cond-mat.str-el↗

arXiv2022

Topological Superconductivity From Forward Phonon Scatterings

We propose a new Rashba-free mechanism to realize topological superconductivity with electron-phonon interaction. In the presence of a magnetic field, electron-phonon interaction with small momentum transfer is found to favor spin-triplet Cooper pairing. This process facilitates the formation of chiral topological superconductivity even when Rashba spin-orbital coupling is absent. As a proof of concept, we propose an experimentally feasible heterostructure to systematically study the entangled relationship among forward-phonon scatterings, Rashba spin-orbital couplings, pairing symmetries, and superconducting topology. Our theory sheds light on the important role of electron-phonon coupled materials in the pursuit of non-Abelian Majorana quasiparticles.

cond-mat.supr-con↗

arXiv2022

Suppressed superexchange interactions in the cuprates by bond-stretching oxygen phonons

We study a multi-orbital Hubbard--Su-Schrieffer-Heeger model for the one-dimensional (1D) corner-shared cuprates in the adiabatic and nonadiabatic limits using the exact diagonalization and determinant quantum Monte Carlo methods. Our results demonstrate that lattice dimerization can be achieved only over a narrow range of couplings slightly below a critical coupling $g_c$ at half-filling. Beyond this critical coupling, the sign of the effective hopping changes, and the lattice becomes unstable. We also examine the model's temperature-dependent uniform magnetic susceptibility and the dynamical magnetic susceptibility and compare them to the results of an effective spin-$1/2$ Heisenberg model. In doing so, we numerically demonstrate that the lattice fluctuation induced by the $e$-ph interaction suppresses the effective superexchange interaction. Our results elucidate the effect of bond-stretching phonons in the parent cuprate compounds in general and are particularly relevant to 1D cuprates, where strong $e$-ph interactions have recently been inferred.

cond-mat.str-el↗

arXiv2022

Thermal Hall Effect in the Kitaev-Heisenberg System with Spin-Phonon Coupling

We investigate the thermal Hall effect in a Kitaev-Heisenberg system, a model for Kitaev candidate $α$-RuCl$_3$, in the presence of the coupling between spin and phonon arising from chlorine atoms' vibration. We observe that the coupling modifies the relative stability between different magnetic states under a magnetic field, especially stabilizing a canted zigzag antiferromagnetic state. Remarkably, the spin-phonon interaction has distinct effects on the thermal Hall conductivity in different magnetically ordered states. For the canted zigzag state, which is relevant to $α\text{-RuCl}_3$, the spin-phonon interaction enhances the energy gap that is induced by a magnetic field and suppresses the thermal Hall conductivity at low temperatures. Importantly, we find that the spin-phonon interaction destabilizes the quantized thermal Hall effect in the spin-liquid state. Our results demonstrate a crucial role of phonon degrees of freedom to the thermal Hall effect in Kitaev materials.

cond-mat.str-el↗

arXiv2021

Particle-hole asymmetry in the dynamical spin and charge structure factors of the corner-shared one-dimensional cuprates

The collective spin and charge excitations of doped cuprates and their relationship to superconductivity are not yet fully understood, particularly in the case of the charge excitations. Here, we study the doping-dependent dynamical spin and charge structure factors of single and multi-orbital models for the one-dimensional corner shared spin-chain cuprates using several numerically exact methods. We find that the singleband Hubbard model can describe the spin and charge excitations of the $pd$-model in the low-energy region, including the particle-hole asymmetry in the spin response. However, our results also reveal that the weight of the interorbital spin excitations between Cu and O orbitals is comparable to the weight of the spin excitations between two Cu orbitals. This finding elucidates the microscopic nature of the spin excitations in the 1D cuprates and sheds light on the spin properties of other oxides. Importantly, we find a particle-hole asymmetry in the orbital-resolved charge excitations, which cannot be described by the singleband Hubbard model and is relevant to resonant inelastic x-ray scattering experiments. Our results imply that the explicit inclusion of the oxygen degrees of freedom may be required to understand experimental observations.

cond-mat.str-el↗

arXiv2021

Phase transitions of the ferroelectric $(\mathrm{ND}_4)_2\mathrm{FeCl}_5\cdot\mathrm{D}_2\mathrm{O}$ under a magnetic field

Due to the strong coupling between magnetism and ferroelectricity, $(\mathrm{ND}_4)_2\mathrm{FeCl}_5\cdot\mathrm{D}_2\mathrm{O}$ exhibits several intriguing magnetic and electric phases. In this letter, we include high-order onsite spin anisotropic interactions in a spin model that successfully captures the ferroelectric phase transitions of $(\mathrm{ND}_4)_2\mathrm{FeCl}_5\cdot\mathrm{D}_2\mathrm{O}$ under a magnetic field and produces the large weights of high-order harmonic components in the cycloid structure that are observed from neutron diffraction experiments. Moreover, we predict a new ferroelectric phase sandwiched between the FE II and FE III phases in a magnetic field. Our results emphasize the importance of the high-order spin anisotropic interactions and provide a guideline to understand multiferroic materials with rich phase diagrams.

cond-mat.mtrl-sci↗

arXiv2021

Reinvestigation of the homogeneous spin model in YbMgGaO$_4$

Motivated by a recent inelastic neutron scattering experiment on $\mathrm{YbMgGaO}_4$ \cite{William2019}, we reinvestigate the homogeneous spin model on the triangular lattice. Using the cluster mean-field theory, we study the phase diagram and the magnetic-field-induced phase transition. We find that the phase boundary between the stripe state and the $120^{\circ}$ antiferromagnetic state is broadened by the magnetic field, leading to a field-induced phase transition. This phase transition is suppressed by the next-nearest neighbor exchange interaction $J_2/J_1$ and vanishes as $J_2/J_1>0.13$. We find a parameter space at $J_2/J_1=0.1$, in which the field-induce transition can be achieved and the deviation of theoretical spin excitation energies from experimental data is only $5.4\%$. Our results imply that an effective homogeneous spin model still works in $\mathrm{YbMgGaO}_4$.

cond-mat.str-el↗