arXiv subjects

Wenyuan Qiu

Publications and source records attributed to Wenyuan Qiu.

7 recordsLinked to original sources

arXiv2026

Pairing symmetry and superconductivity in La$_3$Ni$_2$O$_7$ thin films

The recent discovery of superconductivity with a transition temperature $T_c$ over 40 K in La$_3$Ni$_2$O$_7$ and (La,Pr)$_{3}$Ni$_2$O$_7$ thin films at ambient pressure marks an important step in the field of nickelate superconductors. Here, we perform a renormalized mean-field theory study of the superconductivity in $\mathrm{La_3Ni_2O_7}$ thin films, using a bilayer two-orbital $t-J$ model. Our result reveals an $s_\pm$-wave pairing symmetry driven by the strong interlayer superexchange coupling of $d_{z^2}$ orbital, resembling the pressurized bulk case. Also, we roughly reproduce the experimentally observed nodeless shape of the superconducting gap at the $β$ pocket and the superconducting $T_c$. In addition, by analysing the orbital-resolved pairing configurations and their projections onto Fermi surface, we find that the nodeless feature of $β$ pocket is related to the interlayer pairing within both $d_{z^2}$ and $d_{x^2-y^2}$ orbitals. Moreover, we identify a formation of the inplane inter-orbital $d$-wave pairing between $d_{z^2}$ and $d_{x^2-y^2}$ orbitals, which can even enhance the dominated interlayer $s_\pm$-wave. Our study particularly highlights the diverse relations of different pairing channels in $\mathrm{La_3Ni_2O_7}$ that holds a complex Fermi surface.

cond-mat.supr-con↗

arXiv2026

Progress of ambient-pressure superconductivity in bilayer nickelate thin films

This review summarizes recent progress of ambient-pressure superconductivity in bilayer nickelate La$_3$Ni$_2$O$_7$ thin films, a major advancement following the discovery of high-pressure superconductivity in bulk La$_3$Ni$_2$O$_7$. First, we explain how epitaxial strain engineering enables ambient-pressure superconductivity in La$_3$Ni$_2$O$_7$ thin films, with compressive strain from substrates like SrLaAlO$_4$ stabilizing superconductivity. Next, we review experimental characterizations of related systems, with particular emphasis on ARPES measurements that have shown conflicting Fermi surface topologies. We then discuss progress in increasing the superconducting transition temperature $T_c$. Finally, we summarize theoretical studies of the electronic structure and pairing symmetry of La$_3$Ni$_2$O$_7$ thin films. Together, these advances establish bilayer nickelate thin films as a highly tunable and promising platform for exploring high-$T_c$ superconductivity.

cond-mat.supr-con↗

arXiv2026

Electronic structures and multi-orbital models of La$_3$Ni$_2$O$_7$ thin films at ambient pressure

The recent discovery of superconductivity with a transition temperature $T_c$ exceeding 40 K in La$_3$Ni$_2$O$_7$ and (La,Pr)$_{3}$Ni$_2$O$_7$ thin films at ambient pressure marks a significant breakthrough in the field of nickelate superconductors. Using density functional theory (DFT), we propose a double-stacked two-orbital effective model for La$_3$Ni$_2$O$_7$ thin film based on the Ni$-e_g$ orbitals. Our analysis of the Fermi surface reveals three electron pockets ($α,α^{\prime},β$) and two hole pockets ($γ,γ^{\prime}$), where the additional $α^{\prime}$ and $γ^{\prime}$ pockets arise from inter-stack interactions. Furthermore, we introduce a high-energy model that incorporates O$-p$ orbitals to facilitate future studies. Calculations of spin susceptibility within the random phase approximation (RPA) indicate that magnetic correlations are enhanced by nesting of the $γ$ pocket, which is predominantly derived from the Ni$-d_{z^2}$ orbital. Our results provide a theoretical foundation for understanding the electronic and magnetic properties of La$_3$Ni$_2$O$_7$ thin films.

cond-mat.supr-con↗

arXiv2026

Electronic structures and superconductivity in Nd-doped La$_3$Ni$_2$O$_7$

The recent discovery of high-$T_c$ superconductivity in Ruddlesden-Popper (RP) nickelates has motivated extensive efforts to explore higher $T_c$ superconductors. Here, we systematically investigate Nd-doped La$_3$Ni$_2$O$_7$ using density functional theory (DFT) and renormalized mean-field theory (RMFT). DFT calculations reveal that both the lattice constants and interlayer spacing decrease upon Nd substitution, similar to the effect of physical pressure. However, the in-plane Ni-O-Ni bond angle evolves non-monotonically with doping, increasing to a maximum at 70% ($\sim$ 2/3) Nd doping level and then falling sharply at 80%, which leads to a reduction in orbital overlap. Moreover, Nd doping has a more pronounced effect on the Ni-$d{_{z^2}}$ orbital, demonstrating an orbital-dependent effect of rare-earth substitution. Through the bilayer two-orbital $t-J$ model, RMFT analysis further shows an $s\pm$-wave pairing symmetry, with $T_c$ rising to a maximum at about 70% Nd substitution before declining, in agreement with the transport measurements. The variation in $T_c$ can be traced to the competition between continuously enhanced interlayer superexchange coupling $J_\perp^z$ and a gradual decrease in particle density. These results highlight the delicate interplay among structural tuning, orbital hybridization, and superconductivity, providing important clues to design higher-$T_c$ RP nickelate superconductors.

cond-mat.supr-con↗

arXiv2019

Double Weighted Truncated Nuclear Norm Regularization for Low-Rank Matrix Completion

Matrix completion focuses on recovering a matrix from a small subset of its observed elements, and has already gained cumulative attention in computer vision. Many previous approaches formulate this issue as a low-rank matrix approximation problem. Recently, a truncated nuclear norm has been presented as a surrogate of traditional nuclear norm, for better estimation to the rank of a matrix. The truncated nuclear norm regularization (TNNR) method is applicable in real-world scenarios. However, it is sensitive to the selection of the number of truncated singular values and requires numerous iterations to converge. Hereby, this paper proposes a revised approach called the double weighted truncated nuclear norm regularization (DW-TNNR), which assigns different weights to the rows and columns of a matrix separately, to accelerate the convergence with acceptable performance. The DW-TNNR is more robust to the number of truncated singular values than the TNNR. Instead of the iterative updating scheme in the second step of TNNR, this paper devises an efficient strategy that uses a gradient descent manner in a concise form, with a theoretical guarantee in optimization. Sufficient experiments conducted on real visual data prove that DW-TNNR has promising performance and holds the superiority in both speed and accuracy for matrix completion.

cs.CV↗

arXiv2019

Truncated nuclear norm regularization for low-rank tensor completion

Recently, low-rank tensor completion has become increasingly attractive in recovering incomplete visual data. Considering a color image or video as a three-dimensional (3D) tensor, existing studies have put forward several definitions of tensor nuclear norm. However, they are limited and may not accurately approximate the real rank of a tensor, and they do not explicitly use the low-rank property in optimization. It is proved that the recently proposed truncated nuclear norm (TNN) can replace the traditional nuclear norm, as an improved approximation to the rank of a matrix. In this paper, we propose a new method called the tensor truncated nuclear norm (T-TNN), which suggests a new definition of tensor nuclear norm. The truncated nuclear norm is generalized from the matrix case to the tensor case. With the help of the low rankness of TNN, our approach improves the efficacy of tensor completion. We adopt the definition of the previously proposed tensor singular value decomposition, the alternating direction method of multipliers, and the accelerated proximal gradient line search method in our algorithm. Substantial experiments on real-world videos and images illustrate that the performance of our approach is better than those of previous methods.

cs.CV↗

arXiv2018

Low-Rank Tensor Completion by Truncated Nuclear Norm Regularization

Currently, low-rank tensor completion has gained cumulative attention in recovering incomplete visual data whose partial elements are missing. By taking a color image or video as a three-dimensional (3D) tensor, previous studies have suggested several definitions of tensor nuclear norm. However, they have limitations and may not properly approximate the real rank of a tensor. Besides, they do not explicitly use the low-rank property in optimization. It is proved that the recently proposed truncated nuclear norm (TNN) can replace the traditional nuclear norm, as a better estimation to the rank of a matrix. Thus, this paper presents a new method called the tensor truncated nuclear norm (T-TNN), which proposes a new definition of tensor nuclear norm and extends the truncated nuclear norm from the matrix case to the tensor case. Beneficial from the low rankness of TNN, our approach improves the efficacy of tensor completion. We exploit the previously proposed tensor singular value decomposition and the alternating direction method of multipliers in optimization. Extensive experiments on real-world videos and images demonstrate that the performance of our approach is superior to those of existing methods.

cs.CV↗