arXiv subjects

Jia-Ji Zhu

Publications and source records attributed to Jia-Ji Zhu.

At least 19 recordsLinked to original sources

arXiv2026

Chiral Transfer and Entanglement Generation of Even-Parity Bell States with Engineered Two-Photon Loss

We investigate chiral transfer and dissipative generation of even-parity Bell states in a two-qubit system with coherent two-photon driving and engineered two-photon loss. We find that adiabatic encirclement of a second-order exceptional point induces direction-dependent transfer between the Bell states $|Φ^+\rangle$ and $|Φ^-\rangle$, governed by a time-integrated low-loss branch-selection mechanism. We develop a hybrid-Liouvillian description with a control parameter $q$ that interpolates between conditional no-jump dynamics and unconditional Lindblad evolution, and use it to assess how exceptional-point-induced chirality survives in the presence of quantum jumps. When the same parameter loop is initialized in the separable state $|00\rangle$, it directly generates strong even-parity entanglement. Together, these results extend dissipative Bell-state control beyond the single-excitation manifold. They further demonstrate that exceptional-point-based protocols can unify chiral state transfer and entanglement generation within a single engineered two-photon platform.

quant-ph↗

arXiv2026

Point-gap topology in amorphous non-Hermitian quantum systems

Recent studies have revealed that not only does the correspondence between spectral winding numbers and skin modes break down in non-Hermitian systems, but the energy spectrum itself is highly sensitive to generic perturbations, system size, and boundary conditions. In amorphous non-Hermitian systems, where the positions of lattice sites are uncertain, the spectral instability becomes even more severe, making it difficult to identify stable topological edge states from the eigenvalue spectrum alone. To overcome this challenge, we introduce a correspondence between stable zero-mode singular states and mid-gap states of the energy spectrum in the thermodynamic limit. Because the singular value spectrum is highly robust against small perturbations and variation in size, topological edge states can be reliably probed via singular values even in finite-sized systems. Based on the singular-value decomposition of the Hamiltonian, we construct a topological invariant in real space to characterize the associated topologically protected edge states. Our approach provides a general strategy for exploring point-gap topology in real space and redefine the non-Hermitian skin effect from a new perspective.

quant-ph↗

arXiv2026

Grassmann tensor networks

Developing non-perturbative methods to reveal exotic properties of strongly correlated fermionic systems remains one of the most essential tasks of theoretical physics. Tensor network methods with Grassmann algebra offer powerful numerical tools for fermionic many-body systems in the coherent-state path-integral representation. Despite their vast potential for both condensed-matter and particle-physics communities, Grassmann tensor network methods are somewhat underexploited in practical simulations. In this work, we provide a detailed, self-contained introduction to Grassmann tensor network methods, from the basics of the Grassmann tensor operations to the Grassmannization of typical tensor network algorithms. Furthermore, the resulting Grassmann tensor network methods are validated in several interesting models in both particle physics and condensed matter physics.

cond-mat.str-el↗

arXiv2026

Spin-polarized scanning tunneling microscopy measurement scheme for determining the quantum geometric tensor

The quantum geometric tensor (QGT) embodies the geometry of the eigenstates of a system's Hamiltonian, and its full characterization across diverse quantum systems is essential. However, it is challenging to characterize the QGT of solid-state systems. Here we present an electric scheme to measure the complete QGT of two-dimensional solid-state systems by using spin-polarized scanning tunneling microscopy (STM), in which the spin texture is extracted from geometric amplitudes of Friedel oscillations induced by the intentionally introduced magnetic impurity, and then the QGT is derived from the momentum differential of spin texture. As a canonical spin model, the surface states of a topological insulator offer a promising way to demonstrate the scheme. In a slab of topological insulator, the gapped surface states host complete QGT, i.e., nonvanishing quantum metric and Berry curvature as its symmetric real part and the antisymmetric imaginary part. Thus, a detailed derivation guides the use of the developed scheme to measure the QGT of gapped surface states, even with an external magnetic field. This study opens a new avenue to directly measure the complete QGT of two-dimensional solid-state systems by using spin-polarized STM.

cond-mat.mes-hall↗

arXiv2026

Non-Hermitian second-order topological insulator with point gap

The zero-mode corner states in the gap of two-dimensional non-Hermitian Su-Schrieffer-Heeger model are robust to infinitesimal perturbations that preserve chiral symmetry. However, we demonstrate that this general belief is no longer valid in large-sized systems. To reveal the higher-order topology of non-Hermitian systems, we establish a correspondence between the stable zero-mode singular states and the topologically protected corner states of energy spectrum in the thermodynamic limit. Within this framework, the number of zero-mode singular values is directly linked to the number of mid-gap corner states. The winding numbers in real space can be defined to count the number of stable zero-mode singular states. Our results formulate a bulk-boundary correspondence for both static and Floquet non-Hermitian systems, where topology arises intrinsically from the non-Hermiticity, even without symmetries.

quant-ph↗

arXiv2025

Floquet composite Dirac semimetals

Dirac semimetals can be classified into types I, II, and III based on the topological charge of their Dirac points. If a three-dimensional (3D) system can be sliced into a family of kz-dependent normal and topological insulators, type I Dirac points separate a 2D normal insulator from a 2D first-order topological insulator, while type II (III) Dirac points separate a 2D normal (first-order) insulator from a 2D second-order topological insulator. To investigate the effects arising from the interplay of distinct Dirac points, one may wonder whether these Dirac points can coexist in a single system. Here, we propose a scheme to induce composite Dirac semimetals by a special Floquet driving that preserves time-reversal and space-inversion symmetries. A general description is established to characterize Dirac semimetals in Floquet systems. The results show that Dirac semimetals hosting coexisting type I, II, and III Dirac points can be induced by delta-function or harmonic driving. Our results provide a promising new avenue for exploring novel Dirac semimetals.

cond-mat.mes-hall↗

arXiv2025

The electronic structure of intrinsic magnetic topological insulator MnBi2Te4 quantum wires

The ferromagnetic and antiferromagnetic nanostructure are crucial for fundamental spintronics devices, motivated by its potential application in spintronics, we theoretically investigate the electronic structure of the ferromagnetic and antiferromagnetic phases of the cylindrical intrinsically magnetic topological insulator $\mathrm{MnBi_{2}Te_{4}}$ quantum wires for both cases. We demonstrate that a few surface states exist between the bulk band gap in the ferromagnetic phase, with only one spin branch. In the antiferromagnetic phase, we show that three coexistent states exist between the energy gaps of the quantum wires.

cond-mat.mes-hall↗

arXiv2024

Tunable non-Hermitian skin effect via gain and loss

We investigate theoretically tunable non-Hermitian skin effect in systems with gain and loss, and find that bipolar (quadripolar) non-Hermitian skin effect characterized by topological invariants in one (two)-dimensional system. We also find the partial non-Hermitian skin effect with the coexistence of localized states and extended states. Both types of the non-Hermitian skin effect have not yet been predicted together in a single system. A feasible experimental scheme of our model is proposed to realize in electric circuits. Our investigation unveils a new type of non-Hermitian skin effect and enhance the tunability of the non-Hermitian systems by gain and loss other than the conventional non-reciprocal hopping.

physics.app-ph↗

arXiv2024

RKKY interaction in helical higher-order topological insulators

We theoretically investigate the RKKY interaction in helical higher-order topological insulators (HOTIs), revealing distinct behaviors mediated by hinge and Dirac-type bulk carriers. Our findings show that hinge-mediated interactions consist of Heisenberg, Ising, and Dzyaloshinskii-Moriya (DM) terms, exhibiting a decay with impurity spacing z and oscillations with Fermi energy εF . These interactions demonstrate ferromagnetic behaviors for the Heisenberg and Ising terms and alternating behavior for the DM term. In contrast, bulk-mediated interactions include Heisenberg, twisted Ising, and DM terms, with a conventional cubic oscillating decay. This study highlights the nuanced interplay between hinge and bulk RKKY interactions in HOTIs, offering insights into the design of next-generation quantum devices based on the HOTIs.

cond-mat.mes-hall↗

arXiv2024

Self-supervised Representations and Node Embedding Graph Neural Networks for Accurate and Multi-scale Analysis of Materials

Supervised machine learning algorithms, such as graph neural networks (GNN), have successfully predicted material properties. However, the superior performance of GNN usually relies on end-to-end learning on large material datasets, which may lose the physical insight of multi-scale information about materials. And the process of labeling data consumes many resources and inevitably introduces errors, which constrains the accuracy of prediction. We propose to train the GNN model by self-supervised learning on the node and edge information of the crystal graph. Compared with the popular manually constructed material descriptors, the self-supervised atomic representation can reach better prediction performance on material properties. Furthermore, it may provide physical insights by tuning the range information. Applying the self-supervised atomic representation on the magnetic moment datasets, we show how they can extract rules and information from the magnetic materials. To incorporate rich physical information into the GNN model, we develop the node embedding graph neural networks (NEGNN) framework and show significant improvements in the prediction performance. The self-supervised material representation and the NEGNN framework may investigate in-depth information from materials and can be applied to small datasets with increased prediction accuracy.

cond-mat.mtrl-sci↗

arXiv2024

Emergence of Surface Superconductivity through Interference in Superconducting-proximity Topological Insulators

Superconducting-proximity topological insulators (STIs) have garnered significant research attention over the past two decades. In this Letter, we demonstrate that a low-dimensional STI in the topological-nontrivial phase (TP) exhibits an interference-induced surface (boundary) superconductivity with the surface critical temperature $T_{cs}$ significantly higher than the bulk one $T_{cb}$. Such a surface superconductivity is built due to the interference of the scattering quasiparticle states, rather than due to the presence of the topological bound states (TBSs). As the system goes deeper into the TP, the surface exhibits a crossover from the interference- to TBS-induced phase, where the surface enhancement of superconductivity is governed by the TBSs. Our study unveils a substantial variation in the maximal $T_{cs}$ along this crossover, attaining values being twice the maximal bulk critical temperature of the STI. Beyond shedding light on the nature of surface superconductivity in STIs, our study introduces a tangible method for experimentally manipulating their critical superconducting temperatures.

cond-mat.supr-con↗

arXiv2023

Geometric density of states of electronic structures for local responses: Phase information from the amplitudes of STM measurement

Electronic band structures underlie the physical properties of crystalline materials, their geometrical exploration renovates the conventional cognition and brings about novel applications. Inspired by geometry phases, we introduce a geometric amplitude named as the geometric density of states (GDOS) dictated by the differential curvature of the constant-energy contour. The GDOS determines the amplitude of the real-space Green's function making it attain the ultimate expression with transparent physics. The local responses of crystalline materials are usually formulated by the real-space Green's function, so the relevant physics should be refreshed by GDOS. As an example of local responses, we suggest using scanning tunneling microscopy (STM) to characterize the surface states of three-dimensional topological insulator under an in-plane magnetic field. The GDOS favors the straightforward simulation of STM measurement without resorting to Fourier transform of the real-space measurement, and also excavates the unexplored potential of STM measurement to extract the phase information of wavefunction through its amplitude, i.e., the spin and curvature textures. Therefore, the proposed GDOS deepens the understanding of electronic band structures and is indispensable in local responses, and it should be universal for any periodic systems.

cond-mat.mes-hall↗

arXiv2022

Self-supervised graph neural networks for accurate prediction of Néel temperature

Antiferromagnetic materials are exciting quantum materials with rich physics and great potential for applications. It is highly demanded of the accurate and efficient theoretical method for determining the critical transition temperatures, Néel temperatures, of antiferromagnetic materials. The powerful graph neural networks (GNN) that succeed in predicting material properties lose their advantage in predicting magnetic properties due to the small dataset of magnetic materials, while conventional machine learning models heavily depend on the quality of material descriptors. We propose a new strategy to extract high-level material representations by utilizing self-supervised training of GNN on large-scale unlabeled datasets. According to the dimensional reduction analysis, we find that the learned knowledge about elements and magnetism transfers to the generated atomic vector representations. Compared with popular manually constructed descriptors and crystal graph convolutional neural networks, self-supervised material representations can help us obtain a more accurate and efficient model for Néel temperatures, and the trained model can successfully predict high Néel temperature antiferromagnetic materials. Our self-supervised GNN may serve as a universal pre-training framework for various material properties.

cond-mat.mtrl-sci↗

arXiv2021

Oblique and asymmetric Klein tunneling across smooth NP junctions or NPN junctions in 8-Pmmn borophene

The tunneling of electrons and holes in quantum structures plays a crucial role in studying the transport properties of materials and the related devices. 8-Pmmn borophene is a new two-dimensional Dirac material, which hosts tilted Dirac cone and chiral, anisotropic massless Dirac fermions. We develop the transfer matrix method to investigate the Klein tunneling of massless fermions across the smooth NP junctions and NPN junctions of 8-Pmmn borophene. Like the sharp NP junctions of 8-Pmmn borophene, the tilted Dirac cones induce the oblique Klein tunneling. The angle of perfect transmission to the normal incidence is 20.4 degrees, a constant determined by the Hamiltonian of 8-Pmmn borophene. For the NPN junction, there are branches of the Klein tunneling in the phase diagram. We find that the asymmetric Klein tunneling is induced by the chirality and anisotropy of the carriers. Furthermore, we show the oscillation of electrical resistance related to the Klein tunneling in the NPN junctions. One may analyze the pattern of electrical resistance and verify the existence of asymmetric Klein tunneling experimentally.

cond-mat.mes-hall↗

arXiv2020

An Origin of Dzyaloshinskii-Moriya Interaction at Graphene-Ferromagnet Interfaces Due to the Intralayer RKKY/BR Interaction

We present a theory of both the itinerant carrier-mediated RKKY interaction and the virtual excitations-mediated Bloembergen-Rowland (BR) interaction between magnetic moments in graphene induced by proximity effect with a ferromagnetic film. We show that the RKKY/BR interaction consists of the Heisenberg, Ising, and Dzyaloshinskii-Moriya (DM) terms. In the case of the nearest distance, we estimate the DM term from the RKKY/BR interaction is about 0.13 meV for the graphene/Co interface, which is consistent with the experimental result of DM interaction $0.16 \pm 0.05$ meV. Our calculations indicate that the intralayer RKKY/BR interaction may be a possible physical origin of the DM interaction in the graphene-ferromagnet interface. This work provides a new perspective to comprehend the DM interaction in graphene/ferromagnet systems.

cond-mat.mes-hall↗

arXiv2019

Few-shot machine learning in the three-dimensional Ising model

We investigate theoretically the phase transition in three dimensional cubic Ising model utilizing state-of-the-art machine learning algorithms. Supervised machine learning models show high accuracies (~99\%) in phase classification and very small relative errors ($< 10^{-4}$) of the energies in different spin configurations. Unsupervised machine learning models are introduced to study the spin configuration reconstructions and reductions, and the phases of reconstructed spin configurations can be accurately classified by a linear logistic algorithm. Based on the comparison between various machine learning models, we develop a few-shot strategy to predict phase transitions in larger lattices from trained sample in smaller lattices. The few-shot machine learning strategy for three dimensional(3D) Ising model enable us to study 3D ising model efficiently and provides a new integrated and highly accurate approach to other spin models.

cond-mat.dis-nn↗

arXiv2018

Selective generation and amplification of RKKY interactions by P-N interface

We propose a physical mechanism to generate and selectively amplify anisotropic Rudermann-Kittel-Kasuya-Yosida (RKKY) interactions between two local spins. The idea is to combine the deflection of the carrier velocity by a P-N interface and the locking of this velocity to the carrier spin orientation via spin-orbit coupling. We provide analytical and numerical results to demonstrate this mechanism on the surface of a topological insulator P-N junction. This work identifies the P-N interface as a second knob which, together with the carrier density, enables independent control of the strength and anisotropy of the RKKY interaction over a wide range. These findings may be relevant to scalable quantum computation and two-impurity quantum criticality.

cond-mat.mes-hall↗

arXiv2017

Focusing RKKY interaction by graphene P-N junction

The carrier-mediated RKKY interaction between local spins plays an important role for the application of magnetically doped graphene in spintronics and quantum computation. Previous studies largely concentrate on the influence of electronic states of uniform systems on the RKKY interaction. Here we reveal a very different way to manipulate the RKKY interaction by showing that the anomalous focusing - a well-known electron optics phenomenon in graphene P-N junctions - can be utilized to refocus the massless Dirac electrons emanating from one local spin to the other local spin. This gives rise to rich spatial interference patterns and symmetry-protected non-oscillatory RKKY interaction with a strongly enhanced magnitude. It may provide a new way to engineer the long-range spin-spin interaction in graphene.

cond-mat.mes-hall↗