arXiv subjects

Yuanping Chen

Publications and source records attributed to Yuanping Chen.

At least 19 recordsLinked to original sources

arXiv2026

Biorthogonal dynamical quantum phase transitions in non-Hermitian topological superconductors

Dynamical quantum phase transitions in non-Hermitian systems pose fundamental challenges due to the intrinsic biorthogonality of their eigenstates. In this work, we extend a biorthogonal framework to investigate dynamical quantum phase transitions in non-Hermitian topological superconductors. Taking the non-Hermitian Kitaev chain as a prototypical model, we construct an associated-state formalism and reformulate the Loschmidt rate function, dynamical topological order parameter, and dynamical Fisher zeros. Within this framework, we find that the critical times at which dynamical quantum phase transitions occur differ from those based on the conventional self-normal approaches. We further analyze momentum-resolved subsystems at critical momenta and demonstrate the robustness of the biorthogonal framework. Our work highlights the essential role of biorthogonality in nonequilibrium dynamics and establishes a consistent theoretical framework for dynamical quantum phase transitions in non-Hermitian topological superconductors.

quant-ph↗

arXiv2026

Entanglement signatures of quantum criticality in Floquet non-Hermitian topological systems

The entanglement entropy can be an effective diagnostic tool for probing topological phase transitions. In one-dimensional single particle systems, the periodic driving generates a variety of topological phases and edge modes. In this work, we investigate the topological phase transition of the one-dimensional Floquet Su-Schrieffer-Heeger model using entanglement entropy, and construct the phase diagram based on entanglement entropy. The entanglement entropy exhibits pronounced peaks and follows the logarithmic scaling law at the phase transition points, from which we extract the central charge $c=1$. We further investigate the entanglement spectrum to accurately distinguish the different topological phases. In addition, the coupling between zero and $\pi$ modes leads to characteristic splittings in the entanglement spectrum, signaling their hybridization under periodic driving. These results remain robust in non-Hermitian regimes and in the presence of next-nearest-neighbor hopping, demonstrating the reliability and universality of entanglement entropy as a diagnostic for topological phase transitions.

quant-ph↗

arXiv2025

SDS-Net: Shallow-Deep Synergism-detection Network for infrared small target detection

Current CNN-based infrared small target detection(IRSTD) methods generally overlook the heterogeneity between shallow and deep features, leading to inefficient collaboration between shallow fine grained structural information and deep high-level semantic representations. Additionally, the dependency relationships and fusion mechanisms across different feature hierarchies lack systematic modeling, which fails to fully exploit the complementarity of multilevel features. These limitations hinder IRSTD performance while incurring substantial computational costs. To address these challenges, this paper proposes a shallow-deep synergistic detection network (SDS-Net) that efficiently models multilevel feature representations to increase both the detection accuracy and computational efficiency in IRSTD tasks. SDS-Net introduces a dual-branch architecture that separately models the structural characteristics and semantic properties of features, effectively preserving shallow spatial details while capturing deep semantic representations, thereby achieving high-precision detection with significantly improved inference speed. Furthermore, the network incorporates an adaptive feature fusion module to dynamically model cross-layer feature correlations, enhancing overall feature collaboration and representation capability. Comprehensive experiments on three public datasets (NUAA-SIRST, NUDT-SIRST, and IRSTD-1K) demonstrate that SDS-Net outperforms state-of-the-art IRSTD methods while maintaining low computational complexity and high inference efficiency, showing superior detection performance and broad application prospects. Our code will be made public at https://github.com/PhysiLearn/SDS-Net.

cs.CV↗

arXiv2025

MSCA-Net:Multi-Scale Context Aggregation Network for Infrared Small Target Detection

In complex environments, detecting tiny infrared targets has always been challenging because of the low contrast and high noise levels inherent in infrared images. These factors often lead to the loss of crucial details during feature extraction. Moreover, existing detection methods have limitations in adequately integrating global and local information, which constrains the efficiency and accuracy of infrared small target detection. To address these challenges, this paper proposes a network architecture named MSCA-Net, which integrates three key components: Multi-Scale Enhanced Dilated Attention mechanism (MSEDA), Positional Convolutional Block Attention Module (PCBAM), and Channel Aggregation Feature Fusion Block (CAB). Specifically, MSEDA employs a multi-scale feature fusion attention mechanism to adaptively aggregate information across different scales, enriching feature representation. PCBAM captures the correlation between global and local features through a correlation matrix-based strategy, enabling deep feature interaction. Moreover, CAB enhances the representation of critical features by assigning greater weights to them, integrating both low-level and high-level information, and thereby improving the models detection performance in complex backgrounds. The experimental results demonstrate that MSCA-Net achieves strong small target detection performance in complex backgrounds. Specifically, it attains mIoU scores of 78.43%, 94.56%, and 67.08% on the NUAA-SIRST, NUDT-SIRST, and IRTSD-1K datasets, respectively, underscoring its effectiveness and strong potential for real-world applications.

cs.CV↗

arXiv2025

Detection and control of electronic orbital magnetism by spin waves in honeycomb ferromagnets

Exploring and manipulating the orbital degrees of freedom in solids has become a fascinating research topic in modern magnetism. Here, we demonstrate that spin waves can provide a way to control electronic orbital magnetism by the mechanism of scalar spin chirality, allowing for experimental detection using techniques such as the magneto-optical Kerr effect and scanning transmission electron microscopy. By applying linear spin wave theory, we uncover that electronic magnon-driven orbital magnetization is extremely sensitive to the character of the magnonic excitations. Furthermore, we show that both the induced electronic orbital magnetism and the Nernst transport properties of the orbital angular momentum can be regulated by the strength of the Dzyaloshinskii-Moriya interaction, Kitaev interaction, as well as the direction and magnitude of the external magnetic field. We argue that magnon-mediated electronic orbital magnetism presents an emergent variable which has to be taken into account when considering the physics of coupling magnonic excitiations to phonons and light.

cond-mat.str-el↗

arXiv2025

D3MES: Diffusion Transformer with multihead equivariant self-attention for 3D molecule generation

Understanding and predicting the diverse conformational states of molecules is crucial for advancing fields such as chemistry, material science, and drug development. Despite significant progress in generative models, accurately generating complex and biologically or material-relevant molecular structures remains a major challenge. In this work, we introduce a diffusion model for three-dimensional (3D) molecule generation that combines a classifiable diffusion model, Diffusion Transformer, with multihead equivariant self-attention. This method addresses two key challenges: correctly attaching hydrogen atoms in generated molecules through learning representations of molecules after hydrogen atoms are removed; and overcoming the limitations of existing models that cannot generate molecules across multiple classes simultaneously. The experimental results demonstrate that our model not only achieves state-of-the-art performance across several key metrics but also exhibits robustness and versatility, making it highly suitable for early-stage large-scale generation processes in molecular design, followed by validation and further screening to obtain molecules with specific properties.

cs.LG↗

arXiv2024

YOLO-MST: Multiscale deep learning method for infrared small target detection based on super-resolution and YOLO

With the advancement of aerospace technology and the increasing demands of military applications, the development of low false-alarm and high-precision infrared small target detection algorithms has emerged as a key focus of research globally. However, the traditional model-driven method is not robust enough when dealing with features such as noise, target size, and contrast. The existing deep-learning methods have limited ability to extract and fuse key features, and it is difficult to achieve high-precision detection in complex backgrounds and when target features are not obvious. To solve these problems, this paper proposes a deep-learning infrared small target detection method that combines image super-resolution technology with multi-scale observation. First, the input infrared images are preprocessed with super-resolution and multiple data enhancements are performed. Secondly, based on the YOLOv5 model, we proposed a new deep-learning network named YOLO-MST. This network includes replacing the SPPF module with the self-designed MSFA module in the backbone, optimizing the neck, and finally adding a multi-scale dynamic detection head to the prediction head. By dynamically fusing features from different scales, the detection head can better adapt to complex scenes. The mAP@0.5 detection rates of this method on two public datasets, SIRST and IRIS, reached 96.4% and 99.5% respectively, more effectively solving the problems of missed detection, false alarms, and low precision.

cs.CV↗

arXiv2024

Discovery of 2D Materials via Symmetry-Constrained Diffusion Model

Generative model for 2D materials has shown significant promise in accelerating the material discovery process. The stability and performance of these materials are strongly influenced by their underlying symmetry. However, existing generative models for 2D materials often neglect symmetry constraints, which limits both the diversity and quality of the generated structures. Here, we introduce a symmetry-constrained diffusion model (SCDM) that integrates space group symmetry into the generative process. By incorporating Wyckoff positions, the model ensures adherence to symmetry principles, leading to the generation of 2,000 candidate structures. DFT calculations were conducted to evaluate the convex hull energies of these structures after structural relaxation. From the generated samples, 843 materials that met the energy stability criteria (Ehull < 0.6 eV/atom) were identified. Among these, six candidates were selected for further stability analysis, including phonon band structure evaluations and electronic properties investigations, all of which exhibited phonon spectrum stability. To benchmark the performance of SCDM, a symmetry-unconstrained diffusion model was also evaluated via crystal structure prediction model. The results highlight that incorporating symmetry constraints enhances the effectiveness of generated 2D materials, making a contribution to the discovery of 2D materials through generative modeling.

cond-mat.mtrl-sci↗

arXiv2024

Enhancement in temperature sensing of a reservoir by Kerr-nonlinear resonator

The challenge of developing high-precision temperature sensors is an important issue that has recently received a lot of attention. In this work, we introduce an estimation technique to precisely measure the temperature of a quantum reservoir using a Kerr-nonlinear resonator with drive. Thermalization in our suggested protocol is assessed using Uhlmann-Jozsa fidelity, and then we utilize quantum Fisher information to evaluate the metrological potential of our thermometry scheme. We observe that increasing the Kerr nonlinearity coefficient and driving amplitude significantly enhances precision in the temperature estimation. Furthermore, we also explore the underlying physical mechanisms by analyzing probe purity in the steady state and evaluating the performance of homodyne versus heterodyne detection methods. Our results demonstrate that neither of these Gaussian measurements is optimal; instead, optimal homodyne detection always surpasses heterodyne detection.

quant-ph↗

arXiv2024

Tunable optical amplification and group delay in cavity magnomechanics

In this work, we theoretically investigate the controllable output probe transmission and group delay in a hybrid cavity magnomechanics (CMM) system. The setup comprises a gain (active) cavity and a passive (loss) cavity, which incorporates an optical parametric amplifier (OPA) and two yttrium iron garnet spheres to facilitate magnon-photon coupling. Unlike the single transparency window typically resulting from magnon-photon interactions, we also observe magnomechanically induced transparency due to nonlinear magnon-phonon interactions. Additionally, two absorption dips on either side of the central absorption dip can be asymmetrically modulated into amplification and absorption by varying different system parameters. A PT-symmetric to broken-PT-symmetric phase transition is observed in both balanced and unbalanced gain-to-loss scenarios. Notably, replacing the second passive cavity with an active one mitigates high absorption and introduces effective gain into the system. Our findings reveal that the group delay of the probe light can be adjusted between positive and negative values by modifying various system parameters. This study provides a robust platform for controlling light propagation in CMM systems, highlighting potential applications in optical communication and signal processing.

physics.optics↗

arXiv2024

Enhanced second-order sideband generation and slow-fast light via coupled opto- and magnomechanical microspheres

In this research, we investigate second-order sideband generation (SSG) and slow-fast light using a hybrid system comprised of two coupled opto- and magnomechanical microspheres, namely a YIG sphere and a silica sphere. The YIG sphere hosts a magnon mode and a vibration mode induced by magnetostriction, whereas the silica sphere has an optical whispering gallery mode and a mechanical mode coupled via optomechanical interaction. The mechanical modes of both spheres are close in frequency and are coherently coupled by the straightway physical contact between the two microspheres. We use a perturbation approach to solve the Heisenberg-Langevin equations, offering an analytical framework for transmission rate and SSG. Using experimentally feasible settings, we demonstrate that the transmission rate and SSG are strongly dependent on the magnomechanical, optomechanical, and mechanics mechanics coupling strengths (MMCS) between the two microspheres. The numerical results show that increasing the MMCS can enhance both the transmission rate and SSG efficiency, resulting in gain within our system. Our findings, in particular, reveal that the efficiency of the SSG can be effectively controlled by cavity detuning, decay rate, and pump power. Notably, our findings suggest that modifying the system parameters can alter the group delay, thereby regulating the transition between fast and slow light propagation, and vice versa. Our protocol provides guidelines for manipulating nonlinear optical properties and controlling light propagation, with applications including optical switching, information storage, and precise measurement of weak signals.

physics.optics↗

arXiv2024

Sub-shot-noise sensitivity via superpositions of two deformed kitten states

In the present work we explore nonclassical effects in the phase space of two superposed kitten states induced by photon addition and subtraction operations applied in different sequences. We investigate two scenarios: In the first, photon addition is applied to the state, followed by photon subtraction, while in the second, the order of operations is reversed. We demonstrate that applying multiphoton operations to the state results in notable nearly isotropic sub-Planck structures, with the characteristics of these structures being influenced by the photon addition and subtraction. Increasing the number of added photons compresses the sub-Planck structures in both cases. Photon subtraction, however, has the opposite effect on the sub-Planck structures in the first case and no effect in the second. Furthermore, we observe that the optimal choices of multiphoton operations lead to improved isotropy of sub-Planck structures in our cases. The presence of the sub-Planck structures in our states leads to improved sensitivity to displacements, exceeding the standard quantum limit, as verified across all the depicted scenarios.

quant-ph↗

arXiv2024

Machine learning topological energy braiding of non-Bloch bands

Machine learning has been used to identify phase transitions in a variety of physical systems. However, there is still a lack of relevant research on non-Bloch energy braiding in non-Hermitian systems. In this work, we study non-Bloch energy braiding in one-dimensional non-Hermitian systems using unsupervised and supervised methods. In unsupervised learning, we use diffusion maps to successfully identify non-Bloch energy braiding without any prior knowledge and combine it with k-means to cluster different topological elements into clusters, such as Unlink and Hopf link. In supervised learning, we train a Convolutional Neural Network (CNN) based on Bloch energy data to predict not only Bloch energy braiding but also non-Bloch energy braiding with an accuracy approaching 100%. By analysing the CNN, we can ascertain that the network has successfully acquired the ability to recognise the braiding topology of the energy bands. The present study demonstrates the considerable potential of machine learning in the identification of non-Hermitian topological phases and energy braiding.

cond-mat.mes-hall↗

arXiv2024

Compasslike states in a thermal reservoir and fragility of their nonclassical features

Superposed photon-added and photon-subtracted squeezed-vacuum states exhibit sub-Planck phase-space structures and metrological potential similar to the original compass states (superposition of four coherent states), but are more closely tied to modern experiments. Here, we observe that these compasslike states are highly susceptible to loss of quantum coherence when placed in contact with a thermal reservoir; that is, the interaction with the thermal reservoir causes decoherence, which progressively suppresses the capacity of these states to exhibit interference traits. We focus on the sub-Planck structures of these states and find that decoherence effects on these features are stronger with increasing the average thermal photon number of the reservoir, the squeezing parameter, or the quantity of added (or subtracted) photons to the squeezed-vacuum states. Furthermore, we observe that the sub-Planck structures of the photon-subtracted case survive comparatively longer in the thermal reservoir than their counterparts in the photon-added case, and prolonged contact with the thermal reservoir converts these compasslike states into a classical state.

quant-ph↗

arXiv2024

Generative Design of Crystal Structures by Point Cloud Representations and Diffusion Model

Efficiently generating energetically stable crystal structures has long been a challenge in material design, primarily due to the immense arrangement of atoms in a crystal lattice. To facilitate the discovery of stable material, we present a framework for the generation of synthesizable materials, leveraging a point cloud representation to encode intricate structural information. At the heart of this framework lies the introduction of a diffusion model as its foundational pillar. To gauge the efficacy of our approach, we employ it to reconstruct input structures from our training datasets, rigorously validating its high reconstruction performance. Furthermore, we demonstrate the profound potential of Point Cloud-Based Crystal Diffusion (PCCD) by generating entirely new materials, emphasizing their synthesizability. Our research stands as a noteworthy contribution to the advancement of materials design and synthesis through the cutting-edge avenue of generative design instead of the conventional substitution or experience-based discovery.

cs.AI↗

arXiv2023

Non-Hermitian second-order topological superconductors

The topology of non-Hermitian systems is fundamentally changed by the non-Hermitian skin effect, which leads to the generalized bulk-boundary correspondence. Based on the non-Bloch band theory, we get insight into the interplay between the non-Hermiticity and the second-order topological superconductors in two spatial dimensions. We investigate that the non-Hermiticity drives both the bulk states and topological edge modes to accumulate toward opposite corners of the system depending on the particle and hole degrees of freedom protected by the particle-hole symmetry. Furthermore, the degeneracy of the Majorana corner modes can be broken in terms of both the eigenenergies and the eigenstates. Through an edge theory analysis, we elucidate the impact of non-Hermiticity and enable the extension of higher-order topological superconductors to the realm of non-Hermitian systems. We show that $Z_2$ skin effect and $Z_2$ skin-topological modes reveal the universal characteristics of non-Hermitian second-order topological superconductors and the generalized bulk-boundary correspondence is further enriched by the particle-hole symmetry.

cond-mat.supr-con↗

arXiv2023

Hyperbolic polaritons in topological nodal ring semimetals

In mirror-symmetric systems, there is a possibility of the realization of extended gapless electronic states characterized as nodal lines or rings. Strain induced modifications to these states lead to emergence of different classes of nodal rings with qualitatively different physical properties. Here we study optical response and the electromagnetic wave propagation in type I nodal ring semimetals, in which the low-energy quasiparticle dispersion is parabolic in momentum $k_x$ and $k_y$ and is linear in $k_z$. This leads to a highly anisotropic dielectric permittivity tensor in which the optical response is plasmonic in one spatial direction and dielectric in the other two directions. The resulting normal modes (polaritons) in the bulk material become hyperbolic over a broad frequency range, which is furthermore tunable by the doping level. The propagation, reflection, and polarization properties of the hyperbolic polaritons not only provide valuable information about the electronic structure of these fascinating materials in the most interesting region near the nodal rings but also pave the way to tunable hyperbolic materials with applications ranging from anomalous refraction and waveguiding to perfect absorption in ultrathin subwavelength films.

cond-mat.mes-hall↗

arXiv2022

Topological energy braiding of the non-Bloch bands

The non-Hermitian skin effect, as a unique feature of non-Hermitian systems, will break the topological energy braiding of the Bloch bands in open boundary systems. Going beyond the Bloch band theory, we unveil the energy braiding of the non-Bloch bands by introducing a one-dimensional non-Hermitian tight-binding model. We find an entirely new generic class of topological non-Bloch bands such as Hopf link, which is generally generated by the non-Hermitian skin effect. The energy braiding is topologically robust against any perturbations without gap closing. Furthermore, non-Bloch topological invariants are proposed based on the generalized Brillouin zone to characterize the topology of these non-Bloch bands. The topological phase transition between the distinct phases occurs with the non-Bloch bands touching at exceptional points. We hope that our work can shed light on the topological energy braiding of the non-Bloch bands for non-Hermitian systems.

cond-mat.mes-hall↗