arXiv subjects

Shuyi Li

Publications and source records attributed to Shuyi Li.

At least 19 recordsLinked to original sources

arXiv2026

Two-magnon response from light scattering in altermagnets

Altermagnetism is a recently established class of magnetic order that combines fully compensated moments with momentum-dependent spin splitting, yet identifying its spectroscopic fingerprints remains an open challenge. In this work, we investigate finite-momentum two-magnon excitations in a two-dimensional $d$-wave altermagnet by calculating the two-magnon light scattering intensity in different polarization channels. Using linear spin-wave theory and further incorporating the leading $1/S$ quantum corrections, we demonstrate that the characteristic magnon splitting of altermagnets shifts the upper edge of the two-magnon continuum and the corresponding spectral features to higher energy at $\boldsymbol{X}=(\pi,0)$ relative to the conventional antiferromagnet, by an amount linear in the exchange anisotropy $|\delta J_2|$, while leaving the response at the Brillouin-zone center unchanged. Although magnon-magnon interactions strongly redistribute spectral weight toward lower energies, the energy scale associated with the high-energy momentum-selective reconstruction remains robust. The interactions additionally generate a pronounced low-energy two-peak structure that has no counterpart within linear spin-wave theory. At $\boldsymbol{K}=(\pi/2,\pi/2)$, we show that the interacting two-magnon resonance is twofold degenerate in the conventional antiferromagnetic phase, while a finite altermagnetic exchange anisotropy lifts this degeneracy, producing two peaks whose separation is linear in $|\delta J_2|$. The characteristic energy scales underlying both features are intrinsic to the two-magnon sector rather than to a specific scattering operator. These findings highlight the potential of finite-momentum two-magnon spectroscopy for identifying altermagnetic order in insulating magnets and motivate momentum-resolved resonant inelastic x-ray scattering studies of candidate altermagnetic materials.

cond-mat.str-el↗

arXiv2026

An ultralow-loss integrated photonic platform for discrete-variable quantum information processing

Photonic integrated circuits offer a scalable and robust route toward quantum information technologies by consolidating photon sources and linear optical networks onto compact, wafer-manufacturable chips. Although silicon photonics has enabled diverse discrete-variable quantum breakthroughs -- spanning multiphoton entanglement, quantum networking, and photonic qubit fusion for quantum computing -- scaling these platforms beyond proof-of-principle demonstrations remains severely constrained by a critical system-level bottleneck. Optical loss compounds rapidly across photon generation, routing, and state analysis, causing multiphoton generation probabilities to plummet exponentially as circuit depth and complexity grow. Here we overcome this rate-loss barrier by demonstrating a monolithic, ultralow-loss silicon nitride (Si$_3$N$_4$) integrated photonic platform engineered for high-performance discrete-variable quantum information processing. Our architecture seamlessly integrates narrowband photon-pair sources with low-loss qubit-fusion circuits and reconfigurable state-analysis interferometers. The on-chip sources prepare Einstein-Podolsky-Rosen (EPR) states with a fidelity of 0.9875(3) and exhibit near-unity photon indistinguishability, yielding a heralded Hong-Ou-Mandel interference visibility of 0.990(6). By executing on-chip fusion of two EPR states, we synthesize and characterize four-photon Greenberger-Horne-Zeilinger states with a record fidelity of 0.943(8) and a fourfold count rate of 27 Hz -- more than two orders of magnitude higher than previous silicon-photonic implementations. Combined with standard CMOS-compatible fabrication on 150-mm-diameter wafers, these results establish ultralow-loss Si$_3$N$_4$ integrated photonics as a definitive, manufacturable platform for deployable, large-scale quantum information processors.

quant-ph↗

arXiv2026

Electronic and Magnonic Properties of $g$-Wave Altermagnetism in Intercalated Transition Metal Dichalcogenides

Altermagnetism is a recently identified class of magnetic order characterized by unconventional momentum-dependent spin splitting in the absence of net magnetization, and understanding its electronic and magnetic properties is essential for revealing its fundamental physics and potential applications. In this work we investigate two intercalated transition-metal dichalcogenides, Fe$_{1/4}$NbS$_2$ and V$_{1/3}$NbS$_2$, as candidate altermagnetic materials by using effective tight-binding and spin models complemented by first-principles calculations. We show that the $g$-wave electronic spin splitting originates from bond-dependent hopping anisotropy, leading to material-dependent nodal structures. For the magnetic excitations, the emergence of chiral splitting in the magnon dispersion is controlled by single-ion anisotropy, which manifests as altermagnetic-like nodal structures when spins are oriented along an easy-axis. Conversely, this altermagnetic signature disappears when the spins are aligned in an easy-plane. Beyond linear spin-wave theory, we find that $1/S$ corrections from magnon--magnon interactions preserve the symmetry and nodal structure of the band splitting while generally reducing its magnitude, with strong antiferromagnetic exchange leading to a non-negligible renormalization of the chiral splitting. Our findings establish intercalated transition-metal dichalcogenides as promising platforms for understanding the interplay between crystal symmetry, non-relativistic spin splitting, and magnetic properties in altermagnets.

cond-mat.str-el↗

arXiv2026

Field-Induced Selective Spin Gap Closure and Quantum Criticality in BaNd$_2$ZnS$_5$

We report thermodynamic evidence for field-induced mode-selective quantum criticality in the layered rare-earth magnet BaNd2ZnS5 (BNZS). Below the Neel temperature TN = 2.9 K, spin-orbit-entangled Nd3+ moments form two symmetry-inequivalent low-energy spin-excitation modes arising from Kramers doublet physics under a magnetic field, with distinct gaps Delta_L and Delta_H. For magnetic fields applied along the [110] direction, the lower-energy gap Delta_L softens continuously and collapses at a critical field Hc ~ 2 T, while the higher-energy gap Delta_H remains gapped, leaving the system in an intermediate partially critical phase. Despite the partial nature of the criticality, thermodynamic measurements reveal a continuous quantum phase transition. The ac susceptibility shows universal scaling behavior, with chi_ac(T, H) collapsing onto a single scaling function and following chi_ac ~ T^-0.2 at criticality. A finite residual Sommerfeld coefficient gamma_0 further indicates the emergence of gapless excitations confined to a single symmetry sector near the quantum critical point. In contrast to conventional quantum criticality based on global softening of low-energy excitations, BNZS exhibits a selective breakdown of Kramers-doublet excitations due to its strong anisotropic interactions. Our results establish BNZS as a spin-orbit-coupled rare-earth magnet where quantum criticality is not global but mode-selective, with anisotropic interactions enabling independent criticality in distinct excitation sectors.

cond-mat.str-el↗

arXiv2026

1-Mbps Twin-Field Quantum Key Distribution over 200 km Using Independent Dissipative Kerr Solitons

Twin-field quantum key distribution (TF-QKD) dramatically enhances the secure key rate (SKR) over inter-city distances through its square-root scaling. Further improvements in aggregate SKR can be achieved by wavelength-division multiplexing (WDM) of parallel QKD channels. However, direct implementation in TF-QKD poses significant challenges, as each wavelength channel requires an independent ultra-stable seed laser, narrow-linewidth transmitters, and optical phase-locked loops (OPLLs), which are not easily scalable. Here, we circumvent these limitations by employing two independent, integrated dissipative Kerr soliton (DKS) microcombs at Alice and Bob as multi-wavelength sources. High-visibility single-photon interference across all wavelength channels is achieved by stabilizing the frequencies of every comb line - requiring only the stabilization of the pump wavelength and repetition rates of the two microcombs. Based on this architecture, we perform a full TF-QKD experiment using the sending-or-not-sending protocol, achieving a total SKR of 1.57 Mbps over 201.1 km of fiber using 16 DWDM channels. This result represents more than an order-of-magnitude enhancement compared with single-wavelength TF-QKD at the same distance. Given that a single DKS comb can support over 100 coherent lines across the C-band, this approach offers a scalable pathway toward high-rate quantum key distribution over inter-city distances.

quant-ph↗

arXiv2026

Machine Learning Reconstruction of High-Dimensional Electronic Structure from Angle-Resolved Photoemission Spectroscopy

The emergent behavior of quantum materials is governed by their electronic structure, which can be experimentally probed by photoemission spectroscopy techniques that generate a four-dimensional dataset of energy and momentum. However, the quantitative extraction of Hamiltonian parameters from these high-dimensional spectra remains a significant challenge, currently relying on labor-intensive, expert-dependent analysis rather than standardized workflows. Here, we introduce a deep learning framework based on implicit neural representations to accelerate the retrieval of Hamiltonian parameters in two types of transition-metal oxides: perovskite nickelates and manganites. Our approach outperforms traditional analytical fitting procedures, yielding superior agreement with experimental Fermi surface topologies and energy-momentum dispersions. This work highlights the potential of deep learning tools to bridge the gap between theory and experiment, paving the way for high-throughput, autonomous discovery pipelines in quantum materials.

cond-mat.str-el↗

arXiv2026

Nonthermal Dynamics and Scar-Like Spectral Structures in a High-Spin Fermi Gas

We investigate nonequilibrium dynamics and weak ergodicity breaking in a harmonically trapped spin-$3/2$ Fermi gas by using the time-dependent Hartree-Fock equation. The Shannon entropy remains bounded and oscillatory throughout the evolution, indicating restricted and nonuniform exploration of Hilbert space rather than immediate thermalization. The fidelity exhibits pronounced, nearly periodic revivals whose period is largely insensitive to particle number and interaction strength, while the revival amplitude gradually decreases with increasing system size and interaction strength. The Fourier spectrum of the fidelity reveals a set of sharp and approximately equally spaced peaks. By projecting the time-evolved state onto the instantaneous eigenbasis of the self-consistent mean-field Hamiltonian, we identify a sparse and spectrally stable manifold that forms a quasi-regular energy ladder, with spacing comparable to the dominant quasienergy interval extracted from the fidelity spectrum. These results indicate that the long-lived coherent oscillations originate from collective phase interference associated with a quasi-regular spectral structure embedded in the many-body continuum, rather than from a conventional eigenstate-dominated scar mechanism.

cond-mat.quant-gas↗

arXiv2026

PalmBridge: A Plug-and-Play Feature Alignment Framework for Open-Set Palmprint Verification

Palmprint recognition is widely used in biometric systems, yet real-world performance often degrades due to feature distribution shifts caused by heterogeneous deployment conditions. Most deep palmprint models assume a closed and stationary distribution, leading to overfitting to dataset-specific textures rather than learning domain-invariant representations. Although data augmentation is commonly used to mitigate this issue, it assumes augmented samples can approximate the target deployment distribution, an assumption that often fails under significant domain mismatch. To address this limitation, we propose PalmBridge, a plug-and-play feature-space alignment framework for open-set palmprint verification based on vector quantization. Rather than relying solely on data-level augmentation, PalmBridge learns a compact set of representative vectors directly from training features. During enrollment and verification, each feature vector is mapped to its nearest representative vector under a minimum-distance criterion, and the mapped vector is then blended with the original vector. This design suppresses nuisance variation induced by domain shifts while retaining discriminative identity cues. The representative vectors are jointly optimized with the backbone network using task supervision, a feature-consistency objective, and an orthogonality regularization term to form a stable and well-structured shared embedding space. Furthermore, we analyze feature-to-representative mappings via assignment consistency and collision rate to assess model's sensitivity to blending weights. Experiments on multiple palmprint datasets and backbone architectures show that PalmBridge consistently reduces EER in intra-dataset open-set evaluation and improves cross-dataset generalization with negligible to modest runtime overhead.

cs.CV↗

arXiv2025

Palmprint De-Identification Using Diffusion Model for High-Quality and Diverse Synthesis

Palmprint recognition techniques have advanced significantly in recent years, enabling reliable recognition even when palmprints are captured in uncontrolled or challenging environments. However, this strength also introduces new risks, as publicly available palmprint images can be misused by adversaries for malicious activities. Despite this growing concern, research on methods to obscure or anonymize palmprints remains largely unexplored. Thus, it is essential to develop a palmprint de-identification technique capable of removing identity-revealing features while retaining the image's utility and preserving non-sensitive information. In this paper, we propose a training-free framework that utilizes pre-trained diffusion models to generate diverse, high-quality palmprint images that conceal identity features for de-identification purposes. To ensure greater stability and controllability in the synthesis process, we incorporate a semantic-guided embedding fusion alongside a prior interpolation mechanism. We further propose the de-identification ratio, a novel metric for intuitive de-identification assessment. Extensive experiments across multiple palmprint datasets and recognition methods demonstrate that our method effectively conceals identity-related traits with significant diversity across de-identified samples. The de-identified samples preserve high visual fidelity and maintain excellent usability, achieving a balance between de-identification and retaining non-identity information.

cs.CV↗

arXiv2025

Space compatibility of emerging, wide-bandgap, ultralow-loss integrated photonics

Integrated photonics has revolutionized optical communication, sensing, and computation, offering miniaturized and lightweight solutions for spacecraft with limited size and payload. Novel chip-scale instruments based on ultralow-loss integrated photonic platforms, including lasers, frequency combs and atomic traps, have been developed for space applications. Therefore, quantifying the space compatibility of ultralow-loss photonic integrated circuits (PICs), particularly their radiation resistance, is critical. This study experimentally evaluates the radiation resistance of ultralow-loss Si$_3$N$_4$, 4H-SiC, and LiNbO$_3$ PICs under intense $\gamma$-ray and high-energy proton irradiation. Results show that proton irradiation with $1.1 \times 10^{10}$ $\mathrm{p/cm^2}$ total flux does not significantly increase optical loss or alter the refractive index of these PICs, while $\gamma$-ray irradiation with 1.2 Mrad accumulated dose only marginally increases their optical loss. These findings provide preliminary evidence of the excellent space compatibility of ultralow-loss Si$_3$N$_4$, 4H-SiC, and LiNbO$_3$ PICs, highlighting their potential for compact and lightweight space systems.

physics.optics↗

arXiv2025

A chip-based optoelectronic-oscillator frequency comb

Microresonator-based Kerr frequency combs ("Kerr microcombs") constitute chip-scale frequency combs of broad spectral bandwidth and repetition rate ranging from gigahertz to terahertz. An appealing application exploiting microcombs' coherence and large repetition rate is microwave and millimeter-wave generation. Latest endeavor applying two-point optical frequency division (OFD) on photonic-chip-based microcombs has created microwaves with exceptionally low phase noise. Nevertheless, microcomb-based OFD still requires extensive active locking, additional lasers, and external RF or microwave sources, as well as sophisticated initiation. Here we demonstrate a simple and entirely passive (no active locking) architecture, which incorporates an optoelectronic oscillator (OEO) and symphonizes a coherent microcomb and a low-noise microwave spontaneously. Our OEO microcomb leverages state-of-the-art integrated chip devices including a high-power DFB laser, a broadband silicon Mach-Zehnder modulator, an ultralow-loss silicon nitride microresonator, and a high-speed photodetector. Each can be manufactured in large volume with low cost and high yield using established CMOS and III-V foundries. Our system synergizes a microcomb of 10.7 GHz repetition rate and an X-band microwave with phase noise of $-$97/$-$126/$-$130 dBc/Hz at 1/10/100 kHz Fourier frequency offset, yet does not demand active locking, additional lasers, and external RF or microwave sources. With potential to be fully integrated, our OEO microcomb can become an invaluable technology and building block for microwave photonics, radio-over-fiber, and optical communication.

physics.optics↗

arXiv2025

Perspective of high-speed Mach-Zehnder modulators based on nonlinear optics and complex band structures

Optical modulators are essential building blocks for high-capacity optical communication and massively parallel computing. Among all types of optical modulators, travelling-wave Mach-Zehnder modulators (TW-MZMs) featuring high speed and efficiency are widely used, and have been developed on a variety of integrated material platforms. Existing methods to design and simulate TW-MZMs so far strongly rely on the peculiar material properties, and thus inevitably involve complicated electrical-circuit models. As a result, these methods diverge significantly. In addition, they become increasingly inefficient and inaccurate for TW-MZMs with extending length and levitating modulation speed, posing formidable challenges for millimeter-wave and terahertz operation. Here, we present an innovative perspective to understand and analyze high-speed TW-MZMs. Our perspective leverages nonlinear optics and complex band structures of RF photonic crystals, and is thus entirely electromagnetic-wave-based. Under this perspective, we showcase the design, optoelectronic simulation and experimental validation of high-speed TW-MZMs based on Si and LiNbO$_3$, and further demonstrate unambiguous advantages in simplicity, accuracy and efficiency over conventional methods. Our approach can essentially be applied to nearly any integrated material platform, including those based on semiconductors and electro-absorption materials. With high-frequency electrode designs and optoelectronic co-simulation, our approach facilitates the synergy and convergence of electronics and photonics, and offers a viable route to constructing future high-speed millimeter-wave and terahertz photonics and quantum systems.

physics.optics↗

arXiv2024

Altermagnetism and Strain Induced Altermagnetic Transition in Cairo Pentagonal Monolayer

Altermagnetism, a recently discovered class of magnetic order characterized by vanishing net magnetization and spin-splitting band structures, has garnered significant research attention. In this work, we introduce a novel two-dimensional system that exhibits $g$-wave altermagnetism and undergoes a strain-induced transition from $g$-wave to $d$-wave altermagnetism. This system can be realized in an unconventional monolayer Cairo pentagonal lattice, for which we present a realistic tight-binding model that incorporates both magnetic and non-magnetic sites. Furthermore, we demonstrate that non-trivial band topology can emerge in this system by breaking the symmetry that protects the spin-polarized nodal points. Finally, \emph{ab initio} calculations on several candidate materials, such as FeS$_2$ and Nb$_2$FeB$_2$, which exhibit symmetry consistent with the proposed tight-binding Hamiltonian, are also presented. These findings open new avenues for exploring spintronic devices based on altermagnetic systems.

cond-mat.str-el↗

arXiv2024

Active Learning for Discovering Complex Phase Diagrams with Gaussian Processes

We introduce a Bayesian active learning algorithm that efficiently elucidates phase diagrams. Using a novel acquisition function that assesses both the impact and likelihood of the next observation, the algorithm iteratively determines the most informative next experiment to conduct and rapidly discerns the phase diagrams with multiple phases. Comparative studies against existing methods highlight the superior efficiency of our approach. We demonstrate the algorithm's practical application through the successful identification of the entire phase diagram of a spin Hamiltonian with antisymmetric interaction on Honeycomb lattice, using significantly fewer sample points than traditional grid search methods and a previous method based on support vector machines. Our algorithm identifies the phase diagram consisting of skyrmion, spiral and polarized phases with error less than 5% using only 8% of the total possible sample points, in both two-dimensional and three-dimensional phase spaces. Additionally, our method proves highly efficient in constructing three-dimensional phase diagrams, significantly reducing computational and experimental costs. Our methodological contributions extend to higher-dimensional phase diagrams with multiple phases, emphasizing the algorithm's effectiveness and versatility in handling complex, multi-phase systems in various dimensions.

physics.comp-ph↗

arXiv2024

Order by projection in single-band Hubbard model: a DMRG study

In a Fermi system near or at half-filling, a specific superconducting pairing channel, if not explicitly included in the Hamiltonian, can be boosted by suppressing a competing pairing channel; this is exemplified by the enhancement of extended $s$-wave correlations upon suppressing $s$-wave Cooper pairing. This phenomenon, originally found by the use of generalized uncertainty relations is referred to as \emph{order by projection}. The case of zero on-site Coulomb interaction in the thermodynamic limit, confirms this mechanism through the analytical solution. In this study, we go further and systematically investigate this mechanism for a strongly correlated fermionic Hubbard model, now with finite on-site interaction, on a square lattice with an extended set of hopping parameters. We explore the behaviors of different pairing channels when one of them is suppressed, utilizing density matrix renormalization group calculations. Our findings provide numerical evidence supporting the existence of \emph{order by projection} in the strongly correlated system we studied. We also investigate the effect of the strength of Hubbard $U$, next-nearest neighbor $t'$, hole-doping, as well as finite-size scaling approaching the thermodynamic limit.

cond-mat.str-el↗

arXiv2024

SememeLM: A Sememe Knowledge Enhanced Method for Long-tail Relation Representation

Recognizing relations between two words is a fundamental task with the broad applications. Different from extracting relations from text, it is difficult to identify relations among words without their contexts. Especially for long-tail relations, it becomes more difficult due to inadequate semantic features. Existing approaches based on language models (LMs) utilize rich knowledge of LMs to enhance the semantic features of relations. However, they capture uncommon relations while overlooking less frequent but meaningful ones since knowledge of LMs seriously relies on trained data where often represents common relations. On the other hand, long-tail relations are often uncommon in training data. It is interesting but not trivial to use external knowledge to enrich LMs due to collecting corpus containing long-tail relationships is hardly feasible. In this paper, we propose a sememe knowledge enhanced method (SememeLM) to enhance the representation of long-tail relations, in which sememes can break the contextual constraints between wors. Firstly, we present a sememe relation graph and propose a graph encoding method. Moreover, since external knowledge base possibly consisting of massive irrelevant knowledge, the noise is introduced. We propose a consistency alignment module, which aligns the introduced knowledge with LMs, reduces the noise and integrates the knowledge into the language model. Finally, we conducted experiments on word analogy datasets, which evaluates the ability to distinguish relation representations subtle differences, including long-tail relations. Extensive experiments show that our approach outperforms some state-of-the-art methods.

cs.CL↗

arXiv2024

Kitaev physics in the two-dimensional magnet NiPSe$_3$

The Kitaev interaction, found in candidate materials such as $\alpha$-RuCl$_3$, occurs through the metal ($M$)-ligand ($X$)-metal ($M$) paths of the edge-sharing octahedra because the large spin-orbit coupling (SOC) on the metal atoms activates directional spin interactions. Here, we show that even in $3d$ transition-metal compounds, where the SOC of the metal atom is negligible, heavy ligands can induce bond-dependent Kitaev interactions. In this work, we take as an example the $3d$ transition-metal chalcogenophosphate NiPSe$_3$ and show that the key is found in the presence of a sizable SOC on the Se $p$ orbital, one which mediates the super-exchange between the nearest-neighbor Ni sites. Our study provides a pathway for engineering enhanced Kitaev interactions through the interplay of SOC strength, lattice distortions, and chemical substitutions.

cond-mat.str-el↗

arXiv2023

Bayesian Learning via Q-Exponential Process

Regularization is one of the most fundamental topics in optimization, statistics and machine learning. To get sparsity in estimating a parameter $u\in\mathbb{R}^d$, an $\ell_q$ penalty term, $\Vert u\Vert_q$, is usually added to the objective function. What is the probabilistic distribution corresponding to such $\ell_q$ penalty? What is the correct stochastic process corresponding to $\Vert u\Vert_q$ when we model functions $u\in L^q$? This is important for statistically modeling large dimensional objects, e.g. images, with penalty to preserve certainty properties, e.g. edges in the image. In this work, we generalize the $q$-exponential distribution (with density proportional to) $\exp{(- \frac{1}{2}|u|^q)}$ to a stochastic process named $Q$-exponential (Q-EP) process that corresponds to the $L_q$ regularization of functions. The key step is to specify consistent multivariate $q$-exponential distributions by choosing from a large family of elliptic contour distributions. The work is closely related to Besov process which is usually defined by the expanded series. Q-EP can be regarded as a definition of Besov process with explicit probabilistic formulation and direct control on the correlation length. From the Bayesian perspective, Q-EP provides a flexible prior on functions with sharper penalty ($q<2$) than the commonly used Gaussian process (GP). We compare GP, Besov and Q-EP in modeling functional data, reconstructing images, and solving inverse problems and demonstrate the advantage of our proposed methodology.

stat.ME↗