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Xiaoqi Liu

Publications and source records attributed to Xiaoqi Liu.

At least 19 recordsLinked to original sources
arXiv2026

Nonreciprocal superconducting critical currents with normal state field trainability in kagome superconductor CsV3Sb5

Determining time-reversal symmetry (TRS) and chirality in the superconducting state and its relation to normal-state symmetry and topology are important issues in condensed matter physics. Here, we report nonreciprocal superconducting critical currents (Ic) at zero magnetic field in kagome superconductor CsV3Sb5 nanodevices: Ic differs for opposite directions, indicating spontaneous TRS and inversion symmetry breakings. The polarity of Ic asymmetry changes randomly in repeated thermal cycling to 300 K, consistent with spontaneous TRS breaking. Crucially, on applying a perpendicular magnetic field above the charge density wave (CDW) transition temperature and then removing it to zero above the superconducting onset temperature (Tc), the polarity of Ic asymmetry follows the field direction, ascertaining that the CDW state has a macroscopic and trainable TRS-breaking directionality. The symmetry breaking continues into the superconducting state and generates the nonreciprocal critical currents. These results provide evidence for the loop-current CDW normal state with TRS breaking in CsV3Sb5.

cond-mat.supr-con↗
arXiv2026

Cognitive Flexibility as a Latent Structural Operator for Bayesian State Estimation

Deep stochastic state-space models enable Bayesian filtering in nonlinear, partially observed systems but typically assume a fixed latent structure. When this assumption is violated, parameter adaptation alone may result in persistent belief inconsistency. We introduce \emph{Cognitive Flexibility} (CF) as a representation-level operator that selects latent structures online via an innovation-based predictive score, while preserving the Bayesian filtering recursion. Structural mismatch is formalized as irreducible predictive inconsistency under fixed structure. The resulting belief--structure recursion is shown to be well posed, to exhibit a structural descent property, and to admit finite switching, with reduction to standard Bayesian filtering under correct specification. Experiments on latent-dynamics mismatch, observation-structure shifts, and well-specified regimes confirm that CF improves predictive accuracy under a mismatch while remaining non-intrusive when the model is correctly specified.

eess.SY↗
arXiv2026

Demonstration of robust chiral edge transport in Chern insulator MnBi2Te4 devices with engineered geometric defects

Chiral edge states in Chern insulators are theoretically predicted to propagate unidirectionally along the sample boundary with inherent robustness against local perturbations, which manifests as the immunity to impurity-induced backscattering, a key factor for the development of robust, high-performance quantum devices. However, the direct experimental verification of the robustness of chiral edge states remains scarce. Here, we experimentally validate the robustness of the chiral edge states in MnBi2Te4 devices featuring engineered geometric defects introduced via atomic force microscope (AFM) nanomachining. Specifically, under a moderate perpendicular magnetic field, the MnBi2Te4 devices exhibit the Chern insulator state, characterized by a quantized Hall plateau and simultaneously vanishing longitudinal resistance. To verify the robustness of this topological state, we modify the device geometry by cutting a slit using AFM nanomachining that severs the original edge channel. Remarkably, the quantization behavior survives this drastic modification. The robust nature of the chiral edge transport is further confirmed by two-terminal, three-terminal and non-local measurements, fully demonstrating that the edge currents can bypass the artificial cut without dissipation. Our results unambiguously demonstrate the robustness of chiral edge states against geometric disruption and establish AFM nanomachining as a promising technique for topological quantum devices engineering.

cond-mat.mes-hall↗
arXiv2025

Nanbeige4-3B Technical Report: Exploring the Frontier of Small Language Models

We present Nanbeige4-3B, a family of small-scale but high-performing language models. Pretrained on 23T high-quality tokens and finetuned on over 30 million diverse instructions, we extend the boundary of the scaling law for small language models. In pre-training, we design a Fine-Grained Warmup-Stable-Decay (FG-WSD) training scheduler, which progressively refines data mixtures across stages to boost model performance. In post-training, to improve the quality of the SFT data, we design a joint mechanism that integrates deliberative generation refinement and chain-of-thought reconstruction, yielding substantial gains on complex tasks. Following SFT, we employ our flagship reasoning model to distill Nanbeige4-3B through our proposed Dual Preference Distillation (DPD) method, which leads to further performance gains. Finally, a multi-stage reinforcement learning phase was applied, leveraging verifiable rewards and preference modeling to strengthen abilities on both reasoning and human alignment. Extensive evaluations show that Nanbeige4-3B not only significantly outperforms models of comparable parameter scale but also rivals much larger models across a wide range of benchmarks. The model checkpoints are available at https://huggingface.co/Nanbeige.

cs.CL↗
arXiv2025

Non-stationary Bandit Convex Optimization: A Comprehensive Study

Bandit Convex Optimization is a fundamental class of sequential decision-making problems, where the learner selects actions from a continuous domain and observes a loss (but not its gradient) at only one point per round. We study this problem in non-stationary environments, and aim to minimize the regret under three standard measures of non-stationarity: the number of switches $S$ in the comparator sequence, the total variation $Δ$ of the loss functions, and the path-length $P$ of the comparator sequence. We propose a polynomial-time algorithm, Tilted Exponentially Weighted Average with Sleeping Experts (TEWA-SE), which adapts the sleeping experts framework from online convex optimization to the bandit setting. For strongly convex losses, we prove that TEWA-SE is minimax-optimal with respect to known $S$ and $Δ$ by establishing matching upper and lower bounds. By equipping TEWA-SE with the Bandit-over-Bandit framework, we extend our analysis to environments with unknown non-stationarity measures. For general convex losses, we introduce a second algorithm, clipped Exploration by Optimization (cExO), based on exponential weights over a discretized action space. While not polynomial-time computable, this method achieves minimax-optimal regret with respect to known $S$ and $Δ$, and improves on the best existing bounds with respect to $P$.

stat.ML↗
arXiv2025

Many-User Multiple Access with Random User Activity: Achievability Bounds and Efficient Schemes

We study the Gaussian multiple access channel with random user activity, in the regime where the number of users is proportional to the code length. The receiver may know some statistics about the number of active users, but does not know the exact number nor the identities of the active users. We derive two achievability bounds on the probabilities of missed detection, false alarm, and active user error, and propose an efficient CDMA-type scheme whose performance can be compared against these bounds. The first bound is a finite-length result based on Gaussian random codebooks and maximum-likelihood decoding. The second is an asymptotic bound, established using spatially coupled Gaussian codebooks and approximate message passing (AMP) decoding. These bounds can be used to compute an achievable tradeoff between the active user density and energy-per-bit, for a fixed user payload and target error rate. The efficient CDMA scheme uses a spatially coupled signature matrix and AMP decoding, and we give rigorous asymptotic guarantees on its error performance. Our analysis provides the first state evolution result for spatially coupled AMP with matrix-valued iterates, which may be of independent interest. Numerical experiments demonstrate the promising error performance of the CDMA scheme for both small and large user payloads, when compared with the two achievability bounds.

cs.IT↗
arXiv2025

Inferring Change Points in High-Dimensional Regression via Approximate Message Passing

We consider the problem of localizing change points in a generalized linear model (GLM), a model that covers many widely studied problems in statistical learning including linear, logistic, and rectified linear regression. We propose a novel and computationally efficient Approximate Message Passing (AMP) algorithm for estimating both the signals and the change point locations, and rigorously characterize its performance in the high-dimensional limit where the number of parameters $p$ is proportional to the number of samples $n$. This characterization is in terms of a state evolution recursion, which allows us to precisely compute performance measures such as the asymptotic Hausdorff error of our change point estimates, and allows us to tailor the algorithm to take advantage of any prior structural information on the signals and change points. Moreover, we show how our AMP iterates can be used to efficiently compute a Bayesian posterior distribution over the change point locations in the high-dimensional limit. We validate our theory via numerical experiments, and demonstrate the favorable performance of our estimators on both synthetic and real data in the settings of linear, logistic, and rectified linear regression.

stat.ML↗
arXiv2025

Existence and concentration of nontrivial solutions for quasilinear Schrödinger equation with indefinite potential

This paper is concerned with the quasilinear Schrödinger equation \begin{align*} -Δu+V(x)u+\frac{k}{2}Δ(u^2)u=f(u)\quad \text{in}~~\mathbb{R}^N\text{,} \end{align*} where $N\geq 3$, $k>0$, $V\in C(\R)$ is an indefinite potential. Under structural conditions on the potential $V$ and the nonlinearity $f$, we establish the existence of a nontrivial solution through a combination of a local linking argument, Morse theory, and the Moser iteration. Moreover, if $f$ is odd, we obtain an unbounded sequence of nontrivial solutions via the symmetric Mountain Pass Theorem. Additionally, as $k\rightarrow0$, we analyze the concentration behavior of nontrivial solutions.

math.AP↗
arXiv2025

Coded Many-User Multiple Access via Approximate Message Passing

We consider communication over the Gaussian multiple-access channel in the regime where the number of users grows linearly with the codelength. In this regime, schemes based on sparse superposition coding can achieve a near-optimal tradeoff between spectral efficiency and signal-to-noise ratio. However, these schemes are feasible only for small values of user payload. This paper investigates efficient schemes for larger user payloads, focusing on coded CDMA schemes where each user's information is encoded via a linear code before being modulated with a signature sequence. We propose an efficient approximate message passing (AMP) decoder that can be tailored to the structure of the linear code, and provide an exact asymptotic characterization of its performance. Based on this result, we consider a decoder that integrates AMP and belief propagation and characterize its tradeoff between spectral efficiency and signal-to-noise ratio, for a given target error rate. Simulation results show that the decoder achieves state-of-the-art performance at finite lengths, with a coded CDMA scheme defined using LDPC codes and a spatially coupled matrix of signature sequences.

cs.IT↗
arXiv2025

Variational quantum algorithm for the Poisson equation based on the banded Toeplitz systems

For solving the Poisson equation it is usually possible to discretize it into solving the corresponding linear system $Ax=b$.Variational quantum algorithms (VQAs) for the discreted Poisson equation have been studied before. We give a VQA based on the banded Toeplitz systems for solving the Poisson equation with respect to the structural features of matrix $A$. In detail, we decompose the matrix $A$ and $A^2$ into a linear combination of the corresponding banded Toeplitz matrix and sparse matrices with only a few non-zero elements. For the one-dimensional Poisson equation with different boundary conditions and the $d$-dimensional Poisson equation with Dirichlet boundary conditions, the number of decomposition terms is less than the work in [Phys. Rev. A 108, 032418 (2023)]. Based on the decomposition of the matrix, we design quantum circuits that evaluate efficiently the cost function.Additionally, numerical simulation verifies the feasibility of the proposed algorithm. In the end, the VQAs for linear systems of equations and matrix-vector multiplications with $K$-banded Teoplitz matrix $T_n^K$ are given, where $T_n^K\in R^{n\times n}$ and $K\in O({\rm ploy}\log n)$.

quant-ph↗
arXiv2024

Exploring Graphs with Distinct $M$-Eigenvalues: Product Operation, Wronskian Vertices, and Controllability

Let $\mathcal{G}^M$ denote the set of connected graphs with distinct $M$-eigenvalues. This paper explores the $M$-spectrum and eigenvectors of a new product $G\circ_C H$ of graphs $G$ and $H$. We present the necessary and sufficient condition for $G\circ_C H$ to have distinct $M$-eigenvalues. Specifically, for the rooted product $G\circ H$, we present a more concise and precise condition. A key concept, the $M$-Wronskian vertex, which plays a crucial role in determining graph properties related to separability and construction of specific graph families, is investigated. We propose a novel method for constructing infinite pairs of non-isomorphic $M$-cospectral graphs in $\mathcal{G}^M$ by leveraging the structural properties of the $M$-Wronskian vertex. Moreover, the necessary and sufficient condition for $G\circ H$ to be $M$-controllable is given.

math.CO↗
arXiv2023

Ferromagnetic and insulating behavior in both half magnetic levitation and non-levitation LK-99 like samples

Finding materials exhibiting superconductivity at room temperature has long been one of the ultimate goals in physics and material science. Recently, room-temperature superconducting properties have been claimed in a copper substituted lead phosphate apatite (Pb$_{10-x}$Cu$_x$(PO$_4$)$_6$O, or called LK-99) [1-3]. Using a similar approach, we have prepared LK-99 like samples and confirmed the half-levitation behaviors in some small specimens under the influence of a magnet at room temperature. To examine the magnetic properties of our samples, we have performed systematic magnetization measurements on the as-grown LK-99-like samples, including the half-levitated and non-levitated samples. The magnetization measurements show the coexistence of soft-ferromagnetic and diamagnetic signals in both half-levitated and non-levitated samples. The electrical transport measurements on the as-grown LK-99-like samples including both half-levitated and non-levitated samples show an insulating behavior characterized by the increasing resistivity with the decreasing temperature.

cond-mat.supr-con↗
arXiv2022

Sketching sparse low-rank matrices with near-optimal sample- and time-complexity using message passing

We consider the problem of recovering an $n_1 \times n_2$ low-rank matrix with $k$-sparse singular vectors from a small number of linear measurements (sketch). We propose a sketching scheme and an algorithm that can recover the singular vectors with high probability, with a sample complexity and running time that both depend only on $k$ and not on the ambient dimensions $n_1$ and $n_2$. Our sketching operator, based on a scheme for compressed sensing by Li et al. and Bakshi et al., uses a combination of a sparse parity check matrix and a partial DFT matrix. Our main contribution is the design and analysis of a two-stage iterative algorithm which recovers the singular vectors by exploiting the simultaneously sparse and low-rank structure of the matrix. We derive a nonasymptotic bound on the probability of exact recovery, which holds for any $n_1\times n_2 $ sparse, low-rank matrix. We also show how the scheme can be adapted to tackle matrices that are approximately sparse and low-rank. The theoretical results are validated by numerical simulations and comparisons with existing schemes that use convex programming for recovery.

cs.IT↗
arXiv2019

Learning Personalized Thermal Preferences via Bayesian Active Learning with Unimodality Constraints

Thermal preferences vary from person to person and may change over time. The main objective of this paper is to sequentially pose intelligent queries to occupants in order to optimally learn the indoor air temperature values which maximize their satisfaction. Our central hypothesis is that an occupant's preference relation over indoor air temperature can be described using a scalar function of these temperatures, which we call the "occupant's thermal utility function". Information about an occupant's preference over these temperatures is available to us through their response to thermal preference queries : "prefer warmer," "prefer cooler" and "satisfied" which we interpret as statements about the derivative of their utility function, i.e. the utility function is "increasing", "decreasing" and "constant" respectively. We model this hidden utility function using a Gaussian process prior with built-in unimodality constraint, i.e., the utility function has a unique maximum, and we train this model using Bayesian inference. This permits an expected improvement based selection of next preference query to pose to the occupant, which takes into account both exploration (sampling from areas of high uncertainty) and exploitation (sampling from areas which are likely to offer an improvement over current best observation). We use this framework to sequentially design experiments and illustrate its benefits by showing that it requires drastically fewer observations to learn the maximally preferred temperature values as compared to other methods. This framework is an important step towards the development of intelligent HVAC systems which would be able to respond to occupants' personalized thermal comfort needs. In order to encourage the use of our PE framework and ensure reproducibility in results, we publish an implementation of our work named GPPrefElicit as an open-source package in Python.

cs.LG↗
arXiv2012

Direct Observation of Giant Saturation Magnetization in Fe16N2

Magnetic materials with giant saturation magnetization have been a holy grail for magnetic researchers and condensed matter physicists for decades because of its great scientific and technological impacts. As described by the famous Slater-Pauling curve the material with highest Ms is the Fe65Co35 alloy. This was challenged in 1972 by a report on the compound Fe16N2 with Ms much higher than that of Fe65Co35. Following this claim, there have been enormous efforts to reproduce this result and to understand the magnetism of this compound. However, the reported Ms by different groups cover a broad range, mainly due to the unavailability of directly assessing Ms in Fe16N2. In this article, we report a direct observation of the giant saturation magnetization up to 2500 emu/cm3 using polarized neutron reflectometry (PNR) in epitaxial constrained Fe16N2 thin films prepared using a low-energy and surface-plasma-free sputtering process. The observed giant Ms is corroborated by a previously proposed Cluster + Atom model, the characteristic feature of which, namely, the directional charge transfer is evidenced by polarization-dependent x-ray absorption near edge spectroscopy (XANES).

cond-mat.mtrl-sci↗
arXiv2009

Heavy Fermion-like metal alpha double prime-Fe16N2 with giant saturation magnetization

A new model is proposed for the strong ferromagnetism associated with partially localized orbitals in the Fe16N2 metallic system which draws substantially from models of heavy fermion metals. We demonstrated that an unusual correlation effect is brought up within the Fe-N octahedral cluster region and the effective on-site 3d-3d Coulomb interaction increases due to a substantial 3d electrons charge density difference between the clusters and its surroundings, which leads to a partially localized high spin electron configuration with a long range ferromagnetic order. First principle calculation based on LDA+U method shows that giant magnetic moment can be achieved at sufficiently large Hubbard U value. The feature of the coexistence of the localized and itinerant electron states plays a key role on the formation of the giant saturation magnetization.

cond-mat.mtrl-sci↗
arXiv2009

Discovery of localized states of Fe 3d electrons in Fe16N2 and Fe8N films: an evidence of the existence of giant saturation magnetization

The mystery of giant saturation magnetization of Fe16N2 has remained for 37 years. In this letter, X-ray absorption spectroscopy (XAS) and magnetic circular dichroism (XMCD) are used to study the electron states of Fe atoms in Fe16N2 and Fe8N films. Localized Fe 3d electron states, which are not expected from current theories (models), are observed. The XMCD spectra and elemental hysteresis loops reveal the polarization of N atoms and the hybridization of N and Fe atoms. This discovery points to the origin of giant saturation magnetization in Fe16N2.

cond-mat.mtrl-sci↗