arXiv subjects

Xiaohang Zhang

Publications and source records attributed to Xiaohang Zhang.

At least 19 recordsLinked to original sources

arXiv2026

SesQ: A Surface Electrostatic Simulator for Precise Energy Participation Ratio Simulation in Superconducting Qubits

An accurate and efficient numerical electromagnetic model for superconducting qubits is essential for characterizing and minimizing design-dependent dielectric losses. The energy participation ratio (EPR) is the commonly adopted metric used to evaluate these losses, but its calculation presents a severe multiscale computational challenge. Conventional finite element method (FEM) requires 3D volumetric meshing, leading to prohibitive computational costs and memory requirements when attempting to capture singular electric fields at nanometer-thin material interfaces. To address this bottleneck, we propose SesQ, a surface integral equation simulator tailored for the precise simulation of the EPR. By applying discretization on 2D surfaces, deriving a semi-analytical multilayer Green's function, and employing a dedicated non-conformal boundary mesh refinement scheme, SesQ accurately resolves singular edge fields without an explosive growth in the number of unknowns. Validations with analytically solvable models demonstrate that SesQ accelerates capacitance extraction by roughly two orders of magnitude compared to commercial FEM tools. While achieving comparable accuracy for capacitance extraction, SesQ delivers superior precision for EPR calculation. Simulations of practical transmon qubits further reveal that FEM approaches tend to significantly underestimate the EPR. Finally, the high efficiency of SesQ enables rapid iteration in the layout optimization, as demonstrated by minimizing the EPR of the qubit pattern, establishing the simulator as a powerful tool for the automated design of low-loss superconducting quantum circuits.

quant-ph↗

arXiv2026

Non-Hermitian Sensing via a Divergent Quantum Metric

The quantum metric, a geometric measure of state-space distance, has recently attracted growing attention for capturing anomalous state responses to parameter variations. Especially in non-Hermitian systems, the quantum metric has been observed to diverge when the eigenstates coalesce, a phenomenon identified as a remarkable resource for sensing. Here, by exploiting this divergence, we establish a non-Hermitian sensing scheme that leverages enhanced transient dynamics to provide a geometric gain for amplifying external field signals. We confirm the critical enhancement in the Fisher information using a trapped-ion 171Yb+ platform and demonstrate superior noise robustness over conventional eigenvalue-splitting--based non-Hermitian schemes by evaluating the minimum detectable signal. Moreover, this scheme can be naturally combined with non-Hermitian topological dynamics, revealing a unique unidirectional sensing response, which indicates its potential for directional signal discrimination. Our work establishes a new paradigm for sensing in open quantum systems through critical quantum geometry and opens a route toward robust topological quantum sensing.

quant-ph↗

arXiv2026

S$^3$-Attention:Attention-Aligned Endogenous Retrieval for Memory-Bounded Long-Context Inference

Large language models are increasingly applied to multi-document and long-form inputs, yet long-context inference remains memory- and noise-inefficient. Key-value (KV) caching scales linearly with context length, while external retrieval methods often return lexically similar but causally irrelevant passages. We present S3-Attention, a memory-first inference-time framework that treats long-context processing as attention-aligned endogenous retrieval. S3-Attention decodes transient key and query projections into top-k sparse feature identifiers using lightweight sparse autoencoders, and constructs a CPU-based inverted index mapping features to token positions or spans during a single streaming scan. This design allows the KV cache to be discarded entirely and bounds GPU memory usage by the scan chunk size. At generation time, feature co-activation is used to retrieve compact evidence spans, optionally fused with BM25 for exact lexical matching. Under a unified LongBench evaluation protocol with fixed prompting, decoding, and matched token budgets, S3-Hybrid closely matches full-context inference across multiple model families and improves robustness in several information-dense settings. We also report an engineering limitation of the current prototype, which incurs higher wall-clock latency than optimized full-KV baselines, motivating future kernel-level optimization.

cs.CL↗

arXiv2026

Slip viscosity and strain-rate viscosity in Taylor-Couette laminar flows: Experimental falsification and end-wall effects

The viscous force should be shear force, the difference between the strain-rate viscosity and the slip viscosity is that the former has conjugate shear force, while the latter does not. The study in this paper verifies the physical authenticity of two viscosity models through Taylor Couette laminar flow experiments with inner and outer cylinders rotating at the same angular velocity, and numerically investigate the influence of relative cylinder spacing and rotational speed on the circumferential velocity under the slip model. The experimental results of LDV measurement with a relative cylinder spacing of 0.3 indicate that the maximum deviation from rigid-body rotation is about 0.86%, which is consistent with the theoretical prediction of slip viscosity model. The numerical simulations show that the end-walls have no effect under the strain-rate viscosity model; but when the slip viscosity model is introduced, the end-walls inevitably bring about the circumferential velocity profile changing along the axial direction, and result in a three-dimensional (3D) spiral streamline pattern influenced by the relative cylinder spacing and angular speed of cylinders.

physics.flu-dyn↗

arXiv2025

Identify and Quantify Various Dissipation Mechanisms of Josephson Junction in Superconducting Circuits

Pinpointing the dissipation mechanisms and evaluating their impacts to the performance of Josephson junction (JJ) are crucial for its application in superconducting circuits. In this work, we demonstrate the junction-embedded resonator (JER) as a platform which enables us to identify and quantify various dissipation mechanisms of JJ. JER is constructed by embedding JJ in the middle of an open-circuit, 1/2 λ transmission-line resonator. When the 1st and 2nd harmonics of JER are excited, JJ experiences different boundary conditions, and is dominated by internal and external dissipations, respectively. We systematically study these 2 dissipation mechanisms of JJ by varying the JJ area and number. Our results unveil the completely different behaviors of these 2 dissipation mechanisms, and quantitatively characterize their contributions, shedding a light on the direction of JJ optimization in various applications.

quant-ph↗

arXiv2025

Causal Discovery from Data Assisted by Large Language Models

Knowledge driven discovery of novel materials necessitates the development of the causal models for the property emergence. While in classical physical paradigm the causal relationships are deduced based on the physical principles or via experiment, rapid accumulation of observational data necessitates learning causal relationships between dissimilar aspects of materials structure and functionalities based on observations. For this, it is essential to integrate experimental data with prior domain knowledge. Here we demonstrate this approach by combining high-resolution scanning transmission electron microscopy (STEM) data with insights derived from large language models (LLMs). By fine-tuning ChatGPT on domain-specific literature, such as arXiv papers on ferroelectrics, and combining obtained information with data-driven causal discovery, we construct adjacency matrices for Directed Acyclic Graphs (DAGs) that map the causal relationships between structural, chemical, and polarization degrees of freedom in Sm-doped BiFeO3 (SmBFO). This approach enables us to hypothesize how synthesis conditions influence material properties, particularly the coercive field (E0), and guides experimental validation. The ultimate objective of this work is to develop a unified framework that integrates LLM-driven literature analysis with data-driven discovery, facilitating the precise engineering of ferroelectric materials by establishing clear connections between synthesis conditions and their resulting material properties.

cond-mat.mtrl-sci↗

arXiv2025

Rapid analysis of point-contact Andreev reflection spectra via machine learning with adaptive data augmentation

Delineating the superconducting order parameters is a pivotal task in investigating superconductivity for probing pairing mechanisms, as well as their symmetry and topology. Point-contact Andreev reflection (PCAR) measurement is a simple yet powerful tool for identifying the order parameters. The PCAR spectra exhibit significant variations depending on the type of the order parameter in a superconductor, including its magnitude ($\mathitΔ$), as well as temperature, interfacial quality, Fermi velocity mismatch, and other factors. The information on the order parameter can be obtained by finding the combination of these parameters, generating a theoretical spectrum that fits a measured experimental spectrum. However, due to the complexity of the spectra and the high dimensionality of parameters, extracting the fitting parameters is often time-consuming and labor-intensive. In this study, we employ a convolutional neural network (CNN) algorithm to create models for rapid and automated analysis of PCAR spectra of various superconductors with different pairing symmetries (conventional $s$-wave, chiral $p_x+ip_y$-wave, and $d_{x^2-y^2}$-wave). The training datasets are generated based on the Blonder-Tinkham-Klapwijk (BTK) theory and further modified and augmented by selectively incorporating noise and peaks according to the bias voltages. This approach not only replicates the experimental spectra but also brings the model's attention to important features within the spectra. The optimized models provide fitting parameters for experimentally measured spectra in less than 100 ms per spectrum. Our approaches and findings pave the way for rapid and automated spectral analysis which will help accelerate research on superconductors with complex order parameters.

cond-mat.supr-con↗

arXiv2024

Reward driven workflows for unsupervised explainable analysis of phases and ferroic variants from atomically resolved imaging data

Rapid progress in aberration corrected electron microscopy necessitates development of robust methods for the identification of phases, ferroic variants, and other pertinent aspects of materials structure from imaging data. While unsupervised methods for clustering and classification are widely used for these tasks, their performance can be sensitive to hyperparameter selection in the analysis workflow. In this study, we explore the effects of descriptors and hyperparameters on the capability of unsupervised ML methods to distill local structural information, exemplified by discovery of polarization and lattice distortion in Sm doped BiFeO3 (BFO) thin films. We demonstrate that a reward-driven approach can be used to optimize these key hyperparameters across the full workflow, where rewards were designed to reflect domain wall continuity and straightness, ensuring that the analysis aligns with the material's physical behavior. This approach allows us to discover local descriptors that are best aligned with the specific physical behavior, providing insight into the fundamental physics of materials. We further extend the reward driven workflows to disentangle structural factors of variation via optimized variational autoencoder (VAE). Finally, the importance of well-defined rewards was explored as a quantifiable measure of success of the workflow.

cond-mat.mtrl-sci↗

arXiv2024

Superconducting phase diagram in Bi$_x$Ni$_{1-x}$ thin films$\colon$ the effects of Bi stoichiometry on superconductivity

The Bi${-}$Ni binary system has been of interest due to possible unconventional superconductivity aroused therein, such as time-reversal symmetry breaking in Bi/Ni bilayers or the coexistence of superconductivity and ferromagnetism in Bi$_3$Ni crystals. While Ni acts as a ferromagnetic element in such systems, the role of strong spin-orbit-coupling element Bi in superconductivity has remained unexplored. In this work, we systematically studied the effects of Bi stoichiometry on the superconductivity of Bi$_x$Ni$_{1-x}$ thin films (${x} \approx$ 0.5 to 0.9) fabricated via a composition-spread approach. The superconducting phase map of Bi$_x$Ni$_{1-x}$ thin films exhibited a superconducting composition region attributable to the intermetallic Bi$_3$Ni phase with different amount of excess Bi, revealed by synchrotron X-ray diffraction analysis. Interestingly, the mixed phase region with Bi$_3$Ni and Bi showed unusual increases in the superconducting transition temperature and residual resistance ratio as more Bi impurities were included, with the maximum ${T}_{c}$ ($=$ 4.2 K) observed at $x \approx$ 0.79. A correlation analysis of structural, electrical, and magneto-transport characteristics across the composition variation revealed that the unusual superconducting $"$dome$"$ is due to two competing roles of Bi$\colon$ impurity scattering and carrier doping. We found that the carrier doping effect is dominant in the mild doping regime (0.74 $\leq {x} \leq$ 0.79), while impurity scattering becomes more pronounced at larger Bi stoichiometry.

cond-mat.supr-con↗

arXiv2024

Co-orchestration of Multiple Instruments to Uncover Structure-Property Relationships in Combinatorial Libraries

The rapid growth of automated and autonomous instrumentations brings forth an opportunity for the co-orchestration of multimodal tools, equipped with multiple sequential detection methods, or several characterization tools to explore identical samples. This can be exemplified by the combinatorial libraries that can be explored in multiple locations by multiple tools simultaneously, or downstream characterization in automated synthesis systems. In the co-orchestration approaches, information gained in one modality should accelerate the discovery of other modalities. Correspondingly, the orchestrating agent should select the measurement modality based on the anticipated knowledge gain and measurement cost. Here, we propose and implement a co-orchestration approach for conducting measurements with complex observables such as spectra or images. The method relies on combining dimensionality reduction by variational autoencoders with representation learning for control over the latent space structure, and integrated into iterative workflow via multi-task Gaussian Processes (GP). This approach further allows for the native incorporation of the system's physics via a probabilistic model as a mean function of the GP. We illustrated this method for different modalities of piezoresponse force microscopy and micro-Raman on combinatorial $Sm-BiFeO_3$ library. However, the proposed framework is general and can be extended to multiple measurement modalities and arbitrary dimensionality of measured signals. The analysis code that supports the funding is publicly available at https://github.com/Slautin/2024_Co-orchestration.

cond-mat.mtrl-sci↗

arXiv2023

Newton saw the truth -- on the nature of fluid flow and viscous interaction

The viscous interaction of fluid is understood as the response to deformation, which is proportional to the strain rate. This model has gradually become the standard since Stokes, and has become the basis of the classical flow theory, namely the Navier-Stokes (N-S) equations. However, it has never been accurately verified in the curved laminar flow. Here, a distinctive unambiguous simple experiment is designed to falsify the viscosity model of deformation, and instead a new model is proposed, that is, the viscous friction originates from the slip of fluid layering at molecular scale. Though Newton contributed the initial idea of slip viscosity, the new model cannot be formulated without the help of modern differential geometry. From the new model, the analytical solution of laminar Taylor-Couette (T-C) flow between two concentric cylinders can reproduce the result of the ideal experiment proposed by Newton as the outer cylinder being infinite, which was once considered a mistake of Newton. A significant difference with the solution of the N-S equations when the outer cylinder is relatively large can be used to distinguish the viscosity models, even for the simplest case with both cylinders rotating with the same angular velocity. The accurate measurement data by the LDA support the slip model, and the consequent flow theory inevitably leads to a new vision in turbulence research.

physics.flu-dyn↗

arXiv2022

Unsupervised learning of ferroic variants from atomically resolved STEM images

An approach for the analysis of atomically resolved scanning transmission electron microscopy data with multiple ferroic variants in the presence of imaging non-idealities and chemical variabilities based on a rotationally invariant variational autoencoder (rVAE) is presented. We show that an optimal local descriptor for the analysis is a sub-image centered at specific atomic units, since materials and microscope distortions preclude the use of an ideal lattice as a reference point. The applicability of unsupervised clustering and dimensionality reduction methods is explored and are shown to produce clusters dominated by chemical and microscope effects, with a large number of classes required to establish the presence of rotational variants. Comparatively, the rVAE allows extraction of the angle corresponding to the orientation of ferroic variants explicitly, enabling straightforward identification of the ferroic variants as regions with constant or smoothly changing latent variables and sharp orientational changes. This approach allows further exploration of the chemical variability by separating the rotational degrees of freedom via rVAE and searching for remaining variability in the system. The code used in the manuscript is available at https://github.com/saimani5/ferroelectric_domains_rVAE.

cond-mat.mtrl-sci↗

arXiv2022

Titanium Nitride Film on Sapphire Substrate with Low Dielectric Loss for Superconducting Qubits

Dielectric loss is one of the major decoherence sources of superconducting qubits. Contemporary high-coherence superconducting qubits are formed by material systems mostly consisting of superconducting films on substrate with low dielectric loss, where the loss mainly originates from the surfaces and interfaces. Among the multiple candidates for material systems, a combination of titanium nitride (TiN) film and sapphire substrate has good potential because of its chemical stability against oxidization, and high quality at interfaces. In this work, we report a TiN film deposited onto sapphire substrate achieving low dielectric loss at the material interface. Through the systematic characterizations of a series of transmon qubits fabricated with identical batches of TiN base layers, but different geometries of qubit shunting capacitors with various participation ratios of the material interface, we quantitatively extract the loss tangent value at the substrate-metal interface smaller than $8.9 \times 10^{-4}$ in 1-nm disordered layer. By optimizing the interface participation ratio of the transmon qubit, we reproducibly achieve qubit lifetimes of up to 300 $μ$s and quality factors approaching 8 million. We demonstrate that TiN film on sapphire substrate is an ideal material system for high-coherence superconducting qubits. Our analyses further suggest that the interface dielectric loss around the Josephson junction part of the circuit could be the dominant limitation of lifetimes for state-of-the-art transmon qubits.

quant-ph↗

arXiv2022

Chiral Spin Bobbers in Exchange-Coupled Hard-Soft Magnetic Bilayers

The spin structure of exchange-coupled MnBi:Co-Fe bilayers is investigated by X-ray magnetic circular dichroism (XMCD), polarized neutron reflectometry (PNR), and micromagnetic simu-lations. The purpose of the present research is two-fold. First, the current search for new permanent-magnet materials includes hard-soft nanocomposites, and the analysis of coercivity mechanisms in these structures is an important aspect of this quest. Second, topological micro-magnetic structures such as skyrmions have recently become of intense fundamental and applied research, for example in the context of spin-based electronics. We find that the magnetization reversal of the MnBi:Co-Fe bilayer structure involves a curling-type twisting of the magnetization in the film plane. This curling in the exchange-coupled hard-soft magnetic bilayers is reminiscent of chiral spin structures known as bobbers and, in fact, establishes a new type of skyrmionic spin structure.

cond-mat.mtrl-sci↗

arXiv2022

Hypothesis Learning in Automated Experiment: Application to Combinatorial Materials Libraries

Machine learning is rapidly becoming an integral part of experimental physical discovery via automated and high-throughput synthesis, and active experiments in scattering and electron/probe microscopy. This, in turn, necessitates the development of active learning methods capable of exploring relevant parameter spaces with the smallest number of steps. Here we introduce an active learning approach based on co-navigation of the hypothesis and experimental spaces. This is realized by combining the structured Gaussian Processes containing probabilistic models of the possible system's behaviors (hypotheses) with reinforcement learning policy refinement (discovery). This approach closely resembles classical human-driven physical discovery, when several alternative hypotheses realized via models with adjustable parameters are tested during an experiment. We demonstrate this approach for exploring concentration-induced phase transitions in combinatorial libraries of Sm-doped BiFeO3 using Piezoresponse Force Microscopy, but it is straightforward to extend it to higher-dimensional parameter spaces and more complex physical problems once the experimental workflow and hypothesis-generation are available.

cond-mat.mtrl-sci↗

arXiv2021

Fluxonium: an alternative qubit platform for high-fidelity operations

Superconducting qubits provide a promising path toward building large-scale quantum computers. The simple and robust transmon qubit has been the leading platform, achieving multiple milestones. However, fault-tolerant quantum computing calls for qubit operations at error rates significantly lower than those exhibited in the state of the art. Consequently, alternative superconducting qubits with better error protection have attracted increasing interest. Among them, fluxonium is a particularly promising candidate, featuring large anharmonicity and long coherence times. Here, we engineer a fluxonium-based quantum processor that integrates high qubit-coherence, fast frequency-tunability, and individual-qubit addressability for reset, readout, and gates. With simple and fast gate schemes, we achieve an average single-qubit gate fidelity of 99.97% and a two-qubit gate fidelity of up to 99.72%. This performance is comparable to the highest values reported in the literature of superconducting circuits. Thus our work, for the first time within the realm of superconducting qubits, reveals an approach toward fault-tolerant quantum computing that is alternative and competitive to the transmon system.

quant-ph↗

arXiv2021

Mapping causal patterns in crystalline solids

The evolution of the atomic structures of the combinatorial library of Sm-substituted thin film BiFeO3 along the phase transition boundary from the ferroelectric rhombohedral phase to the non-ferroelectric orthorhombic phase is explored using scanning transmission electron microscopy (STEM). Localized properties including polarization, lattice parameter, and chemical composition are parameterized from atomic-scale imaging and their causal relationships are reconstructed using a linear non-Gaussian acyclic model (LiNGAM). This approach is further extended toward exploring the spatial variability of the causal coupling using the sliding window transform method, which revealed that new causal relationships emerged both at the expected locations, such as domain walls and interfaces, but also at additional regions forming clusters in the vicinity of the walls or spatially distributed features. While the exact physical origins of these relationships are unclear, they likely represent nanophase separated regions in the morphotropic phase boundaries. Overall, we pose that an in-depth understanding of complex disordered materials away from thermodynamic equilibrium necessitates understanding not only of the generative processes that can lead to observed microscopic states, but also the causal links between multiple interacting subsystems.

physics.data-an↗

arXiv2021

Deep learning polarization distributions in ferroelectrics from STEM data: with and without atom finding

Over the last decade, scanning transmission electron microscopy (STEM) has emerged as a powerful tool for probing atomic structures of complex materials with picometer precision, opening the pathway toward exploring ferroelectric, ferroelastic, and chemical phenomena on the atomic-scale. Analyses to date extracting a polarization signal from lattice coupled distortions in STEM imaging rely on discovery of atomic positions from intensity maxima/minima and subsequent calculation of polarization and other order parameter fields from the atomic displacements. Here, we explore the feasibility of polarization mapping directly from the analysis of STEM images using deep convolutional neural networks (DCNNs). In this approach, the DCNN is trained on the labeled part of the image (i.e., for human labelling), and the trained network is subsequently applied to other images. We explore the effects of the choice of the descriptors (centered on atomic columns and grid-based), the effects of observational bias, and whether the network trained on one composition can be applied to a different one. This analysis demonstrates the tremendous potential of the DCNN for the analysis of high-resolution STEM imaging and spectral data and highlights the associated limitations.

physics.data-an↗