arXiv subjects

Wenjie Sun

Publications and source records attributed to Wenjie Sun.

At least 19 recordsLinked to original sources

arXiv2026

ARGON: A GNN-Empowered Compilation Framework for Scalable Neutral Atom Computing

Neutral atom quantum systems offer a promising pathway to large-scale quantum computing due to high qubit uniformity and flexible connectivity. To exploit this architecture, compilers must coordinate dynamic atom transport alongside highly parallel entangling gates. As circuits scale, the interplay between these operations becomes a system bottleneck, introducing denser logical interactions and longer temporal dependencies. Compilers must simultaneously satisfy rigid spatial constraints and complex movement schedules. Existing joint spatiotemporal compilation methods face an exponentially expanding search space, incurring substantial overheads or compromising fidelity as circuit size grows. In this work, we propose ARGON, a scalable compilation framework that introduces a spatiotemporal decoupling paradigm for neutral atom processors. Our key novelty is offloading static geometric conflict resolution to an offline phase, precomputing a library of hardware-certified, high-parallelism spatial layouts. To guide temporal routing, we deploy a Graph Neural Network (GNN) predictor to evaluate candidate layouts against deep temporal horizons, proactively evading downstream kinematic bottlenecks. Finally, a heuristic router translates the selected sequence into collision-free physical transport. Evaluations show ARGON completes compilation in under 10 seconds, delivering up to a >10^4x and 600x average speedup over state-of-the-art baselines. ARGON also minimizes routing decoherence and reduces Rydberg stages, improving execution fidelity by up to 10^2x on dense circuits.

cs.ET↗

arXiv2026

Steering Vision-Language Models with Joint Sparse Autoencoders

Sparse Autoencoders (SAEs) have shown promise for analyzing language models, but applying them to vision-language models (VLMs) often yields representations that are difficult to use as controllable cross-modal steering directions. We introduce the Joint Sparse Autoencoder (JSAE), which uses an explicit alignment constraint to jointly factorize sequence-pooled vision and language activations into shared, interpretable image/caption-level features. Applied to LLaVA, JSAE recovers cross-modal features for recognizable concepts (e.g., food and animals). Through bidirectional interventions (additive steering and suppression), we observe a layer-dependent asymmetry under our protocol: additive steering peaks at mid-to-late (pre-output) layers and weakens at both ends, whereas suppression scores remain within a comparable range across all probed layers within statistical noise. Experiments on three VLMs, namely LLaVA-v1.6-Mistral-7B, Llama3-LLaVA-8B, and the MoE-based Qwen3-VL-30B, show related layer-localized effects across architectures. Together, these results suggest that explicitly aligned sparse representations support more controllable intervention-based analysis of multimodal features, within an identifiable layer range, than the unconstrained alternatives tested here.

cs.CV↗

arXiv2026

Law of Neural Interaction: Depth-Width Shape, Interaction Efficiency, and Generalization

The guidance of scaling laws has increased the resource demands of modern large language models (LLMs), yet it remains questionable whether these models utilize resources effectively under a fixed budget. Previous research has proved superposition as a key contributor to loss. By leveraging the Neural Feature Ansatz, we extend superposition from parameter space to gradient space and define it as neural interaction. We find that under a fixed budget, good generalization is usually accompanied by efficient neural interactions, and the model can be placed in an efficient interaction interval by adjusting its depth-width ratio ($R_{D/W}$). In addition, as the budget scales up, the efficient interaction interval of the model remains relatively stable. By comparing existing small scale dense LLMs, we observe that models operating near this interval tend to perform better on the MMLU-Pro benchmark. Our findings reveal that the $R_{D/W}$ influences resource utilization efficiency and thereby affects generalization, providing insights into model shape initialization and the understanding of model generalization mechanisms. Code for Neural Interaction Law is available at: https://anonymous.4open.science/r/Neural_Interaction_Law-D788

cs.LG↗

arXiv2026

Superconducting Dome in $\mathrm{La}_{3-x}\mathrm{Sr}_{x}\mathrm{Ni}_{2}\mathrm{O}_{7-δ}$ Thin Films

The ambient-pressure superconductivity in $\mathrm{La}_{3}\mathrm{Ni}_{2}\mathrm{O}_{7}$ thin films via compressive epitaxial strain provides a highly accessible platform for diverse characterization techniques, facilitating the studies of high-temperature superconductivity. Here, we systematically map the phase diagram and reveal the superconducting dome with an electron-hole crossover in compressively strained $\mathrm{La}_{3-x}\mathrm{Sr}_{x}\mathrm{Ni}_{2}\mathrm{O}_{7-δ}$ thin films by simultaneously tuning Sr doping and oxygen content. The maximum transition temperature (${T}_{c}$) coincides with an anomalous sign change in the Hall coefficient (${R}_{H}$), reminiscent of electron-doped cuprates, which may signal a Fermi surface reconstruction. Beyond the superconducting dome, a $\ln1/T$ insulating regime and a $T$-linear resistivity regime are also resolved, resembling behaviors observed in cuprates and infinite-layer nickelates. This work reveals a dome-shaped relationship between ${T}_{c}$ and ${R}_{H}$ and establishes a key framework for understanding unconventional superconductivity in nickelate systems.

cond-mat.supr-con↗

arXiv2026

HeartLLM: Discretized ECG Tokenization for LLM-Based Diagnostic Reasoning

Electrocardiography (ECG) plays a central role in cardiovascular diagnostics, yet existing automated approaches often struggle to generalize across clinical tasks and offer limited support for open-ended reasoning. We present HeartLLM, a novel framework that integrates time-series (TS) and language modeling by enabling large language models (LLMs) to process 12-lead ECG signals for clinical text generation tasks. Our approach discretizes continuous ECG embeddings into quantized codes using a lead-wise encoder and quantization module. These quantized codes are then mapped to an extended ECG vocabulary to form ECG tokens, enabling the model to process both ECG and natural language inputs within a unified framework. To bridge the modality gap, we pretrain the model on an autoregressive ECG token forecasting task, allowing the LLM to capture temporal dynamics through its inherent language modeling capability. Finally, we perform instruction tuning on both ECG question answering and diagnostic report generation. Without modifying the core model, HeartLLM achieves strong performance across tasks while maintaining generalization to out-of-distribution settings. Extensive experiments demonstrate the effectiveness of each component and highlight the potential of integrating discretized ECG tokens into LLMs for medical reasoning.

cs.AI↗

arXiv2025

Observation of superconductivity-induced leading-edge gap in Sr-doped $\mathrm{La}_{3}\mathrm{Ni}_{2}\mathrm{O}_{7}$ thin films

The discovery of high-temperature superconductivity in pressurized bulk $\mathrm{La}_{3}\mathrm{Ni}_{2}\mathrm{O}_{7}$ has ignited significant interest in nickelate superconductors. Unlike cuprates, where superconductivity predominantly originates from the $\mathrm{3}d_{x^2-y^2}$ orbital, nickelates exhibit additional complexities involving contributions from the $\mathrm{3}d_{z^2}$ orbital, prompting fundamental questions about their pairing mechanisms. Despite recent progress in stabilizing superconductivity in $\mathrm{La}_{3}\mathrm{Ni}_{2}\mathrm{O}_{7}$ thin films at ambient pressure, direct spectroscopic evidence of the superconducting gap opening remains elusive. Here, we present an in-situ angle-resolved photoemission spectroscopy study of Sr-doped superconducting $\mathrm{La}_{3}\mathrm{Ni}_{2}\mathrm{O}_{7}$ thin films. Fermi surface mapping reveals Ni-$\mathrm{3}d_{x^2-y^2}$-derived $α$ and $β$ pockets, with orbital fillings of 0.11$\pm$0.02 electrons and 0.66$\pm$0.03 holes per Ni, respectively, resulting in a total of 0.45$\pm$0.04 electrons for each Ni. These bands exhibit moderate electron correlations, characterized by a band renormalization factor of 3-4. Notably, both $α$ and $β$ bands exhibit leading-edge shifts across the superconducting transition, with gap magnitude of ~1-2 meV at Fermi momenta along the Brillouin zone diagonal and slightly away from the zone diagonal, deviating from the conventional $d_{x^2-y^2}$-wave gap structure. Additionally, the Ni-$\mathrm{3}d_{z^2}$-derived $γ$ band lies ~75 meV below the Fermi level, indicating a $\mathrm{3}d_{x^2-y^2}$-dominated fermiology in this compound.

cond-mat.supr-con↗

arXiv2025

DYNAMO: Dynamic Neutral Atom Multi-programming Optimizer Towards Quantum Operating Systems

As quantum computing advances towards practical applications, quantum operating systems become inevitable, where multi-programming -- the core functionality of operating systems -- enables concurrent execution of multiple quantum programs to enhance hardware utilization. However, most quantum compilation work focuses solely on single-circuit execution, severely limiting resource efficiency and hindering quantum operating system development. We propose Dynamic Neutral Atom Multi-programming Optimizer (DYNAMO), a method that realizes multi-programming on neutral atom quantum architectures through parallel compilation and intelligent resource allocation across multiple quantum processing units (QPUs). DYNAMO addresses two critical challenges: inefficient and difficult resource partitioning, and complex scheduling conflicts from concurrent program. Our method enables efficient spatial and temporal resource sharing while maintaining circuit correctness and hardware constraints. Experimental evaluation across circuits ranging from 12 to over 1200 gates demonstrates that DYNAMO achieves up to 14.39x compilation speedup while reducing execution stages by an average of 50.47%. Furthermore, DYNAMO successfully distributes workloads across multiple QPUs with balanced resource utilization. By enabling efficient multi-programming capabilities, DYNAMO establishes a critical foundation towards realizing practical quantum operating systems.

quant-ph↗

arXiv2025

RFWNet: A Lightweight Remote Sensing Object Detector Integrating Multiscale Receptive Fields and Foreground Focus Mechanism

Challenges in remote sensing object detection(RSOD), such as high interclass similarity, imbalanced foreground-background distribution, and the small size of objects in remote sensing images, significantly hinder detection accuracy. Moreover, the tradeoff between model accuracy and computational complexity poses additional constraints on the application of RSOD algorithms. To address these issues, this study proposes an efficient and lightweight RSOD algorithm integrating multiscale receptive fields and foreground focus mechanism, named robust foreground weighted network(RFWNet). Specifically, we proposed a lightweight backbone network receptive field adaptive selection network (RFASNet), leveraging the rich context information of remote sensing images to enhance class separability. Additionally, we developed a foreground-background separation module(FBSM)consisting of a background redundant information filtering module (BRIFM) and a foreground information enhancement module (FIEM) to emphasize critical regions within images while filtering redundant background information. Finally, we designed a loss function, the weighted CIoU-Wasserstein loss (LWCW),which weights the IoU-based loss by using the normalized Wasserstein distance to mitigate model sensitivity to small object position deviations. The comprehensive experimental results demonstrate that RFWNet achieved 95.3% and 73.2% mean average precision (mAP) with 6.0 M parameters on the DOTA V1.0 and NWPU VHR-10 datasets, respectively, with an inference speed of 52 FPS.

cs.CV↗

arXiv2025

Sparsification and Reconstruction from the Perspective of Representation Geometry

Sparse Autoencoders (SAEs) have emerged as a predominant tool in mechanistic interpretability, aiming to identify interpretable monosemantic features. However, how does sparse encoding organize the representations of activation vector from language models? What is the relationship between this organizational paradigm and feature disentanglement as well as reconstruction performance? To address these questions, we propose the SAEMA, which validates the stratified structure of the representation by observing the variability of the rank of the symmetric semipositive definite (SSPD) matrix corresponding to the modal tensor unfolded along the latent tensor with the level of noise added to the residual stream. To systematically investigate how sparse encoding alters representational structures, we define local and global representations, demonstrating that they amplify inter-feature distinctions by merging similar semantic features and introducing additional dimensionality. Furthermore, we intervene the global representation from an optimization perspective, proving a significant causal relationship between their separability and the reconstruction performance. This study explains the principles of sparsity from the perspective of representational geometry and demonstrates the impact of changes in representational structure on reconstruction performance. Particularly emphasizes the necessity of understanding representations and incorporating representational constraints, providing empirical references for developing new interpretable tools and improving SAEs. The code is available at \hyperlink{https://github.com/wenjie1835/SAERepGeo}{https://github.com/wenjie1835/SAERepGeo}.

cs.LG↗

arXiv2025

Superconductivity and phase diagram in Sr-doped La$_{3-x}$Sr$_{x}$Ni$_2$O$_7$ thin films

Recent studies have demonstrated ambient pressure superconductivity in compressively strained La$_{3}$Ni$_{2}$O$_{7}$ thin films, yet the phase diagram of heterovalent doping$-$critical for advancing the field$-$remains unexplored. Here, we report superconductivity in Sr$^{2+}$-doped La$_{3-x}$Sr$_{x}$Ni$_2$O$_7$ films synthesized via molecular beam epitaxy with ozone-assisted post-annealing. The superconducting transition temperature ($T_{\mathrm{c}}$) follows an asymmetric dome-like profile, persisting across a wide doping range ($0 \leq x \leq 0.21$) before diminishing at $x \approx 0.38$. Optimally doped films ($x = 0.09$) achieve $T_{\mathrm{c}}$ of $\sim$ 42 K, with high critical current ($J_{\mathrm{c}} > 1.4$ $\mathrm{kA/cm^{2}}$ at 2 K) and upper critical fields ($μ_{0}H_{\mathrm{c,\parallel}}(0)= 83.7$ $\mathrm{T}$, $μ_{0}H_{\mathrm{c,\perp}}(0)= 110.3$ $\mathrm{T}$), comparable to reported La$_{3-x}$Pr$_{x}$Ni$_2$O$_7$ films. Scanning transmission electron microscopy reveals oxygen vacancies predominantly occupy at planar NiO$_{2}$ sites$-$unlike apical-site vacancies in bulk samples$-$due to Coulomb repulsion destabilizing planar oxygen under compressive strain. Additionally, the elongated out-of-plane Ni-O bonds, exceeding those in pressurized bulk samples by $4\%$, likely weaken the interlayer $d_{z^2}$ coupling, thus contributing to the reduced $T_{\mathrm{c}}$ in strained films. This work establishes heterovalent Sr$^{2+}$ doping as a robust tuning parameter for nickelate superconductivity, unveiling a unique phase diagram topology.

cond-mat.supr-con↗

arXiv2025

Physics-Aware Compilation for Parallel Quantum Circuit Execution on Neutral Atom Arrays

Neutral atom quantum computers are one of the most promising quantum architectures, offering advantages in scalability, dynamic reconfigurability, and potential for large-scale implementations. These characteristics create unique compilation challenges, especially regarding compilation efficiency while adapting to hardware flexibility. However, existing methods encounter significant performance bottlenecks at scale, hindering practical applications. We propose Physics-Aware Compilation (PAC), a method that improves compilation efficiency while preserving the inherent flexibility of neutral atom systems. PAC introduces physics-aware hardware plane partitioning that strategically allocates hardware resources based on physical device characteristics like AOD and SLM trap properties and qubit mobility constraints. Additionally, it implements parallel quantum circuit division with an improved Kernighan-Lin algorithm that enables simultaneous execution across independent regions while maintaining circuit fidelity. Our experimental evaluation compares PAC with state-of-the-art methods across increasingly larger array sizes ranging from 16x16 to 64x64 qubits. Results demonstrate that PAC achieves up to 78.5x speedup on 16x16 arrays while maintaining comparable circuit quality. PAC's compilation efficiency advantage increases with system scale, demonstrating scalability for practical quantum applications on larger arrays. PAC explores a viable path for practical applications of neutral atom quantum computers by effectively addressing the tension between compilation efficiency and hardware flexibility.

quant-ph↗

arXiv2025

HAQA: A Hardware-Guided and Fidelity-Aware Strategy for Efficient Qubit Mapping Optimization

Quantum algorithms rely on quantum computers for implementation, but the physical connectivity constraints of modern quantum processors impede the efficient realization of quantum algorithms. Qubit mapping, a critical technology for practical quantum computing applications, directly determines the execution efficiency and feasibility of algorithms on superconducting quantum processors. Existing mapping methods overlook intractable quantum hardware fidelity characteristics, reducing circuit execution quality. They also exhibit prolonged solving times or even failure to complete when handling large-scale quantum architectures, compromising efficiency. To address these challenges, we propose a novel qubit mapping method HAQA. HAQA first introduces a community-based iterative region identification strategy leveraging hardware connection topology, achieving effective dimensionality reduction of mapping space. This strategy avoids global search procedures, with complexity analysis demonstrating quadratic polynomial-level acceleration. Furthermore, HAQA implements a hardware-characteristic-based region evaluation mechanism, enabling quantitative selection of mapping regions based on fidelity metrics. This approach effectively integrates hardware fidelity information into the mapping process, enabling fidelity-aware qubit allocation. Experimental results demonstrate that HAQA significantly improves solving speed and fidelity while ensuring solution quality. When applied to state-of-the-art quantum mapping techniques Qsynth-v2 and TB-OLSQ2, HAQA achieves acceleration ratios of 632.76 and 286.87 respectively, while improving fidelity by up to 52.69% and 238.28%

quant-ph↗

arXiv2025

Superconductivity Favored Anisotropic Phase Stiffness in Infinite-Layer Nickelates

In unconventional superconductors such as cuprates and iron pnictides and chalcogenides, phase stiffness - a measure of the energy cost associated with superconducting phase variations - is on the same order of magnitude as the strength of Cooper pairing, translating to superconductivity governed by phase fluctuations. However, due to a lack of a direct experimental probe, there remains a fundamental gap in establishing microscopic picture between unconventional superconductivity and phase fluctuations. Here we show a vector current technique that allows for in-situ angle-resolved transport measurements, providing exclusive evidence suggesting an anisotropic nature of phase stiffness in infinite-layer nickelate superconductors. Pronounced anisotropy of in-plane resistance manifests itself in both normal and superconducting transition states, indicating crystal symmetry breaking. Remarkably, the electric conductivity of Nd0.8Sr0.2NiO2 peaks at 125° between the direction of the current and crystal principal axis, but this angle evolves to 160° near zero-resistance temperature. Further measurements reveal that the superconductivity is favored along a direction with minimized phase fluctuations, an orientation strikingly deviating from the symmetric direction imposed by both electronic anisotropy and the underlying crystal lattice. Identical measurements conducted on a prototypical cuprate superconductor yield consistent results, suggesting that this previously unknown behavior could be ubiquitous. By shielding insight into the contrasting anisotropy between electron fluid and superfluid, our findings provide clues for a unified framework for understanding unconventional superconductors

cond-mat.supr-con↗

arXiv2024

MLQM: Machine Learning Approach for Accelerating Optimal Qubit Mapping

Quantum circuit mapping is a critical process in quantum computing that involves adapting logical quantum circuits to adhere to hardware constraints, thereby generating physically executable quantum circuits. Current quantum circuit mapping techniques, such as solver-based methods, often encounter challenges related to slow solving speeds due to factors like redundant search iterations. Regarding this issue, we propose a machine learning approach for accelerating optimal qubit mapping (MLQM). First, the method proposes a global search space pruning scheme based on prior knowledge and machine learning, which in turn improves the solution efficiency. Second, to address the limited availability of effective samples in the learning task, MLQM introduces a novel data augmentation and refinement scheme, this scheme enhances the size and diversity of the quantum circuit dataset by exploiting gate allocation and qubit rearrangement. Finally, MLQM also further improves the solution efficiency by pruning the local search space, which is achieved through an adaptive dynamic adjustment mechanism of the solver variables. Compared to state-of-the-art qubit mapping approaches, MLQM achieves optimal qubit mapping with an average solving speed-up ratio of 1.79 and demonstrates an average advantage of 22% in terms of space complexity.

quant-ph↗

arXiv2024

Electronic Structure of Superconducting Infinite-Layer Lanthanum Nickelates

Revealing the momentum-resolved electronic structure of infinite-layer nickelates is essential for understanding this new class of unconventional superconductors, but has been hindered by the formidable challenges in improving the sample quality. In this work, we report for the first time the angle-resolved photoemission spectroscopy of superconducting La$_{0.8}$Sr$_{0.2}$NiO$_{2}$ films prepared by molecular beam epitaxy and ${\mathrm{\textit{in situ}}}$ atomic-hydrogen reduction. The measured Fermi topology closely matches theoretical calculations, showing a large Ni-$d_{x^2-y^2}$ derived Fermi sheet that evolves from hole-like to electron-like along $k_{z}$, and a three-dimensional (3D) electron pocket centered at Brillouin zone corner. The Ni-$d_{x^2-y^2}$ derived bands show a mass enhancement ($m^*/m_{\rm{DFT}}$) of 2-3,while the 3D electron band shows negligible band renormalization. Moreover, the Ni-$d_{x^2-y^2}$ derived states also display a band dispersion anomaly at higher binding energy, reminiscent of the waterfall feature and kinks observed in cuprates.

cond-mat.supr-con↗

arXiv2024

${\mathrm{\textit{In situ}}}$ preparation of superconducting infinite-layer nickelate thin films with atomically flat surface

Since their discovery, the infinite-layer nickelates have been regarded as an appealing system for gaining deeper insights into high temperature superconductivity (HTSC). However, the synthesis of superconducting samples has been proved to be challenging. Here, we develop an ultrahigh vacuum (UHV) ${\mathrm{\textit{in situ}}}$ reduction method using atomic hydrogen as reducing agent and apply it in lanthanum nickelate system. The reduction parameters, including the reduction temperature (${\mathrm{\textit{T}_{R}}}$) and hydrogen pressure (${\mathrm{\textit{P}_{H}}}$), are systematically explored. We found that the reduction window for achieving superconducting transition is quite wide, reaching nearly 80$^\circ$C in ${\mathrm{\textit{T}_{R}}}$ and 3 orders of magnitude in ${\mathrm{\textit{P}_{H}}}$ when the reduction time is set to 30 mins. And there exists an optimal ${\mathrm{\textit{P}_{H}}}$ for achieving the highest ${\mathrm{\textit{T}_{c}}}$ if both ${\mathrm{\textit{T}_{R}}}$ and reduction time are fixed. More prominently, as confirmed by atomic force microscopy and scanning transmission electron microscopy, the atomically flat surface can be preserved during the ${\mathrm{\textit{in situ}}}$ reduction process, providing advantages over the ${\mathrm{\textit{ex situ}}}$ CaH$_2$ method for surface-sensitive experiments.

cond-mat.supr-con↗

arXiv2024

Superconductivity in freestanding infinite-layer nickelate membranes

The observation of superconductivity in infinite-layer nickelates has attracted significant attention due to its potential as a new platform for exploring high $ \mathrm{\textit{T}}_{c} $ superconductivity. However, thus far, superconductivity has only been observed in epitaxial thin films, which limits the manipulation capabilities and modulation methods compared to two-dimensional exfoliated materials. Given the exceptionally giant strain tunability and stacking capability of freestanding membranes, separating superconducting nickelates from the as-grown substrate is a novel way to engineer the superconductivity and uncover the underlying physics. Herein, we report the synthesis of the superconducting freestanding $ \mathrm{La}_{0.8}\mathrm{Sr}_{0.2}\mathrm{Ni}\mathrm{O}_{2} $ membranes ($ \mathrm{\textit{T}}_{c}\mathrm{=}\mathrm{10.9}\;\mathrm{K} $), emphasizing the crucial roles of the interface engineering in the precursor phase film growth and the quick transfer process in achieving superconductivity. Our work offers a new versatile platform for investigating the superconductivity in nickelates, such as the pairing symmetry via constructing Josephson tunneling junctions and higher $ \mathrm{\textit{T}}_{c} $ values via high-pressure experiments.

cond-mat.supr-con↗

arXiv2023

Large upper critical fields and dimensionality crossover of superconductivity in infinite-layer nickelate La$_{0.8}$Sr$_{0.2}$NiO$_{2}$

The recently emerging superconductivity in infinite-layer nickelates, with isostructure and isoelectron of cuprates, provides a new platform to explore the pairing mechanism of high-temperature superconductors. In this work, we studied the upper critical field ($H_{\rm{c2}}$) of a high-quality La$_{0.8}$Sr$_{0.2}$NiO$_{2}$ thin film with superconducting transition temperature, $T_{\rm{c}}$ = 18.8 K, using high magnetic field up to 56 T. A very large $H_{\rm{c2}}$, $\sim$ 40 T for $H$ $\Arrowvert$ $c$ and $\sim$ 52 T for $H$ $\Arrowvert$ $ab$, was confirmed, which suggests that infinite-layer nickelates also have great application potential. The anisotropy of $H_{\rm{c2}}$ monotonically decreases from $\sim$ 10 near $T_{\rm{c}}$ to $\sim$ 1.5 at 2 K. Angle dependence of $H_{\rm{c2}}$ confirms the crossover of superconductivity from two-dimensional (2D) to three-dimensional (3D) as the temperature decreases. We discussed that the interstitial orbital effect causes the weakening of anisotropy. The observed abnormal upturning of $H_{\rm{c2}}$ at low temperatures is found to be a universal behavior independent of film quality and rare earth elements. Therefore, it should not be the Fulde-Ferrell-Larkin-Ovchinnikov (FFLO) state due to the fact that it is in the dirty limit and insensitive to disorder.

cond-mat.supr-con↗