arXiv subjects

Jiankun Li

Publications and source records attributed to Jiankun Li.

13 recordsLinked to original sources

arXiv2026

High-Fidelity Hole Spin Qubits Reveal Quadrupolar Nuclear-Bath Dynamics in Isotopically Purified Planar Germanium

Planar Germanium has emerged as a promising platform to build spin-based large scale quantum computers. By exploiting the anisotropic hyperfine interaction of holes in Ge, qubits with long T2* have been recently realized. While the performance of single qubits is still more or less limited by 73Ge nuclear spin fluctuations, the site-to-site variation of qubit sweet spot becomes obstacles to maintaining high fidelity of each qubit across the whole wafer. To achieve high performance Ge-based quantum circuit, it is therefore essential to eliminate the origin source of hyperfine noise. In its Silicon counterparts, reduction of 29Si abundance enables exceptional high-fidelity operation. In contrast, hole qubits based on isotopically purified Ge have not been demonstrated. Here, we report the synthesis of high quality 2-dimensional hole gas (2DHG) with enriched 70GeH4 precursor. Due to the suppression of nonzero spin nucleus, the qubits' T2* on the sweet spot is moderately extended beyond 20 us, surpassing the previous best reported Ge hole qubits. More importantly, the qubits' T2* off the sweet spot is enhanced to above 3 us, enabling single qubit gate fidelity exceeding 99.9% in both operating regimes. Hahn-echo spectroscopy further resolves a finite-frequency nuclear-noise channel that is distinct from the conventional Larmor-linked hyperfine response. We associate this channel with quadrupole-modified dynamics of residual 73Ge nuclei sampling local electric-field gradients near the Ge/SiGe interface. Its field scaling and angle-dependent visibility are consistent with a qubit-visible quadrupolar nuclear-noise component transduced through the anisotropic hyperfine interaction of Ge holes. These results establish isotopically purified planar Ge as a high-coherence scalable platform for hole spin qubits and provide a spectroscopic probe of interfacial quadrupolar nuclear dynamics.

quant-ph↗

arXiv2026

Quantum geometry induced anomalous chiral transport and hidden symmetry breaking in centrosymmetric 2M-WS2

Chirality, a widely existing material property in nature involving the breaking of the left-right symmetry, has profound influences in various fields of natural sciences. Nonlinear response, such as electronic magnetochiral anisotropy (eMChA), has been recognized as a sensitive probe for the effects of symmetry breaking and nontrivial quantum geometries in solids. So far, observations of eMChA have primarily been limited to inversion-symmetry broken materials. Here, we report a remarkable chiral transport in centrosymmetric candidate topological superconductor 2M-WS2 flakes observed via second-harmonic generation under an out-of-plane magnetic field. More importantly, the eMChA becomes significant around the crossover temperature TFL ~ 25 K from the Fermi liquid (FL) to strange metal (SM) in the normal state, which interestingly echoes with the anomalously large Nernst response at the same temperature in bulk 2M-WS2. These observations reveal a direct correspondence between the nonlinear response, Nernst response, and FL-SM transition in 2M-WS2. Theoretical analysis indicates that nontrivial quantum geometry is behind the simultaneous response of eMChA and Nernst effects in 2M-WS2 and the contribution from the orbital magnetic moment at the Fermi surface becomes significant during the FL-SM transition. Based on first-principles calculations, a thick-layer-sliding mechanism with minimal energy gain in 2M-WS2 provides one possibility for the generation of such nontrivial quantum geometry. The intertwined physics of remarkable eMChA, Nernst response, and FL-SM transition make 2M-WS2 a rare quantum platform to study the chiral transport and unexplored phenomena in strange metals, which may shed light on the trans-century, unresolved scientific issue in unconventional high-temperature superconductivity.

cond-mat.str-el↗

arXiv2025

Entanglement entropy between tangent balls in CFT$_D$

We apply the universal method developed in \cite{Jiang:2025jnk} to compute the entanglement entropy between two tangent balls in CFT$_D$. When taking the radius of one ball to infinity, it gives the entanglement entropy between a ball and its tangent half plane. In two-dimensional case, this configuration is equivalent to the entanglement in boundary conformal field theory (BCFT) between the negative half-axis and an interval ending on the boundary.

hep-th↗

arXiv2025

Tunable Wigner Molecules in a Germanium Quantum Dot

The interplay between Coulomb interactions and kinetic energy underlies many exotic phases in condensed matter physics. In a two-dimensional electronic system, If Coulomb interaction dominates over kinetic energy, electrons condense into a crystalline phase which is referred as Wigner crystal. This ordered state manifests as Wigner molecule for few electrons at the microscopic scale. Observation of Wigner molecules has been reported in quantum dot and moire superlattice systems. Here we demonstrate hole Wigner molecules can be formed in a gate-defined germanium quantum dot with high tunability. By varying voltages applied to the quantum dot device, we can precisely tune the hole density by either changing the hole occupancy or the quantum dot size. For densities smaller than a certain critical value, Coulomb interaction localizes individual holes into ordered lattice sites, forming a Wigner molecule. By increasing the densities, melting process from a Wigner molecule to Fermi liquid-like particles is observed. An intermediate configuration which indicates the coexistence of ordered structure and disordered structure can be formed within a narrow effective density range. Our results provide a new platform for further exploration of the microscopic feature of strong correlated physics and open an avenue to exploit the application of Wigner molecules for quantum information in a very promising spin qubit platform.

cond-mat.mes-hall↗

arXiv2024

ADRS-CNet: An adaptive dimensionality reduction selection and classification network for DNA storage clustering algorithms

DNA storage technology offers new possibilities for addressing massive data storage due to its high storage density, long-term preservation, low maintenance cost, and compact size. To improve the reliability of stored information, base errors and missing storage sequences are challenges that must be faced. Currently, clustering and comparison of sequenced sequences are employed to recover the original sequence information as much as possible. Nonetheless, extracting DNA sequences of different lengths as features leads to the curse of dimensionality, which needs to be overcome. To address this, techniques like PCA, UMAP, and t-SNE are commonly employed to project high-dimensional features into low-dimensional space. Considering that these methods exhibit varying effectiveness in dimensionality reduction when dealing with different datasets, this paper proposes training a multilayer perceptron model to classify input DNA sequence features and adaptively select the most suitable dimensionality reduction method to enhance subsequent clustering results. Through testing on open-source datasets and comparing our approach with various baseline methods, experimental results demonstrate that our model exhibits superior classification performance and significantly improves clustering outcomes. This displays that our approach effectively mitigates the impact of the curse of dimensionality on clustering models.

cs.LG↗

arXiv2024

Exploring the Causality of End-to-End Autonomous Driving

Deep learning-based models are widely deployed in autonomous driving areas, especially the increasingly noticed end-to-end solutions. However, the black-box property of these models raises concerns about their trustworthiness and safety for autonomous driving, and how to debug the causality has become a pressing concern. Despite some existing research on the explainability of autonomous driving, there is currently no systematic solution to help researchers debug and identify the key factors that lead to the final predicted action of end-to-end autonomous driving. In this work, we propose a comprehensive approach to explore and analyze the causality of end-to-end autonomous driving. First, we validate the essential information that the final planning depends on by using controlled variables and counterfactual interventions for qualitative analysis. Then, we quantitatively assess the factors influencing model decisions by visualizing and statistically analyzing the response of key model inputs. Finally, based on the comprehensive study of the multi-factorial end-to-end autonomous driving system, we have developed a strong baseline and a tool for exploring causality in the close-loop simulator CARLA. It leverages the essential input sources to obtain a well-designed model, resulting in highly competitive capabilities. As far as we know, our work is the first to unveil the mystery of end-to-end autonomous driving and turn the black box into a white one. Thorough close-loop experiments demonstrate that our method can be applied to end-to-end autonomous driving solutions for causality debugging. Code will be available at https://github.com/bdvisl/DriveInsight.

cs.CV↗

arXiv2024

Nematic Ising superconductivity with hidden magnetism in few-layer 6R-TaS2

In van der Waals heterostructures (vdWHs), the manipulation of interlayer stacking/coupling allows for the construction of customizable quantum systems exhibiting exotic physics. An illustrative example is the diverse range of states of matter achieved through varying the proximity coupling between two-dimensional (2D) quantum spin liquid (QSL) and superconductors within the TaS2 family. This study presents a demonstration of the intertwined physics of spontaneous rotational symmetry breaking, hidden magnetism, and Ising superconductivity in the three-fold rotationally symmetric, non-magnetic natural vdWHs 6R-TaS2. A distinctive phase emerges in 6R-TaS2 below a characteristic temperature (T*) of approximately 30 K, which is characterized by a remarkable set of features, including a giant extrinsic anomalous Hall effect (AHE), Kondo screening, magnetic field-tunable thermal hysteresis, and nematic magneto-resistance. At lower temperatures, a coexistence of nematicity and Kondo screening with Ising superconductivity is observed, providing compelling evidence of hidden magnetism within a superconductor. This research not only sheds light on unexpected emergent physics resulting from the coupling of itinerant electrons and localized/correlated electrons in natural vdWHs but also emphasizes the potential for tailoring exotic quantum states through the manipulation of interlayer interactions.

cond-mat.str-el↗

arXiv2024

Three-dimensional quantum Griffiths singularity in bulk iron-pnictide superconductors

The quantum Griffiths singularity (QGS) is a phenomenon driven by quenched disorders that break conventional scaling invariance and result in a divergent dynamical critical exponent during quantum phase transitions (QPT). While this phenomenon has been well-documented in low-dimensional conventional superconductors and in three-dimensional (3D) magnetic metal systems, its presence in 3D superconducting systems and in unconventional high-temperature superconductors (high-Tc SCs) remains unclear. In this study, we report the observation of robust QGS in the superconductor-metal transition (SMT) of both quasi-2D and 3D anisotropic unconventional high-Tc superconductor CaFe1-xNixAsF (x < 5%) bulk single crystals, where the QGS states persist to up to 5.3 K. A comprehensive quantum phase diagram is established that delineates the 3D anisotropic QGS of SMT induced by perpendicular and parallel magnetic field. Our findings reveal the universality of QGS in 3D superconducting systems and unconventional high-Tc SCs, thereby substantially expanding the range of applicability of QGS.

cond-mat.str-el↗

arXiv2024

CLIP-GS: CLIP-Informed Gaussian Splatting for View-Consistent 3D Indoor Semantic Understanding

Exploiting 3D Gaussian Splatting (3DGS) with Contrastive Language-Image Pre-Training (CLIP) models for open-vocabulary 3D semantic understanding of indoor scenes has emerged as an attractive research focus. Existing methods typically attach high-dimensional CLIP semantic embeddings to 3D Gaussians and leverage view-inconsistent 2D CLIP semantics as Gaussian supervision, resulting in efficiency bottlenecks and deficient 3D semantic consistency. To address these challenges, we present CLIP-GS, efficiently achieving a coherent semantic understanding of 3D indoor scenes via the proposed Semantic Attribute Compactness (SAC) and 3D Coherent Regularization (3DCR). SAC approach exploits the naturally unified semantics within objects to learn compact, yet effective, semantic Gaussian representations, enabling highly efficient rendering (>100 FPS). 3DCR enforces semantic consistency in 2D and 3D domains: In 2D, 3DCR utilizes refined view-consistent semantic outcomes derived from 3DGS to establish cross-view coherence constraints; in 3D, 3DCR encourages features similar among 3D Gaussian primitives associated with the same object, leading to more precise and coherent segmentation results. Extensive experimental results demonstrate that our method remarkably suppresses existing state-of-the-art approaches, achieving mIoU improvements of 21.20% and 13.05% on ScanNet and Replica datasets, respectively, while maintaining real-time rendering speed. Furthermore, our approach exhibits superior performance even with sparse input data, substantiating its robustness.

cs.CV↗

arXiv2023

Uncertainty Guided Adaptive Warping for Robust and Efficient Stereo Matching

Correlation based stereo matching has achieved outstanding performance, which pursues cost volume between two feature maps. Unfortunately, current methods with a fixed model do not work uniformly well across various datasets, greatly limiting their real-world applicability. To tackle this issue, this paper proposes a new perspective to dynamically calculate correlation for robust stereo matching. A novel Uncertainty Guided Adaptive Correlation (UGAC) module is introduced to robustly adapt the same model for different scenarios. Specifically, a variance-based uncertainty estimation is employed to adaptively adjust the sampling area during warping operation. Additionally, we improve the traditional non-parametric warping with learnable parameters, such that the position-specific weights can be learned. We show that by empowering the recurrent network with the UGAC module, stereo matching can be exploited more robustly and effectively. Extensive experiments demonstrate that our method achieves state-of-the-art performance over the ETH3D, KITTI, and Middlebury datasets when employing the same fixed model over these datasets without any retraining procedure. To target real-time applications, we further design a lightweight model based on UGAC, which also outperforms other methods over KITTI benchmarks with only 0.6 M parameters.

cs.CV↗

arXiv2022

DIP: Deep Inverse Patchmatch for High-Resolution Optical Flow

Recently, the dense correlation volume method achieves state-of-the-art performance in optical flow. However, the correlation volume computation requires a lot of memory, which makes prediction difficult on high-resolution images. In this paper, we propose a novel Patchmatch-based framework to work on high-resolution optical flow estimation. Specifically, we introduce the first end-to-end Patchmatch based deep learning optical flow. It can get high-precision results with lower memory benefiting from propagation and local search of Patchmatch. Furthermore, a new inverse propagation is proposed to decouple the complex operations of propagation, which can significantly reduce calculations in multiple iterations. At the time of submission, our method ranks first on all the metrics on the popular KITTI2015 benchmark, and ranks second on EPE on the Sintel clean benchmark among published optical flow methods. Experiment shows our method has a strong cross-dataset generalization ability that the F1-all achieves 13.73%, reducing 21% from the best published result 17.4% on KITTI2015. What's more, our method shows a good details preserving result on the high-resolution dataset DAVIS and consumes 2x less memory than RAFT.

cs.CV↗

arXiv2022

Practical Stereo Matching via Cascaded Recurrent Network with Adaptive Correlation

With the advent of convolutional neural networks, stereo matching algorithms have recently gained tremendous progress. However, it remains a great challenge to accurately extract disparities from real-world image pairs taken by consumer-level devices like smartphones, due to practical complicating factors such as thin structures, non-ideal rectification, camera module inconsistencies and various hard-case scenes. In this paper, we propose a set of innovative designs to tackle the problem of practical stereo matching: 1) to better recover fine depth details, we design a hierarchical network with recurrent refinement to update disparities in a coarse-to-fine manner, as well as a stacked cascaded architecture for inference; 2) we propose an adaptive group correlation layer to mitigate the impact of erroneous rectification; 3) we introduce a new synthetic dataset with special attention to difficult cases for better generalizing to real-world scenes. Our results not only rank 1st on both Middlebury and ETH3D benchmarks, outperforming existing state-of-the-art methods by a notable margin, but also exhibit high-quality details for real-life photos, which clearly demonstrates the efficacy of our contributions.

cs.CV↗

arXiv2021

Radiation-hardened and Repairable MoS$_2$ Field Effect Devices with Polymer Solid Electrolyte Gates

As human activities expand into naturally or man-made radiation-prone environment, the need for radiation-hardened (Rad-Hard) electronic hardware surged. The state-of-the-art silicon-based and two-dimensional (2D) materials based Rad-Hard transistors can withstand up to 1 Mrad (Si) of total ionization dose (TID), while higher TID tolerance is being heatedly sought after. Here we present few-layer MoS$_2$ Rad-Hard field-effect transistors (FETs) with polymer solid electrolyte (PSE) gate dielectrics. The MoS$_2$ PSE-FETs exhibit a TID tolerance of up to 3.75 Mrad (Si) at a dose rate of 523 rad (Si) s$^{-1}$ and can be repaired with a moderate thermal annealing at 100 $^{\circ}$C for 5 minutes. Combining the excellent intrinsic radiation tolerance and the reparability, the MoS$_2$ PSE-FETs reach a TID tolerance of up to 10 Mrad (Si). Complementary metal-oxide-semiconductor (CMOS)-like MoS$_2$ PSE-inverters have been built and show similar high radiation tolerance. Furthermore, the feasibility of wafer-scale Rad-Hard PSE-inverter array has been demonstrated using chemical vapor deposition (CVD) grown monolayer MoS$_2$. Our studies uncover the potential of 2D materials based PSE devices in future Rad-Hard integrated circuits (ICs).

cond-mat.mtrl-sci↗