arXiv subjects

Qiang Zheng

Publications and source records attributed to Qiang Zheng.

At least 19 recordsLinked to original sources

arXiv2026

Wafer-scale hybrid molecular beam epitaxy of BaTiO3 and SrTiO3 on silicon

The integration of epitaxial barium titanate (BTO) on silicon represents a highly promising pathway for next-generation, energy-efficient photonic integrated circuits due to BTO's exceptionally high Pockels coefficients. However, the scalable epitaxy of BTO on Si remains hindered by complex stoichiometric control and slow growth rates. In this work, we demonstrate the continuous, uniform wafer-scale growth of high-quality BTO films on SrTiO3 (STO)-buffered 4-inch Si(001) wafers using a fully hybrid molecular beam epitaxy (hMBE) approach. By utilizing titanium tetraisopropoxide as a titanium precursor, we achieve a self-regulating, adsorption-controlled layer-by-layer growth at rates exceeding 75 nm/h, while maintaining an atomically sharp and structurally coherent BTO/STO interface. We systematically compare the structural, ferroelectric, and electro-optic (EO) properties of fully hMBE-grown BTO with those deposited via pulsed laser deposition (PLD) on identical STO/Si templates. While both techniques yield high-quality c-domain dominated films, the optimized hMBE-grown BTO exhibits superior crystallinity and a larger effective EO coefficient of 248 pm/V, surpassing that of the PLD-grown films (220 pm/V). These results highlight the advantages of the fully hMBE approach as a scalable, deterministic, and high-performance materials platform for wafer-scale integrated ferroelectric photonics.

cond-mat.mtrl-sci↗

arXiv2026

Bulk superconductivity up to 96 K in pressurized nickelate single crystals

Recently, the Ruddlesden-Popper bilayer nickelate $La_3Ni_2O_7$ has emerged as a superconductor with a transition temperature ($T_c$) of approximately 80 K above 14 GPa (Refs. 1-3). Achieving higher $T_c$ in nickelate superconductors, along with the synthesis of reproducible high-quality single crystals without relying on high-oxygen-pressure growth conditions, remains a significant challenge$^{[4-7]}$. Here we report superconductivity up to 96 K under high pressure in bilayer nickelate single crystals synthesized at ambient pressure. Energy-dispersive spectroscopy, single-crystal X-ray diffraction, nuclear quadrupole resonance and scanning transmission electron microscopy evidenced high crystal quality of the flux-grown $La_2SmNi_2O_{7-δ}$ single crystals. $La_2SmNi_2O_7$ exhibits clear bulk superconductivity, including zero resistivity ($T_{c,max}^{onset}$ = 92 K and $T_{c,max}^{zero}$ = 73 K at 21.6 GPa) and the Meissner effect ($T_c$= 60 K at 20.6 GPa). A low-temperature high-pressure structural study indicates that both monoclinic and tetragonal structures can support superconductivity in this bilayer nickelate. Furthermore, we established a correlation between higher $T_c$ under high pressures and larger in-plane lattice distortion under ambient conditions, corroborated by observing even higher $T_c^{onset}$ of 96 K in $La_{1.57}Sm_{1.43}Ni_2O_{7-δ}$. This study overcomes key limitations in growing nickelate superconductor crystals, resolves the crystal structure in the superconducting state and demonstrates an effective pathway towards achieving higher $T_c$.

cond-mat.supr-con↗

arXiv2026

Reply to "Threefold error in the reported zero-field cooled magnetic moment of single crystal $La_2SmNi_2O_7$ (arXiv: 2602.23240)"

We respond to the critique by Aleksandr V. Korolev and Evgeny F. Talantsev on the superconducting phase fraction ($f$) calculations in Li et al. Nature 649, 871-878 (2026). First, the weak upturn in the low-temperature tail of our data has been confirmed to originate from the background, and the paramagnetic Meissner effect is absent in our case; thus, field-cooled (FC) data can be used for superconducting phase fraction calculations. Second, demagnetization effect must be calculated based on the actual measured moment as a function of $f$, which has been well-established and routinely employed in the superconductivity community. In contrast, Korolev and Talantsev treated the demagnetization field as a constant; thus, their calculation underestimates $f$ by a factor of $(1-Nχ_{meas})(1-N)$. This factor is close to 1/3, given $N$ = 0.849, $χ_{meas}$ = -1.313 in our study, which explains the origin of their deviated result (nearly three times smaller than our results). Third, our sample is a homogeneous high-quality bulk single crystal, evidenced by various techniques, making the existence of multiple discrete superconducting regions highly unlikely. We conclude that the superconducting phase fraction calculations reported in Li et al. Nature 649, 871-878 (2026) are not invalidated by the analyses presented in Korolev et al. arXiv: 2602.23240 (2026).

cond-mat.supr-con↗

arXiv2025

CNOT Oriented Synthesis for Small-Scale Boolean Functions Using Spatial Structures of Parallelotopes

Quantum computing has garnered significant interest for its potential to achieve exponential speedups over classical approaches. However, in the Noisy Intermediate-Scale Quantum (NISQ) era, quantum circuit scalability remains limited by gate fidelity and qubit counts, restricting physical implementations to small-scale circuits. While prior work has explored logic network structures for quantum circuit synthesis, these methods often neglect the spatial structure intrinsic to Boolean functions. In this paper, we leverage this spatial structure, encoded by parallelotopes embedded in the hypercube defined by the Boolean function, to access a broader optimization space, enhancing synthesis efficiency and reducing circuit complexity. We propose the Spatial Structure-based Hypercube Reduction~(SSHR), a novel synthesis method tailored for small-scale Boolean functions ($\leq 8$). SSHR extracts global spatial features to minimize the use of Multi-Control Toffoli (MCT) gates. To further exploit spatial correlations, we introduce two variants: SSHR-H employs heuristic functions to accelerate synthesis runtime, while SSHR-I integrates an Integer Linear Programming (ILP) solver to maximize spatial structure utilization. Our approach outperforms existing techniques in small-scale circuit synthesis, achieving 56\% and 81\% reductions in CNOT gate counts compared to the Exclusive Sum-of-Products (ESOP) and Xor-And-Inverter Graph (XAG) methods, respectively.

quant-ph↗

arXiv2025

$La_3Pd_2NaO_9$: A High-Valent Insulating Palladate

A high-valent palladate, $La_3Pd_2NaO_9$, has been synthesized for the first time. Single crystals with dimensions of 20 $μ$m on edge were successfully grown using the flux method at 420 $^o$C and 70 bar oxygen pressure. Energy dispersive spectroscopy (EDS) and inductively coupled plasma mass spectroscopy (ICP) measurements show that the atomic ratio of La: (Pd+Na) is 3: 3 and Pd: Na is 2: 1. X-ray photoelectron spectroscopy (XPS) measurements show that the oxidation state of Pd is dominated by +4. Synchrotron X-ray single-crystal diffraction measurements revealed that this material crystallizes in the monoclinic $P2_1/c$ space group with charge ordering of Na and Pd. Real-space imaging via scanning transmission electron microscopy (STEM) confirmed the crystal structure and revealed excellent sample homogeneity. Electrical resistivity measurements show an insulating behavior. Magnetic measurements show an unexpected paramagnetic behavior, which probably originate from a small fraction of high-spin Pd$^{2+}$ evidenced by XPS. The successful growth of $La_3Pd_2NaO_9$ single crystals with a high-valent oxidation state of Pd offers an approach for exploring interesting palladates, including potential bilayer Ruddlesden-Popper palladates analogous to the high temperature superconducting $La_3Ni_2O_7$.

cond-mat.str-el↗

arXiv2025

Deteriorated Interlayer Coupling in Twisted Bilayer Cobaltites

A wealth of remarkable behaviors is observed at the interfaces between magnetic oxides due to the coexistence of Coulomb repulsion and interatomic exchange interactions. While previous research has focused on bonded oxide heterointerfaces, studies on magnetism in van der Waals interfaces remain rare. In this study, we stacked two freestanding cobaltites with precisely controlled twist angles. Scanning transmission electron microscopy revealed clear and ordered moiré patterns, which exhibit an inverse relationship with the twist angle. We found that the Curie temperature in the twisted region is reduced by approximately 13 K compared to the single-layer region using nitrogen-vacancy (NV) magnetometry. This phenomenon may be related to the weakening of the orbital hybridization between oxygen ions and transition metal ions in the unbonded interfaces. Our findings suggest a potential avenue for modulating magnetic interactions in correlated systems through twist, providing opportunities for the discovery of unknown quantum states.

cond-mat.mtrl-sci↗

arXiv2025

Damage of bilayer structure in La3Ni2O7-d induced by high pO2 annealing

The discovery of superconductivity with onset temperature of ~80 K in pressurized bilayer Ruddlesden-Popper La3Ni2O7-d has attracted much attention. Despite intense research, determination of the exact oxygen content and understanding of the relationship between superconductivity and oxygen content remain a big challenge. Here, we report a systematical study on the structure and physical properties of La3Ni2O7-d polycrystalline powders which were prepared using sol-gel method at ambient pressure and then annealed under various oxygen pressure. We found that high pO2 annealing with slow cooling results in a new phase, which can be modeled using the hybrid single-layer-trilayer La3Ni2O7 or the tetragonal bilayer La3Ni2O7. Scanning transmission electron microscopy (STEM) measurements revealed significant single layers and trilayers after high oxygen pressure annealing, evidencing damage of the bilayer structure. The superconducting transition under high pressure became weak for high pO2 annealed samples, which is consistent with the damage of the bilayer structure. Our results reveal that the bilayer structure is fragile and post-annealing under near atmosphere pressure of oxygen is suitable to maintain bilayer structure and increase oxygen content at the same time.

cond-mat.supr-con↗

arXiv2025

Signature of superconductivity in pressurized La4Ni3O10-x single crystals grown at ambient pressure

Nickelates have attracted enormous attention since the discovery of high-temperature superconductivity in La3Ni2O7 under high pressure. However, whether superconducting nickelate single crystals can be prepared at ambient pressure remains elusive. Here we report signature of superconductivity in pressurized La4Ni3O10-x single crystals grown from potassium carbonate flux at ambient pressure. Single crystal X-ray diffraction and scanning transmission electron microscopy investigations re-vealed high-quality single crystals with perfect stacking of trilayers. Resistivity measurements indicate that the metal-to-metal transition observed at ambient pressure was suppressed under high pressure, and a sharp drop occurred at ~30 K at 77.9 GPa, consistent with superconductivity in pressurized La4Ni3O10 single crystals grown by the floating zone method at an oxygen pressure of >18 bar. Our results not only provide an important path to prepare high-quality nickelate single crystals but also support superconductivity in nickelates under high pressure, promoting more systematic and in-depth research in this compelling field.

cond-mat.supr-con↗

arXiv2025

SA-MLP: A Low-Power Multiplication-Free Deep Network for 3D Point Cloud Classification in Resource-Constrained Environments

Point cloud classification plays a crucial role in the processing and analysis of data from 3D sensors such as LiDAR, which are commonly used in applications like autonomous vehicles, robotics, and environmental monitoring. However, traditional neural networks, which rely heavily on multiplication operations, often face challenges in terms of high computational costs and energy consumption. This study presents a novel family of efficient MLP-based architectures designed to improve the computational efficiency of point cloud classification tasks in sensor systems. The baseline model, Mul-MLP, utilizes conventional multiplication operations, while Add-MLP and Shift-MLP replace multiplications with addition and shift operations, respectively. These replacements leverage more sensor-friendly operations that can significantly reduce computational overhead, making them particularly suitable for resource-constrained sensor platforms. To further enhance performance, we propose SA-MLP, a hybrid architecture that alternates between shift and adder layers, preserving the network depth while optimizing computational efficiency. Unlike previous approaches such as ShiftAddNet, which increase the layer count and limit representational capacity by freezing shift weights, SA-MLP fully exploits the complementary advantages of shift and adder layers by employing distinct learning rates and optimizers. Experimental results show that Add-MLP and Shift-MLP achieve competitive performance compared to Mul-MLP, while SA-MLP surpasses the baseline, delivering results comparable to state-of-the-art MLP models in terms of both classification accuracy and computational efficiency. This work offers a promising, energy-efficient solution for sensor-driven applications requiring real-time point cloud classification, particularly in environments with limited computational resources.

cs.CV↗

arXiv2024

PointViG: A Lightweight GNN-based Model for Efficient Point Cloud Analysis

In the domain of point cloud analysis, despite the significant capabilities of Graph Neural Networks (GNNs) in managing complex 3D datasets, existing approaches encounter challenges like high computational costs and scalability issues with extensive scenarios. These limitations restrict the practical deployment of GNNs, notably in resource-constrained environments. To address these issues, this study introduce Point<\b> Vi<\b>sion G<\b>NN (PointViG), an efficient framework for point cloud analysis. PointViG incorporates a lightweight graph convolutional module to efficiently aggregate local features and mitigate over-smoothing. For large-scale point cloud scenes, we propose an adaptive dilated graph convolution technique that searches for sparse neighboring nodes within a dilated neighborhood based on semantic correlation, thereby expanding the receptive field and ensuring computational efficiency. Experiments demonstrate that PointViG achieves performance comparable to state-of-the-art models while balancing performance and complexity. On the ModelNet40 classification task, PointViG achieved 94.3% accuracy with 1.5M parameters. For the S3DIS segmentation task, it achieved an mIoU of 71.7% with 5.3M parameters. These results underscore the potential and efficiency of PointViG in point cloud analysis.

cs.CV↗

arXiv2024

PointMT: Efficient Point Cloud Analysis with Hybrid MLP-Transformer Architecture

In recent years, point cloud analysis methods based on the Transformer architecture have made significant progress, particularly in the context of multimedia applications such as 3D modeling, virtual reality, and autonomous systems. However, the high computational resource demands of the Transformer architecture hinder its scalability, real-time processing capabilities, and deployment on mobile devices and other platforms with limited computational resources. This limitation remains a significant obstacle to its practical application in scenarios requiring on-device intelligence and multimedia processing. To address this challenge, we propose an efficient point cloud analysis architecture, \textbf{Point} \textbf{M}LP-\textbf{T}ransformer (PointMT). This study tackles the quadratic complexity of the self-attention mechanism by introducing a linear complexity local attention mechanism for effective feature aggregation. Additionally, to counter the Transformer's focus on token differences while neglecting channel differences, we introduce a parameter-free channel temperature adaptation mechanism that adaptively adjusts the attention weight distribution in each channel, enhancing the precision of feature aggregation. To improve the Transformer's slow convergence speed due to the limited scale of point cloud datasets, we propose an MLP-Transformer hybrid module, which significantly enhances the model's convergence speed. Furthermore, to boost the feature representation capability of point tokens, we refine the classification head, enabling point tokens to directly participate in prediction. Experimental results on multiple evaluation benchmarks demonstrate that PointMT achieves performance comparable to state-of-the-art methods while maintaining an optimal balance between performance and accuracy.

cs.CV↗

arXiv2024

PMT-MAE: Dual-Branch Self-Supervised Learning with Distillation for Efficient Point Cloud Classification

Advances in self-supervised learning are essential for enhancing feature extraction and understanding in point cloud processing. This paper introduces PMT-MAE (Point MLP-Transformer Masked Autoencoder), a novel self-supervised learning framework for point cloud classification. PMT-MAE features a dual-branch architecture that integrates Transformer and MLP components to capture rich features. The Transformer branch leverages global self-attention for intricate feature interactions, while the parallel MLP branch processes tokens through shared fully connected layers, offering a complementary feature transformation pathway. A fusion mechanism then combines these features, enhancing the model's capacity to learn comprehensive 3D representations. Guided by the sophisticated teacher model Point-M2AE, PMT-MAE employs a distillation strategy that includes feature distillation during pre-training and logit distillation during fine-tuning, ensuring effective knowledge transfer. On the ModelNet40 classification task, achieving an accuracy of 93.6\% without employing voting strategy, PMT-MAE surpasses the baseline Point-MAE (93.2\%) and the teacher Point-M2AE (93.4\%), underscoring its ability to learn discriminative 3D point cloud representations. Additionally, this framework demonstrates high efficiency, requiring only 40 epochs for both pre-training and fine-tuning. PMT-MAE's effectiveness and efficiency render it well-suited for scenarios with limited computational resources, positioning it as a promising solution for practical point cloud analysis.

cs.CV↗

arXiv2024

Efficient Point Cloud Classification via Offline Distillation Framework and Negative-Weight Self-Distillation Technique

The rapid advancement in point cloud processing technologies has significantly increased the demand for efficient and compact models that achieve high-accuracy classification. Knowledge distillation has emerged as a potent model compression technique. However, traditional KD often requires extensive computational resources for forward inference of large teacher models, thereby reducing training efficiency for student models and increasing resource demands. To address these challenges, we introduce an innovative offline recording strategy that avoids the simultaneous loading of both teacher and student models, thereby reducing hardware demands. This approach feeds a multitude of augmented samples into the teacher model, recording both the data augmentation parameters and the corresponding logit outputs. By applying shape-level augmentation operations such as random scaling and translation, while excluding point-level operations like random jittering, the size of the records is significantly reduced. Additionally, to mitigate the issue of small student model over-imitating the teacher model's outputs and converging to suboptimal solutions, we incorporate a negative-weight self-distillation strategy. Experimental results demonstrate that the proposed distillation strategy enables the student model to achieve performance comparable to state-of-the-art models while maintaining lower parameter count. This approach strikes an optimal balance between performance and complexity. This study highlights the potential of our method to optimize knowledge distillation for point cloud classification tasks, particularly in resource-constrained environments, providing a novel solution for efficient point cloud analysis.

cs.CV↗

arXiv2024

MindSpore Quantum: A User-Friendly, High-Performance, and AI-Compatible Quantum Computing Framework

We introduce MindSpore Quantum, a pioneering hybrid quantum-classical framework with a primary focus on the design and implementation of noisy intermediate-scale quantum (NISQ) algorithms. Leveraging the robust support of MindSpore, an advanced open-source deep learning training/inference framework, MindSpore Quantum exhibits exceptional efficiency in the design and training of variational quantum algorithms on both CPU and GPU platforms, delivering remarkable performance. Furthermore, this framework places a strong emphasis on enhancing the operational efficiency of quantum algorithms when executed on real quantum hardware. This encompasses the development of algorithms for quantum circuit compilation and qubit mapping, crucial components for achieving optimal performance on quantum processors. In addition to the core framework, we introduce QuPack, a meticulously crafted quantum computing acceleration engine. QuPack significantly accelerates the simulation speed of MindSpore Quantum, particularly in variational quantum eigensolver (VQE), quantum approximate optimization algorithm (QAOA), and tensor network simulations, providing astonishing speed. This combination of cutting-edge technologies empowers researchers and practitioners to explore the frontiers of quantum computing with unprecedented efficiency and performance.

quant-ph↗

arXiv2023

Design and synthesis of three-dimensional hybrid Ruddlesden-Popper nickelate single crystals

Advancement of technologies relies on discovery of new materials with emerging physical properties that are determined by their crystal structures. Ruddlesden-Popper (R-P) phases with formula of $A_{n+1}$$B_n$$X_{3n+1}$ (n=1,2,3...) are among one of the most widely studied class of materials due to their electrical, optical, magnetic, thermal properties and their combined multifunctional properties(Ref.1-6). In R-P phases, intergrowth is well-known in the short range(Ref.7-9); however, no existing compounds have been reported to have different n mixed in bulk single crystals. Here we design a hybrid R-P nickelate $La_2NiO_4$$La_3Ni_2O_7$ by alternatively stacking bilayers, which is the active structural motif in the newly discovery high-$T_c$ superconductor $La_3Ni_2O_7$ and single layers of the antiferromagnetic insulator $La_2NiO_4$. We report the successful synthesis of $La_2NiO_4$$La_3Ni_2O_7$ single crystals, and X-ray diffraction and real-space imaging vis STEM show that the crystal structure consists of single layers and bilayers of $NiO_6$ octahedral stacking alternatively perpendicular to the ab plane, characterized by the orthorhombic Immm (No.71) space group. Resistivity measurements indicate a peculiar insulator-to-metal transition around 140 K on cooling. Correlated density functional theory calculations corroborate this finding, and reveal that the single layer becomes paramagnetic metallic due to charge transfer via LaO layers. The discovery of $La_2NiO_4$$La_3Ni_2O_7$ opens a door to access a completely new family of 3D hybrid R-P phases with the formula of $A_{n+1}$$B_n$$X_{3n+1}$$A'_{m+1}$$B'_m$$X'_{3m+1}$ which potentially host a plethora of emerging physical properties for various applications.

cond-mat.mtrl-sci↗

arXiv2023

Stacking disorder and thermal transport properties of $α$-RuCl$_3$

$α$-RuCl$_3$, a well-known candidate material for Kitaev quantum spin liquid, is prone to stacking disorder due to the weak van der Waals bonding between the honeycomb layers. After a decade of intensive experimental and theoretical studies, the detailed correlation between stacking degree of freedom, structure transition, magnetic and thermal transport properties remains unresolved. In this work, we reveal the effects of a small amount of stacking disorder inherent even in high quality $α$-RuCl$_3$ crystals. This small amount of stacking disorder results in the variation of the magnetic ordering temperature, suppresses the structure transition and thermal conductivity. Crystals with minimal amount of stacking disorder have a T$_N>$7.4\,K and exhibit a well-defined structure transition around 140\,K upon cooling. For those with more stacking faults and a T$_N$ below 7\,K, the structure transition occurs well below 140\,K upon cooling and is incomplete, manifested by the diffuse streaks and the coexistence of both high temperature and low temperature phases down to the lowest measurement temperature. Both types of crystals exhibit oscillatory field dependent thermal conductivity and a plateau-like feature in thermal Hall resistivity in the field-induced quantum spin liquid state. However, $α$-RuCl$_3$ crystals with minimal amount of stacking disorder have a higher thermal conductivity that pushes the thermal Hall conductivity to be closer to the half-integer quantized value. These findings demonstrate a strong correlation between layer stacking, structure transition, magnetic and thermal transport properties, underscoring the importance of interlayer coupling in $α$-RuCl$_3$ despite the weak van der Waals bonding.

cond-mat.mtrl-sci↗

arXiv2023

Miniature Magnetic Nano islands in a Morphotropic Cobaltite Matrix

High-density magnetic memories are key components in spintronics, quantum computing, and energy-efficient electronics. Reduced dimensionality and magnetic domain stability at the nanoscale are essential for the miniaturization of magnetic storage units. Yet, inducing magnetic order, and selectively tuning spin-orbital coupling at specific locations have remained challenging. Here we demonstrate the construction of switchable magnetic nano-islands in a nonmagnetic matrix based on cobaltite homo-structures. The magnetic and electronic states are laterally modified by epitaxial strain, which is regionally controlled by freestanding membranes. Atomically sharp grain boundaries isolate the crosstalk between magnetically distinct regions. The minimal size of magnetic nano-islands reaches 35 nm in diameter, enabling an areal density of 400 Gbit per inch square. Besides providing an ideal platform for precisely controlled read and write schemes, this methodology can enable scalable and patterned memories on silicon and flexible substrates for various applications.

cond-mat.mtrl-sci↗

arXiv2022

Synthesis of functional nitride membranes using sacrificial water-soluble BaO layers

Transition metal nitrides (TMNs) exhibit fascinating physical properties that hold great potential in future device applications. To stack two-dimensional TMNs with other functional materials that have dissimilar orientations and symmetries requires to separate epitaxial TMNs from the growth substrates. However, the lattice constants of TMNs are not compatible with those of most sacrificial layers, leading to a great challenge to fabricate high-quality single crystalline TMN membranes. In this letter, we report the application of a water-soluble BaO sacrificial layer as a general approach to create freestanding TMN membranes. Taken CrN as an example, the relatively small lattice mismatch and identical cubic structure between BaO and CrN ensure the growth of heterostructures. Millimeter-size CrN membrane allows us to directly observe the planar-view of atomic structure and to correlate its electronic state with intrinsic transport properties. Our work provides the opportunity to fabricate freestanding TMN membranes and the ability to transfer them to arbitrary substrates. The integration of TMN membranes with other materials will stimulate further studies in the emergent phenomena at heterointerfaces.

cond-mat.mtrl-sci↗