arXiv subjects

Tiejun Wang

Publications and source records attributed to Tiejun Wang.

14 recordsLinked to original sources

arXiv2026

Trainable Quantum Channels as Computational Primitives for Quantum Learning

Variational quantum learning is traditionally constrained to unitary dynamics, often treating quantum channels as detrimental noise. In this work, we reformulate the quantum channels as trainable computational primitives and establish a non-unitary quantum machine learning framework grounded in open-system dynamics. We demonstrate that the outputs of channel-enhanced quantum models form a structured superposition of multiple functional components. Each component is governed by an effective observable whose spectrum can be adaptively modulated during training, a significant departure from the spectral invariance in unitary transformations. Moreover, the proposed framework generalizes conventional unitary quantum models by retaining them as a special case while introducing additional non-unitary degrees of freedom. Furthermore, we reveal that trainable quantum channels enrich the optimization geometry through ensemble-averaged gradient and additional optimization directions induced by the Kraus operators. Extensive experiments on classification tasks using trainable amplitude-damping and phase-damping channels confirm enhanced optimization dynamics and predictive performance. In addition, we experimentally validate the proposed framework through hardware inference using ten-qubit quantum models implemented on the superconducting quantum processor, confirming its practical feasibility and hardware compatibility. Our work provides a principled approach for leveraging quantum channels as trainable resources and advances the design of high-performance quantum learning architectures.

quant-ph↗

arXiv2025

Concentration-Dependent Tungsten Effects on Chemical Short-Range Order and Deformation Behavior in Ni-W alloys

Ni-W based medium heavy alloys offer a promising pathway to bridge the density-strength gap between tungsten heavy alloys and ultrahigh-strength steels. In this study, the effects of W concentration on chemical short-range order (CSRO), deformation behavior, and grain boundary chemistry of Ni-xW alloys in the range x = 0 to 38 wt% were systematically investigated using a suite of advanced characterization and modeling techniques, including synchrotron X-ray diffraction, transmission electron microscopy, atom probe tomography, and first-principles thermodynamic simulations. Our study reveals that strong CSRO emerges when W content exceeds about 30 wt%, producing distinct diffuse scattering and significantly enhancing strain-hardening capacity. During deformation, the presence of SRO promotes planar slip and twin formation, leading to strong dislocation interactions and elevated flow stress. Hall-Petch analysis demonstrates an exceptionally high grain boundary strengthening coefficient (ky about 1100 MPa micrometer^(1/2)) in Ni-38W, underscoring the intrinsic strengthening effect associated with CSRO. First-principles cluster expansion coupled with Monte Carlo simulations reveals that increasing W content enhances SRO tendency through the stabilization of Ni4W-type local configurations. These findings establish a mechanistic link between W concentration, CSRO evolution, and mechanical response, providing new insights for designing high-density, high-strength Ni-W based alloys with optimized performance.

cond-mat.mtrl-sci↗

arXiv2025

An Improved Time Series Anomaly Detection by Applying Structural Similarity

Effective anomaly detection in time series is pivotal for modern industrial applications and financial systems. Due to the scarcity of anomaly labels and the high cost of manual labeling, reconstruction-based unsupervised approaches have garnered considerable attention. However, accurate anomaly detection remains an unsettled challenge, since the optimization objectives of reconstruction-based methods merely rely on point-by-point distance measures, ignoring the potential structural characteristics of time series and thus failing to tackle complex pattern-wise anomalies. In this paper, we propose StrAD, a novel structure-enhanced anomaly detection approach to enrich the optimization objective by incorporating structural information hidden in the time series and steering the data reconstruction procedure to better capture such structural features. StrAD accommodates the trend, seasonality, and shape in the optimization objective of the reconstruction model to learn latent structural characteristics and capture the intrinsic pattern variation of time series. The proposed structure-aware optimization objective mechanism can assure the alignment between the original data and the reconstructed data in terms of structural features, thereby keeping consistency in global fluctuation and local characteristics. The mechanism is pluggable and applicable to any reconstruction-based methods, enhancing the model sensitivity to both point-wise anomalies and pattern-wise anomalies. Experimental results show that StrAD improves the performance of state-of-the-art reconstruction-based models across five real-world anomaly detection datasets.

cs.LG↗

arXiv2025

CAPMix: Robust KPI Anomaly Detection for AIOps in Noisy and Dynamic Environments

Time-series anomaly detection is crucial in AIOps for maintaining large-scale service reliability. In production, streams of Key Performance Indicators (KPI) are high-dimensional, non-stationary, and affected by noise, deployment changes, and latent anomalies, making real failures hard to distinguish from benign variation. Most existing methods assume either normality (learning from "normal" history) or rely on injected anomalies for training. Yet injected patterns often misalign with real failure modes, skewing decision boundaries -- aka. Anomaly Shift. We propose CAPMix, a controllable anomaly augmentation framework with prior-guided injection for realistic temporal behaviors. CAPMix combines label revision and dual-space mixup to enhance robustness under contaminated and mixed data. CAPMix consistently outperforms state-of-the-art methods on public AIOps and time-series benchmarks. It has been deployed in Kuaishou's large-scale production system, reducing false alarms and improving monitoring reliability. A real-world dataset is also released to enrich the research on robust KPI anomaly detection.

cs.LG↗

arXiv2025

Generalizability of local neural operator: example for elastodynamic problems

Local neural operator (LNO) conception has provided a feasible way for scientific computations. The LNO learns transient partial differential equations from random field samples, and then the pre-trained LNO solves practical problems on specific computational domains. For applications, we may ask: Are the training samples rich enough? To what extent can we trust the solutions obtained from pre-trained LNO models for unknown cases? The generalizability of LNO could answer these questions. Here, we propose to use two plain scalar features, the amplitude and wavenumber of the input functions, to indicate the richness of training samples and to evaluate the generalization error of pre-trained LNO. In elastodynamic practices, we find that isolated evolving wavenumber modes for Lam\'e-Navier equation caused the training dataset to lack mode diversity. By data supplementation and model fine-tuning targeting to the discovered lack modes, the pre-trained and fine-tuned LNO model solves Lamb problem correctly and efficiently. These results and the proposed generalization criteria provide a paradigm for LNO applications.

physics.comp-ph↗

arXiv2024

RobotDiffuse: Diffusion-Based Motion Planning for Redundant Manipulators with the ROP Obstacle Avoidance Dataset

Redundant manipulators, with their higher Degrees of Freedom (DoFs), offer enhanced kinematic performance and versatility, making them suitable for applications like manufacturing, surgical robotics, and human-robot collaboration. However, motion planning for these manipulators is challenging due to increased DoFs and complex, dynamic environments. While traditional motion planning algorithms struggle with high-dimensional spaces, deep learning-based methods often face instability and inefficiency in complex tasks. This paper introduces RobotDiffuse, a diffusion model-based approach for motion planning in redundant manipulators. By integrating physical constraints with a point cloud encoder and replacing the U-Net structure with an encoder-only transformer, RobotDiffuse improves the model's ability to capture temporal dependencies and generate smoother, more coherent motion plans. We validate the approach using a complex simulator and release a new dataset, Robot-obtalcles-panda (ROP), with 35M robot poses and 0.14M obstacle avoidance scenarios. The highest overall score obtained in the experiment demonstrates the effectiveness of RobotDiffuse and the promise of diffusion models for motion planning tasks. The dataset can be accessed at https://github.com/ACRoboT-buaa/RobotDiffuse.

cs.RO↗

arXiv2022

Controlled Quantum Teleportation in the Presence of an Adversary

We present a device independent analysis of controlled quantum teleportation where the receiver is not trusted. We show that the notion of genuine tripartite nonlocality allows us to certify control power in such a scenario. By considering a specific adversarial attack strategy on a device characterized by depolarizing noise, we find that control power is a monotonically increasing function of genuine tripartite nonlocality. These results are relevant for building practical quantum communication networks and also shed light on the role of nonlocality in multipartite quantum information processing.

quant-ph↗

arXiv2022

Deep Learning based Intelligent Coin-tap Test for Defect Recognition

The coin-tap test is a convenient and primary method for non-destructive testing, while its manual on-site operation is tough and costly. With the help of the latest intelligent signal processing method, convolutional neural networks (CNN), we achieve an intelligent coin-tap test which exhibited superior performance in recognizing the defects. However, this success of CNNs relies on plenty of well-labeled data from the identical scenario, which could be difficult to get for many real industrial practices. This paper further develops transfer learning strategies for this issue, that is, to transfer the model trained on data of one scenario to another. In experiments, the result presents a notable improvement by using domain adaptation and pseudo label learning strategies. Hence, it becomes possible to apply the model into scenarios with none or little (less than 10\%) labeled data adopting the transfer learning strategies proposed herein. In addition, we used a benchmark dataset constructed ourselves throughout this study. This benchmark dataset for the coin-tap test containing around 100,000 sound signals is published at https://github.com/PPhub-hy/torch-tapnet.

eess.SP↗

arXiv2022

Local neural operator for solving transient partial differential equations on varied domains

Artificial intelligence (AI) shows great potential to reduce the huge cost of solving partial differential equations (PDEs). However, it is not fully realized in practice as neural networks are defined and trained on fixed domains and boundaries. Herein, we propose local neural operator (LNO) for solving transient PDEs on varied domains. It comes together with a handy strategy including boundary treatments, enabling one pre-trained LNO to predict solutions on different domains. For demonstration, LNO learns Navier-Stokes equations from randomly generated data samples, and then the pre-trained LNO is used as an explicit numerical time-marching scheme to solve the flow of fluid on unseen domains, e.g., the flow in a lid-driven cavity and the flow across the cascade of airfoils. It is about 1000$\times$ faster than the conventional finite element method to calculate the flow across the cascade of airfoils. The solving process with pre-trained LNO achieves great efficiency, with significant potential to accelerate numerical calculations in practice.

cs.LG↗

arXiv2021

Meta-fences: blocking waves utilizing a layer of identical tiny scatters

Wave steering by artificial materials (for example, phononic crystals and acoustic metamaterials) is a fascinating frontier in modern physics and engineering, but suffers from bulky sizes and intractable challenges in fabrication. Here, a sparse layer of identical tiny scatters, which we call meta-fences, is presented with a non-destructive way to omnidirectionally block flexural waves in plates. The underlying mechanism is that the restraining force and moment of the scatter are tuned simultaneously to counter-balance the incident wave. Both our experimental results and numerical analysis have demonstrated that broadband wave sources ranging from 3 to 7 kHz can be segregated from the protected area by the meta-fence. In addition, the meta-fence is further assembled into a waveguide routing with an arbitrary configuration. Compared with previous isolators and waveguides, our meta-fences exhibit absolute advantages in compact size, flexible configuration, and high structural strength. The current scenario sheds light on the design of lightweight-and-strong architectures for vibration control and energy harvesting with a high efficiency, and can be extended to microfluidics, acoustics, seismology and other fields.

physics.app-ph↗

arXiv2020

Steering Flexural Waves by Amplitude-Shift Elastic Metasurfaces

As 2D materials with subwavelength structures, elastic metasurfaces show remarkable abilities to manipulate elastic waves at will through artificial boundary conditions. However, the application prospects of current metasurfaces may be restricted by their phase-only modulating boundaries. Herein, we present the next generation of elastic metasurfaces by additionally incorporating amplitude-shift modulation. A general theory for target wave fields steered by metasurfaces is proposed by modifying the Huygens-Fresnel principle. As examples, two amplitude-shift metasurfaces concerning flexural waves in thin plates are carried out: one is to transform a cylindrical wave into a Gaussian beam by elaborating both amplitude and phase shifts, and the other one is to focus the incidence by amplitude modulations only. These examples coincide well over theoretical calculations, numerical simulations and experimental tests. This work may underlie the design of metasurfaces with complete control over guided elastic waves, and may extend to more sophisticated applications, such as analog signal processing and holographic imaging.

physics.app-ph↗

arXiv2020

A Study on Evaluation Standard for Automatic Crack Detection Regard the Random Fractal

A reasonable evaluation standard underlies construction of effective deep learning models. However, we find in experiments that the automatic crack detectors based on deep learning are obviously underestimated by the widely used mean Average Precision (mAP) standard. This paper presents a study on the evaluation standard. It is clarified that the random fractal of crack disables the mAP standard, because the strict box matching in mAP calculation is unreasonable for the fractal feature. As a solution, a fractal-available evaluation standard named CovEval is proposed to correct the underestimation in crack detection. In CovEval, a different matching process based on the idea of covering box matching is adopted for this issue. In detail, Cover Area rate (CAr) is designed as a covering overlap, and a multi-match strategy is employed to release the one-to-one matching restriction in mAP. Extended Recall (XR), Extended Precision (XP) and Extended F-score (Fext) are defined for scoring the crack detectors. In experiments using several common frameworks for object detection, models get much higher scores in crack detection according to CovEval, which matches better with the visual performance. Moreover, based on faster R-CNN framework, we present a case study to optimize a crack detector based on CovEval standard. Recall (XR) of our best model achieves an industrial-level at 95.8, which implies that with reasonable standard for evaluation, the methods for object detection are with great potential for automatic industrial inspection.

cs.CV↗

arXiv2020

Experimental simulation of the Parity-Time-symmetric dynamics using photonics qubits

The concept of parity-time (PT) symmetry originates from the framework of quantum mechanics, where if the Hamiltonian operator satisfies the commutation relation with the parity and time operators, it shows all real eigen-energy spectrum. Recently, PT symmetry was introduced into optics, electronic circuits, acoustics, and so many other classical fields to further study the dynamics of the Hamiltonian and the energy of the system. Focusing on the dynamical evolution of the quantum state under the action of PT symmetric Hamiltonian, here we experimentally demonstrated the general dynamical evolution of a two-level system under the PT symmetric Hamiltonian using single-photon system. By enlarging the system using ancillary qubits and encoding the subsystem under the non-Hermitian Hamiltonian with post-selection, the evolution of the state can be observed with a high fidelity when the successfully parity-time symmetrically evolved subspace is solely considered. Owing to the effectively operation of the dilation method, our work provides a route for further exploiting the exotic properties of PT symmetric Hamiltonian for quantum simulation and quantum information processing.

quant-ph↗

arXiv2018

Untethered microbot powered by giant magnetoelastic strain

Magnetic elastomers deform under a magnetic field, working as a soft actuator. Untethered microbot made of magnetic elastomers have great potentials in performing medical tasks inside human body as mini doctors. However, the lack of a highly deformable and efficient actuator strongly limits the development of magnetic microbots. In this paper, we developed an actuator of magnetic elastomer capable of large deformation strain by harnessing magnetic pull-in instability. We design three prototypes of untethered microbots by using this actuator: a robot grips objects with a large rotating angle, a robot navigates in a fluid channel and delivers drug, and a robot self-accelerates and quickly jumps up. The pull-in mechanism enables a new type of soft robot with fast response, remote wireless control and extremely high power density.

physics.app-ph↗