arXiv subjects

Ruoyu Li

Publications and source records attributed to Ruoyu Li.

At least 37 records · Page 2Linked to original sources

arXiv2025

Experimental online quantum dots charge autotuning using neural networks

Spin-based semiconductor qubits hold promise for scalable quantum computing, yet they require reliable autonomous calibration procedures. This study presents an experimental demonstration of online single-dot charge autotuning using a convolutional neural network integrated into a closed-loop calibration system. The autotuning algorithm explores the gates' voltage space to localize charge transition lines, thereby isolating the one-electron regime without human intervention. This exploration leverages the model's uncertainty estimation to find the appropriate gate configuration with minimal measurements while reducing the risk of failures. In 20 experimental runs, our method achieved a success rate of 95% in locating the target electron regime, highlighting the robustness of this approach against noise and distribution shifts from the offline training set. Each tuning run lasted an average of 2 hours and 9 minutes, primarily due to the limited speed of the current measurement. This work validates the feasibility of machine learning-driven real-time charge autotuning for quantum dot devices, advancing the development toward the control of large qubit arrays.

cond-mat.mes-hall↗

arXiv2024

Interactive Medical Image Segmentation: A Benchmark Dataset and Baseline

Interactive Medical Image Segmentation (IMIS) has long been constrained by the limited availability of large-scale, diverse, and densely annotated datasets, which hinders model generalization and consistent evaluation across different models. In this paper, we introduce the IMed-361M benchmark dataset, a significant advancement in general IMIS research. First, we collect and standardize over 6.4 million medical images and their corresponding ground truth masks from multiple data sources. Then, leveraging the strong object recognition capabilities of a vision foundational model, we automatically generated dense interactive masks for each image and ensured their quality through rigorous quality control and granularity management. Unlike previous datasets, which are limited by specific modalities or sparse annotations, IMed-361M spans 14 modalities and 204 segmentation targets, totaling 361 million masks-an average of 56 masks per image. Finally, we developed an IMIS baseline network on this dataset that supports high-quality mask generation through interactive inputs, including clicks, bounding boxes, text prompts, and their combinations. We evaluate its performance on medical image segmentation tasks from multiple perspectives, demonstrating superior accuracy and scalability compared to existing interactive segmentation models. To facilitate research on foundational models in medical computer vision, we release the IMed-361M and model at https://github.com/uni-medical/IMIS-Bench.

cs.CV↗

arXiv2024

Probing g-tensor reproducibility and spin-orbit effects in planar silicon hole quantum dots

In this work, we probe the sensitivity of hole-spin properties to hole occupation number in a planar silicon double-quantum dot device fabricated on a 300 mm integrated platform. Using DC transport measurements, we investigate the g-tensor and spin-relaxation induced leakage current within the Pauli spin-blockade regime as a function of magnetic-field orientation at three different hole occupation numbers. We find the g-tensor and spin-leakage current to be highly anisotropic due to light-hole/heavy-hole mixing and spin-orbit mixing, but discover the anisotropies to be relatively insensitive to the dot hole number. Furthermore, we extract the dominant inter-dot spin-orbit coupling mechanism as surface Dresselhaus, with an in-plane orientation parallel to transport and magnitude $\boldsymbol{t_{SO}}$ $\approx$ 300 neV. Finally, we observe a strong correlation between the g-factor difference ($δ$$\boldsymbol{g}$) between each dot and the spin-leakage current anisotropy, as a result of $δ$$\boldsymbol{g}$ providing an additional spin-relaxation pathway, and should be considered. ]Our findings indicate that hole-spin devices are not as sensitive to precise operating conditions as anticipated. This has important implications optimizing spin control and readout based on magnetic-field direction, together with tuning large arrays of QDs as spin-qubits.

cond-mat.mes-hall↗

arXiv2024

LCP: Enhancing Scientific Data Management with Lossy Compression for Particles

Many scientific applications opt for particles instead of meshes as their basic primitives to model complex systems composed of billions of discrete entities. Such applications span a diverse array of scientific domains, including molecular dynamics, cosmology, computational fluid dynamics, and geology. The scale of the particles in those scientific applications increases substantially thanks to the ever-increasing computational power in high-performance computing (HPC) platforms. However, the actual gains from such increases are often undercut by obstacles in data management systems related to data storage, transfer, and processing. Lossy compression has been widely recognized as a promising solution to enhance scientific data management systems regarding such challenges, although most existing compression solutions are tailored for Cartesian grids and thus have sub-optimal results on discrete particle data. In this paper, we introduce LCP, an innovative lossy compressor designed for particle datasets, offering superior compression quality and higher speed than existing compression solutions. Specifically, our contribution is threefold. (1) We propose LCP-S, an error-bound aware block-wise spatial compressor to efficiently reduce particle data size. This approach is universally applicable to particle data across various domains. (2) We develop LCP, a hybrid compression solution for multi-frame particle data, featuring dynamic method selection and parameter optimization. (3) We evaluate our solution alongside eight state-of-the-art alternatives on eight real-world particle datasets from seven distinct domains. The results demonstrate that our solution achieves up to 104% improvement in compression ratios and up to 593% increase in speed compared to the second-best option, under the same error criteria.

cs.DC↗

arXiv2024

Robust quantum dots charge autotuning using neural network uncertainty

This study presents a machine-learning-based procedure to automate the charge tuning of semiconductor spin qubits with minimal human intervention, addressing one of the significant challenges in scaling up quantum dot technologies. This method exploits artificial neural networks to identify noisy transition lines in stability diagrams, guiding a robust exploration strategy leveraging neural networks' uncertainty estimations. Tested across three distinct offline experimental datasets representing different single quantum dot technologies, the approach achieves over 99% tuning success rate in optimal cases, where more than 10% of the success is directly attributable to uncertainty exploitation. The challenging constraints of small training sets containing high diagram-to-diagram variability allowed us to evaluate the capabilities and limits of the proposed procedure.

quant-ph↗

arXiv2024

Industrial 300$\,$mm wafer processed spin qubits in natural silicon/silicon-germanium

The realisation of an universal quantum computer will require the operation of thousands to millions of qubits. The possibility of using existing industrial semiconductor fabrication techniques and infrastructure for up-scaling and reproducibility makes silicon based spin qubits one of the most promising platforms to achieve this goal. The implementation of the up to now largest semiconductor based quantum processor was realized in a silicon/silicon-germanium heterostructure known for its low charge noise, long qubit coherence times and fast driving speeds, but the high structural complexity creates challenges for industrial implementations. Here we demonstrate quantum dots hosted in a natural Si/SiGe heterostructure fully fabricated by an industrial 300$\,$mm semiconductor wafer process line from heterostructure growth to Co micromagnet monolithic integration. We report charge noise values below 2$\,\mathrm{μeV/\sqrt{Hz}}$, spin relaxation times of over 1$\,$s and coherence times $T_2^*$ and $T_2^H$ of 1$\,\mathrm{μs}$ and 50$\,\mathrm{μs}$ respectively, for quantum wells grown using natural silicon. Further, we achieve Rabi frequencies up to 5$\,$MHz and single qubit gate fidelities above 99$\,\%$. In addition to scalability, the high reproducibility of the 300$\,$mm processes enables the deterministic study of qubit metric dependencies on process parameters, which is essential for optimising qubit quality.

cond-mat.mes-hall↗

arXiv2024

CMOS compatibility of semiconductor spin qubits

Several domains of society will be disrupted once millions of high-quality qubits can be brought together to perform fault-tolerant quantum computing (FTQC). All quantum computing hardware available today is many orders of magnitude removed from the requirements for FTQC. The intimidating challenges associated with integrating such complex systems have already been addressed by the semiconductor industry -hence many qubit makers have retrofitted their technology to be CMOS-compatible. This compatibility, however, can have varying degrees ranging from the mere ability to fabricate qubits using a silicon wafer as a substrate, all the way to the co-integration of qubits with high-yield, low-power advanced electronics to control these qubits. Extrapolating the evolution of quantum processors to future systems, semiconductor spin qubits have unique advantages in this respect, making them one of the most serious contenders for large-scale FTQC. In this review, we focus on the overlap between state-of-the-art semiconductor spin qubit systems and CMOS industry Very Large-Scale Integration (VLSI) principles. We identify the main differences in spin qubit operation, material, and system requirements compared to well-established CMOS industry practices. As key players in the field are looking to collaborate with CMOS industry partners, this review serves to accelerate R&D towards the industrial scale production of FTQC processors.

cond-mat.mes-hall↗

arXiv2024

Make Your Home Safe: Time-aware Unsupervised User Behavior Anomaly Detection in Smart Homes via Loss-guided Mask

Smart homes, powered by the Internet of Things, offer great convenience but also pose security concerns due to abnormal behaviors, such as improper operations of users and potential attacks from malicious attackers. Several behavior modeling methods have been proposed to identify abnormal behaviors and mitigate potential risks. However, their performance often falls short because they do not effectively learn less frequent behaviors, consider temporal context, or account for the impact of noise in human behaviors. In this paper, we propose SmartGuard, an autoencoder-based unsupervised user behavior anomaly detection framework. First, we design a Loss-guided Dynamic Mask Strategy (LDMS) to encourage the model to learn less frequent behaviors, which are often overlooked during learning. Second, we propose a Three-level Time-aware Position Embedding (TTPE) to incorporate temporal information into positional embedding to detect temporal context anomaly. Third, we propose a Noise-aware Weighted Reconstruction Loss (NWRL) that assigns different weights for routine behaviors and noise behaviors to mitigate the interference of noise behaviors during inference. Comprehensive experiments on three datasets with ten types of anomaly behaviors demonstrates that SmartGuard consistently outperforms state-of-the-art baselines and also offers highly interpretable results.

cs.CR↗

arXiv2024

DeviceRadar: Online IoT Device Fingerprinting in ISPs using Programmable Switches

Device fingerprinting can be used by Internet Service Providers (ISPs) to identify vulnerable IoT devices for early prevention of threats. However, due to the wide deployment of middleboxes in ISP networks, some important data, e.g., 5-tuples and flow statistics, are often obscured, rendering many existing approaches invalid. It is further challenged by the high-speed traffic of hundreds of terabytes per day in ISP networks. This paper proposes DeviceRadar, an online IoT device fingerprinting framework that achieves accurate, real-time processing in ISPs using programmable switches. We innovatively exploit "key packets" as a basis of fingerprints only using packet sizes and directions, which appear periodically while exhibiting differences across different IoT devices. To utilize them, we propose a packet size embedding model to discover the spatial relationships between packets. Meanwhile, we design an algorithm to extract the "key packets" of each device, and propose an approach that jointly considers the spatial relationships and the key packets to produce a neighboring key packet distribution, which can serve as a feature vector for machine learning models for inference. Last, we design a model transformation method and a feature extraction process to deploy the model on a programmable data plane within its constrained arithmetic operations and memory to achieve line-speed processing. Our experiments show that DeviceRadar can achieve state-of-the-art accuracy across 77 IoT devices with 40 Gbps throughput, and requires only 1.3% of the processing time compared to GPU-accelerated approaches.

cs.NI↗

arXiv2024

Genos: General In-Network Unsupervised Intrusion Detection by Rule Extraction

Anomaly-based network intrusion detection systems (A-NIDS) use unsupervised models to detect unforeseen attacks. However, existing A-NIDS solutions suffer from low throughput, lack of interpretability, and high maintenance costs. Recent in-network intelligence (INI) exploits programmable switches to offer line-rate deployment of NIDS. Nevertheless, current in-network NIDS are either model-specific or only apply to supervised models. In this paper, we propose Genos, a general in-network framework for unsupervised A-NIDS by rule extraction, which consists of a Model Compiler, a Model Interpreter, and a Model Debugger. Specifically, observing benign data are multimodal and usually located in multiple subspaces in the feature space, we utilize a divide-and-conquer approach for model-agnostic rule extraction. In the Model Compiler, we first propose a tree-based clustering algorithm to partition the feature space into subspaces, then design a decision boundary estimation mechanism to approximate the source model in each subspace. The Model Interpreter interprets predictions by important attributes to aid network operators in understanding the predictions. The Model Debugger conducts incremental updating to rectify errors by only fine-tuning rules on affected subspaces, thus reducing maintenance costs. We implement a prototype using physical hardware, and experiments demonstrate its superior performance of 100 Gbps throughput, great interpretability, and trivial updating overhead.

cs.CR↗

arXiv2023

Modelling semiconductor spin qubits and their charge noise environment for quantum gate fidelity estimation

The spin of an electron confined in semiconductor quantum dots is currently a promising candidate for quantum bit (qubit) implementations. Taking advantage of existing CMOS integration technologies, such devices can offer a platform for large scale quantum computation. However, a quantum mechanical framework bridging a device's physical design and operational parameters to the qubit energy space is lacking. Furthermore, the spin to charge coupling introduced by intrinsic or induced Spin-Orbit-Interaction (SOI) exposes the qubits to charge noise compromising their coherence properties and inducing quantum gate errors. We present here a co-modelling framework for double quantum dot (DQD) devices and their charge noise environment. We use a combination of an electrostatic potential solver, full configuration interaction quantum mechanical methods and two-level-fluctuator models to study the quantum gate performance in realistic device designs and operation conditions. We utilize the developed models together alongside the single electron solutions of the quantum dots to simulate one- and two- qubit gates in the presence of charge noise. We find an inverse correlation between quantum gate errors and quantum dot confinement frequencies. We calculate X-gate fidelities >97% in the simulated Si-MOS devices at a typical TLF densities. We also find that exchange driven two-qubit SWAP gates show higher sensitivity to charge noise with fidelities down to 91% in the presence of the same density of TLFs. We further investigate the one- and two- qubit gate fidelities at different TLF densities. We find that given the small size of the quantum dots, sensitivity of a quantum gate to the distance between the noise sources and the quantum dot creates a strong variability in the quantum gate fidelities which can compromise the device yields in scaled qubit technologies.

cond-mat.mes-hall↗

arXiv2023

End-Tethered Chains Increase the Local Glass Transition Temperature of Matrix Chains by 45 K Next to Solid Substrates Independent of Chain Length

The local glass transition temperature Tg of pyrene-labeled polystyrene (PS) chains intermixed with end-tethered PS chains grafted to a neutral silica substrate was measured by fluorescence spectroscopy. To isolate the impact of the grafted chains, the films were capped with bulk neat PS layers eliminating competing effects of the free surface. Results demonstrate that end-grafted chains strongly increase the local Tg of matrix chains by $\approx$45 K relative to bulk Tg, independent of grafted chain molecular weight from Mn = 8.6 to 212 kg/mol and chemical end-group, over a wide range of grafting densities $σ$ = 0.003 to 0.33 chains/nm$^2$ spanning the mushroom-to-brush transition regime. The tens-of-degree increase in local Tg resulting from immobilization of the chain ends by covalent bonding in this athermal system suggests a mechanism that substantially increases the local activation energy required for cooperative rearrangements.

cond-mat.soft↗

arXiv2022

Low charge noise quantum dots with industrial CMOS manufacturing

Silicon spin qubits are among the most promising candidates for large scale quantum computers, due to their excellent coherence and compatibility with CMOS technology for upscaling. Advanced industrial CMOS process flows allow wafer-scale uniformity and high device yield, but off the shelf transistor processes cannot be directly transferred to qubit structures due to the different designs and operation conditions. To therefore leverage the know-how of the micro-electronics industry, we customize a 300mm wafer fabrication line for silicon MOS qubit integration. With careful optimization and engineering of the MOS gate stack, we report stable and uniform quantum dot operation at the Si/SiOx interface at milli-Kelvin temperature. We extract the charge noise in different devices and under various operation conditions, demonstrating a record-low average noise level of 0.61 $μ$eV/${\sqrt{Hz}}$ at 1 Hz and even below 0.1 $μ$eV/${\sqrt{Hz}}$ for some devices and operating conditions. By statistical analysis of the charge noise with different operation and device parameters, we show that the noise source can indeed be well described by a two-level fluctuator model. This reproducible low noise level, in combination with uniform operation of our quantum dots, marks CMOS manufactured MOS spin qubits as a mature and highly scalable platform for high fidelity qubits.

cond-mat.mes-hall↗

arXiv2022

Multi-view Gait Recognition based on Siamese Vision Transformer

While the Vision Transformer has been used in gait recognition, its application in multi-view gait recognition is still limited. Different views significantly affect the extraction and identification accuracy of the characteristics of gait contour. To address this, this paper proposes a Siamese Mobile Vision Transformer (SMViT). This model not only focuses on the local characteristics of the human gait space but also considers the characteristics of long-distance attention associations, which can extract multi-dimensional step status characteristics. In addition, it describes how different perspectives affect gait characteristics and generate reliable perspective feature relationship factors. The average recognition rate of SMViT on the CASIA B data set reached 96.4%. The experimental results show that SMViT can attain state-of-the-art performance compared to advanced step recognition models such as GaitGAN, Multi_view GAN, Posegait and other gait recognition models.

cs.CV↗

arXiv2021

Millikelvin temperature cryo-CMOS multiplexer for scalable quantum device characterisation

Quantum computers based on solid state qubits have been a subject of rapid development in recent years. In current Noisy Intermediate-Scale Quantum (NISQ) technology, each quantum device is controlled and characterised though a dedicated signal line between room temperature and base temperature of a dilution refrigerator. This approach is not scalable and is currently limiting the development of large-scale quantum system integration and quantum device characterisation. Here we demonstrate a custom designed cryo-CMOS multiplexer operating at 32 mK. The multiplexer exhibits excellent microwave properties up to 10 GHz at room and millikelvin temperatures. We have increased the characterisation throughput with the multiplexer by measuring four high-quality factor superconducting resonators using a single input and output line in a dilution refrigerator. Our work lays the foundation for large-scale microwave quantum device characterisation and has the perspective to address the wiring problem of future large-scale quantum computers.

quant-ph↗

arXiv2021

High mobility SiMOSFETs fabricated in a full 300mm CMOS process

The quality of the semiconductor-barrier interface plays a pivotal role in the demonstration of high quality reproducible quantum dots for quantum information processing. In this work, we have measured SiMOSFET Hall bars on undoped Si substrates in order to investigate the quality of the devices fabricated in a full CMOS process. We report a record mobility of 17'500 cm2/Vs with a sub-10 nm oxide thickness indicating a high quality interface, suitable for future qubit applications. We also study the influence of gate materials on the mobilities and discuss the underlying mechanisms, giving insight into further material optimization for large scale quantum processors.

cond-mat.mes-hall↗

arXiv2020

KIT MOMA: A Mobile Machines Dataset

Mobile machines typically working in a closed site, have a high potential to utilize autonomous driving technology. However, vigorously thriving development and innovation are happening mostly in the area of passenger cars. In contrast, although there are also many research pieces about autonomous driving or working in mobile machines, a consensus about the SOTA solution is still not achieved. We believe that the most urgent problem that should be solved is the absence of a public and challenging visual dataset, which makes the results from different researches comparable. To address the problem, we publish the KIT MOMA dataset, including eight classes of commonly used mobile machines, which can be used as a benchmark to evaluate the SOTA algorithms to detect mobile construction machines. The view of the gathered images is outside of the mobile machines since we believe fixed cameras on the ground are more suitable if all the interesting machines are working in a closed site. Most of the images in KIT MOMA are in a real scene, whereas some of the images are from the official website of top construction machine companies. Also, we have evaluated the performance of YOLO v3 on our dataset, indicating that the SOTA computer vision algorithms already show an excellent performance for detecting the mobile machines in a specific working site. Together with the dataset, we also upload the trained weights, which can be directly used by engineers from the construction machine industry. The dataset, trained weights, and updates can be found on our Github. Moreover, the demo can be found on our Youtube.

cs.CV↗

arXiv2020

Relative hyperbolicity for automorphisms of free products

We prove that for a free product $G$ with free factor system $\mathcal{G}$, any automorphism $ϕ$ preserving $\mathcal{G}$, atoroidal (in a sense relative to $\mathcal{G}$) and none of whose power send two different conjugates of subgroups in $\mathcal{G}$ on conjugates of themselves by the same element, gives rise to a semidirect product $G\rtimes_ϕ\mathbb{Z}$ that is relatively hyperbolic with respect to suspensions of groups in $\mathcal{G}$. We recover a theorem of Gautero-Lustig and Ghosh that, if $G$ is a free group, $ϕ$ an automorphism of $G$, and $\mathcal{G}$ is its family of polynomially growing subgroups, then the semidirect product by $ϕ$ is relatively hyperbolic with respect to the suspensions of these subgroups. We apply the first result to the conjugacy problem for certain automorphisms (atoroidal and toral) of free products of abelian groups.

math.GR↗