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Correcting on-chip distortion of control pulses with silicon spin qubits

Chinese Physics B 2025年第1期34卷 265-271页

作者： Ming Ni Rong-Long Ma Zhen-Zhen Kong Ning Chu Wei-Zhu Liao Sheng-Kai Zhu Chu Wang Gang Luo Di Liu Gang Cao Gui-Lei Wang Hai-Ou Li Guo-Ping Guo CAS Key Laboratory of Quantum Information University of Science and Technology of China(USTC)Hefei 230026China CAS Center for Excellence in Quantum Information and Quantum Physics University of Science and Technology of ChinaHefei 230026China Integrated Circuit Advanced Process Research and Development Center Institute of MicroelectronicsChinese Academy of Sciences(CAS)Beijing 100029China Hefei National Laboratory Hefei 230088China Beijing Superstring Academy of Memory Technology Beijing 100176China Origin Quantum Computing Company Limited Hefei 230026China

In semiconductor quantum dot systems,pulse distortion is a significant source of coherent errors,which impedes qubit characterization and ***,we demonstrate two calibration methods using a two-qubit system as the detector to correct distortion and calibrate the transfer function of the control *** methods are straightforward to implement,robust against noise,and applicable to a wide range of qubit *** two methods differ in correction accuracy and *** first,coarse predistortion(CPD)method,partially mitigates *** second,all predistortion(APD)method,measures the transfer function and significantly enhances exchange oscillation *** methods use exchange oscillation homogeneity as the metric and are suitable for any qubit driven by a diabatic *** believe these methods will enhance qubit characterization accuracy and operation quality in future applications.

关键词： quantum computation quantum dot pulse distortion

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A Bitcoin Address Multi-Classification Mechanism Based on Bipartite Graph-Based Maximization Consensus

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Computer Modeling in Engineering & Sciences 2024年第4期139卷 783-800页

作者： Lejun Zhang Junjie Zhang Kentaroh Toyoda Yuan Liu Jing Qiu Zhihong Tian Ran Guo College of Information Engineering Yangzhou UniversityYangzhou225127China Cyberspace Institute Advanced Technology Guangzhou UniversityGuangzhou510006China Research and Development Center for E-Learning Ministry of EducationBeijing100039China Institute of High Performance Computing(IHPC) Agency for ScienceTechnology and Research(A∗STAR)Singapore138632Singapore School of Physics and Materials Science Guangzhou UniversityGuangzhou510006China

Bitcoin is widely used as the most classic electronic currency for various electronic services such as exchanges,gambling,marketplaces,and also scams such as high-yield investment *** the services operated by a Bitcoin address can help determine the risk level of that address and build an alert model *** engineering can also be used to flesh out labeled addresses and to analyze the current state of Bitcoin in a small *** this paper,we address the problem of identifying multiple classes of Bitcoin services,and for the poor classification of individual addresses that do not have significant features,we propose a Bitcoin address identification scheme based on joint multi-model prediction using the mapping relationship between addresses and *** innovation of the method is to(1)Extract as many valuable features as possible when an address is given to facilitate the multi-class service identification task.(2)Unlike the general supervised model approach,this paper proposes a joint prediction scheme for multiple learners based on address-entity mapping ***,after obtaining the overall features,the address classification and entity clustering tasks are performed separately,and the results are subjected to graph-basedmaximization *** final result ismade to baseline the individual address classification results while satisfying the constraint of having similarly behaving entities as far as *** testing and evaluating over 26,000 Bitcoin addresses,our feature extraction method captures more useful *** addition,the combined multi-learner model obtained results that exceeded the baseline classifier reaching an accuracy of 77.4%.

关键词： Bitcoin multi-service classification graph maximization consensus data security

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Secure Multi-party kNN Search in Large-scale Spatial Data Federation

Secure Multi-party kNN Search in Large-scale Spatial Data Fe...

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2022 IEEE International Conference on Big Data, Big Data 2022

作者： Zhang, Yuanyuan Shi, Yexuan Zhou, Nan Xu, Yi Xu, Ke Beihang University State Key Laboratory of Software Development Environment Beijing Advanced Innovation Center for Future Blockchain and Privacy Computing Beijing China

ISBN: (纸本)9781665480451

kNN is a fundamental query in various location based services such as POI recommendation and ride planning. There is an increasing demand to scale such services by querying over a data federation, where the entire dataset is distributedly held by multiple data providers (a.k.a., silos), and each silo keeps its data partition private. However, it is challenging to provide secure kNN queries over a large-scale data federation. Prior secure kNN queries can be are highly inefficient if performed cross silos because they involve excessive secure distance operations, which can be two or three orders of magnitude slower than the corresponding plaintext operations. In this work, we propose a novel threshold based framework for efficient kNN queries over a spatial data federation. The key idea is to rewrite excessive secure distance computations as light-weight secure operations. We further propose an adaptive threshold algorithm to reduce the secure communication rounds and accelerate the query processing. Extensive evaluations on both synthetic and real-world datasets show that compared with the state-of-the-art secure kNN querying methods, our solutions reduce the time cost by up to 104.1 times and communication cost by three orders of magnitude. © 2022 IEEE.

关键词： Location based services

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Towards optimized tensor code generation for deep learning on sunway many-core processor

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Frontiers of Computer Science 2024年第2期18卷 1-15页

作者： Mingzhen LI Changxi LIU Jianjin LIAO Xuegui ZHENG Hailong YANG Rujun SUN Jun XU Lin GAN Guangwen YANG Zhongzhi LUAN Depei QIAN State Key Laboratory of Software Development Environment Beijing 100191China School of Computer Science and Engineering Beihang UniversityBeijing 100191China National University of Singapore Singapore 119077Singapore State Key Laboratory of Mathematical Engineering and Advanced Computing Wuxi 214000China Science and Technology on Special System Simulation Laboratory Beijing Simulation Center Beijing 100854China Department of Computer Science and Technology Tsinghua UniversityBeijing 100084China

The flourish of deep learning frameworks and hardware platforms has been demanding an efficient compiler that can shield the diversity in both software and hardware in order to provide application *** the existing deep learning compilers,TVM is well known for its efficiency in code generation and optimization across diverse hardware *** the meanwhile,the Sunway many-core processor renders itself as a competitive candidate for its attractive computational power in both scientific computing and deep learning *** paper combines the trends in these two ***,we propose swTVM that extends the original TVM to support ahead-of-time compilation for architecture requiring cross-compilation such as *** addition,we leverage the architecture features during the compilation such as core group for massive parallelism,DMA for high bandwidth memory transfer and local device memory for data locality,in order to generate efficient codes for deep learning workloads on *** experiment results show that the codes generated by swTVM achieve 1.79x improvement of inference latency on average compared to the state-of-the-art deep learning framework on Sunway,across eight representative *** work is the first attempt from the compiler perspective to bridge the gap of deep learning and Sunway processor particularly with productivity and efficiency in *** believe this work will encourage more people to embrace the power of deep learning and Sunwaymany-coreprocessor.

关键词： sunway processor deep learning compiler code generation performance optimization

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Silicon wafer defect pattern detection using machine learning

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Materials Today: Proceedings 2023年

作者： Jariya, Mayank Kumar, Parveen Devi, Rekha Singh, Balwinder Center for Development of Advanced Computing Punjab Mohali160 071 India Chandigarh University Punjab Mohali140 413 India

A silicon integrated circuit has been created on silicon wafers by diffusing doping materials on the surface of the same. The silicon wafer is a thin disk made from semiconductor material (mostly ultrapure silicon). Silicon chip fabrication is an industrial process;hence defects occur on silicon wafers caused by unclean factory environment, production machine errors and fabrication process errors. The accurate classification of wafer defects provides information for engineers through regular patterns in the processes. Manual inspection of every chip is not economically feasible. In this study, WM-811k is used as the dataset. This is one of the most extensive wafer bin map dataset available open source on kaggle. It was created by MIRLABS. The dataset was compiled by taking the test data on 811,457 silicon wafer, in which each wafer map image was collected from real-world fabrication. A CNN and DNN model is used to classify the defect patterns. © 2023

关键词： Deep neural networks

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A multilayer deep learning framework for auto-content tagging 5th

A multilayer deep learning framework for auto-content taggin...

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5th International Conference on advanced Machine Learning Technologies and Applications, AMLTA 2020

作者： Jain, Shikha Jain, Ajit Kumar Singh, Shashi Pal Banasthali Vidyapith BanasthaliRajasthan India AAI Center for Development of Advanced Computing Pune India

ISBN: (纸本)9789811533822

With the rapid and recent development of Internet of things (IoT), Bigdata, the most fundamental challenge is to explore the large volume of data from heterogeneous data sources (logs, audio, video, reports, news, social media, etc.). The tools and techniques that are being used in successful transforming of structured data into business intelligence simply do not work when it comes to unstructured data. An estimated 80% of data held by firms today are unstructured. We have powerful computations tools such as graphics processor units (GPUs) for complex calculations and Hadoop, spark for Bigdata management still technology is lacking in complete utilization of available data. Deep learning is a powerful tool to resolve the issues related to data, such as automatic extraction of complex data and representation of large volume unsupervised and uncategorized raw data. But extracted data do not have any standard format. In the survey of Bigdata mining system and IoT systems, it has been found that a layer is needed in Bigdata mining framework that can work on structured, unstructured and semi-structured data and can integrate the complete raw data. The proposed framework is a multilayer deep learning model that performed the clustering of unstructured huge text data followed by classification layer, CNN, to extract features and classify the raw data clusters in parallel manner. Conversion of unstructured data in standard format by automates content tagging helps decision-making systems to easily identify the related data and increase the data utilization. © Springer Nature Singapore Pte Ltd 2021.

关键词： Multilayers

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Quantum Secure Key Management & Delivery Protocol in the QKD framework

Quantum Secure Key Management & Delivery Protocol in the QKD...

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India Council International Subsections Conference (INDISCON), IEEE

作者： Manjiri Harmalkar Kurunandan Jain K B Aneesh Kumar Prabhakar Krishnan Amrita Center for Cybersecurity Systems and Networks Amrita Vishwa Vidyapeetham Amritapuri India Cyber Security Group Center for Development of Advanced Computing Thiruvanathapuram Kerala India

ISBN: (数字)9798350376753

ISBN: (纸本)9798350376760

The current Quantum Key Distribution (QKD) key delivery API relies on HTTPS for secure communication. It is vulnerable to quantum computer threats due to its use of public key cryptography. To mitigate insider attacks, leveraging quantum computing threats within the security boundary and encrypting communication between the application and the KMS is essential. This paper focuses on simulating a 2-node QKD topology using the DARPAN Application. Additionally, we conduct tests on QKD KMS APIs with the Postman tool to ensure consistent retrieval of shared keys at both communication endpoints. Through our simulations and tests, we aim to validate the effectiveness and reliability of the proposed approach in securely managing cryptographic keys within the QKD-network.

关键词： Quantum computing Public key cryptography HTTP Topology Quantum key distribution Cryptography Reliability Optimization Quantum communication Testing

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Software Defined Network Based Error Correction Mechanism in Quantum Key Distribution

Software Defined Network Based Error Correction Mechanism in...

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India Council International Subsections Conference (INDISCON), IEEE

作者： Rasha Shajahan Kurunandan Jain K B Aneesh Kumar Prabhakar Krishnan Center for Cybersecurity Systems and Networks Amrita Vishwa Vidyapeetham Amritapuri-campus India Cyber Security Group Center for Development of Advanced Computing Thiruvanathapuram Kerala India

ISBN: (数字)9798350376753

ISBN: (纸本)9798350376760

Quantum Key Distribution (QKD) is a secure communication method that uses quantum mechanics to exchange secret keys. Currently, the QKD process uses a static error correction method, which can lead to inaccuracies and inefficient resource use. The project aims to develop a new QKD error correction method that adapts dynamically based on real-time Quantum Bit Error Rate (QBER) measurements and Software Defined Networking (SDN) principles. Two specific error correction mechanisms, the 2D Parity check and the Cascade Protocol, will be used to match the QBER value. The investigation will be conducted using the DARPAN application, providing a quantum-enabled environment.

关键词： Quantum computing Bit error rate Quantum channels Receivers Parity check codes Real-time systems Error correction Quantum key distribution Software measurement Software defined networking

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Simulation and Operation Analysis of a Smart Grid Using Simulink 14th

Simulation and Operation Analysis of a Smart Grid Using Simu...

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14th International conference of the IMACS TC1 Committee, ELECTRIMACS 2022

作者： Van Waeyenberge, Alexander Canizes, Bruno Soares, João Ramos, Sérgio Ravyts, Simon Chavez, Juliana Vale, Zita GECAD Research Center Polytechnic of Porto Porto Portugal GECAD Research Group on Intelligent Engineering and Computing for Advanced Innovation and Development LASI—Intelligent Systems Associate Laboratory Polytechnic of Porto Porto Portugal ESAT ELECTA KU Leuven Ghent Belgium

ISBN: (纸本)9783031248368

Changes will be required to handle the increased power flow in the network as the distribution infrastructure ages and the number of EVs and renewable grows. Designing and operating an intelligent network that reacts to changing power flows to ensure the optimal operation is the most economical option than fortifying the network with heavier cables. This work aims to build a model of a 13 bus medium voltage distribution network with high penetration of distributed energy resources and use it to analyze network conditions. The MATLAB Simulink software is used to model and evaluate the network. The outcomes suggest the model is promising and valid even when renewable generation is at low levels. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Smart power grids

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EEG-Based Eye Blink Detection and Interpretation for Human-Computer Interaction and Communication

EEG-Based Eye Blink Detection and Interpretation for Human-C...

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Brain Computer Interface & Healthcare Technologies (iCon-BCIHT), International Conference on

作者： SS Deepu P. Chithra Praveen Panicker PS Suboodh PJ Binu MR Dhanya V Anusree MS Archana BK Athira Health and Technology Group Center for Development of Advanced Computing Thiruvanathapuram Kerala India Department of Neurology Government Medical College Thiruvananthapuram Kerala State India

ISBN: (数字)9798331540067

ISBN: (纸本)9798331540074

This research develops a real-time Brain-Computer Interface (BCI) for interpreting eye blinks as a means of hands-free communication, particularly useful for individuals with limited mobility or in high-focus tasks. Combining machine learning (ML) and signal processing, the study detects voluntary eye blinks with high accuracy. Data from 20 participants were analyzed using a random forest classifier, achieving accuracy between 85.29% and 92.15%. A peak detection method further improved blink detection accuracy to over 98%. This system enhances accessibility in various interactive environments, improving life quality for those with disabilities.

关键词： Human computer interaction Computers Accuracy Medical services Signal processing Brain-computer interfaces Real-time systems Noise measurement Random forests

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