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Quantum computing and engineering (QCE), IEEE International Conference on

作者： Yutaro Akahoshi Jun Fujisaki Hirotaka Oshima Shintaro Sato Keisuke Fujii Quantum Laboratory Fujitsu Research Fujitsu Limited Kanagawa Japan Fujitsu Quantum Computing Joint Research Division Center for Quantum Information and Quantum Biology Osaka University Toyonaka Osaka Japan Graduate School of Engineering Science Osaka University Toyonaka Osaka Japan RIKEN Center for Quantum Computing (RQC) Wako Saitama Japan

ISBN: (数字)9798331541378

ISBN: (纸本)9798331541385

Quantum computers hold the promise of revolutionizing several computational tasks by significantly enhancing their calculation speed compared to classical computers. However, there is still a large gap towards fault-tolerant quantum computing (FTQC) due to the large requirement of physical qubits. Under these circumstances, authors proposed a partially fault-tolerant quantum computing architecture that consists of the fault-tolerant Clifford gates and the sufficiently clean analog rotation gate, named “Space-Time efficient Analog Rotation gate quantum computing architecture (STAR architecture).” Although its theoretical foundation is given, we need to develop an efficient software implementation tailored for STAR architecture due to its unique choice of the fundamental gate set. In this study, we construct an integrated software system of STAR-architecture-based quantum computation, which generates physical instructions executable on quantum devices from input logical quantum circuits. The system mainly consists of two parts, a circuit converter and an operation controller. The circuit converter comprises a set of conversion subroutines, which bridge several instruction layers: logical circuit layer, lattice surgery layer, and physical circuit layer. The operation controller efficiently executes the generated instructions. Especially, it enables efficient treatment of the analog rotation gate, which needs real-time scheduling of running instructions due to its probabilistic nature.

关键词： Computers Fault tolerance Quantum computing Fault tolerant systems Stars Quantum mechanics Surgery Computer architecture Logic gates Quantum circuit

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Provably secure fog-based authentication protocol for VANETs

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Computer Networks 2024年 246卷

作者： Awais, Syed Muhammad Yucheng, Wu Mahmood, Khalid Badar, Hafiz Muhammad Sanaullah Kharel, Rupak Das, Ashok Kumar School of Microelectronics and Communication Engineering Chongqing University Chongqing China Graduate School of Intelligent Data Science National Yunlin University of Science and Technology Yunlin Douliu64002 Taiwan Department of Emerging Computing Technologies Emerson University Multan Pakistan School of Computing and Engineering University of Huddersfield HuddersfieldHD1 3DH United Kingdom The Faculty of Electrical and Electronics Engineering Ton Duc Thang University Ho Chi Minh City Viet Nam Center for Security Theory and Algorithmic Research International Institute of Information Technology Hyderabad500 032 India

The integration of fog computing and vehicular ad-hoc networks (VANETs) has become increasingly important due to advancements in cloud computing, Internet of Things (IoT) technologies, and intelligent transportation systems. However, ensuring secure communication in fog-based VANETs poses a significant challenge. To address this challenge, we propose a novel authenticated key agreement protocol that achieves mutual authentication, generates a secure session key for confidential communication, and ensures privacy protection without relying on bilinear pairing. We provide rigorous security proofs for our protocol, specifically tailored for fog-based VANETs, demonstrating its ability to meet their stringent security requirements. Simulations using NS3 allow for a comprehensive performance evaluation, providing insights into the protocol's efficiency and scalability. Additionally, our protocol is highly efficient, featuring low computation and communication costs, thereby offering a practical and scalable solution for secure communication in fog-based VANETs. © 2024

关键词： Fog

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Dense Coding with Locality Restriction on Decoders: Quantum Encoders versus Superquantum Encoders

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PRX Quantum 2022年第3期3卷 030346-030346页

作者： Masahito Hayashi Kun Wang Shenzhen Institute for Quantum Science and Engineering Southern University of Science and Technology Shenzhen 518055 China Guangdong Provincial Key Laboratory of Quantum Science and Engineering Southern University of Science and Technology Shenzhen 518055 China Graduate School of Mathematics Nagoya University Nagoya 464-8602 Japan Institute for Quantum Computing Baidu Research Beijing 100193 China

We investigate practical dense coding by imposing locality restrictions on decoders and symmetry restrictions on encoders within the resource theory of asymmetry framework. In this task, the sender Alice and the helper Fred preshare an entangled state. Alice encodes a classical message into the quantum state using a symmetric preserving encoder and sends the encoded state to Bob through a noiseless quantum channel. The receiver Bob and helper Fred are limited to performing quantum measurements satisfying certain locality restrictions to decode the classical message. We are interested in the ultimate dense coding capacity under these constraints. Our contributions are summarized as follows. First, we derive both one-shot and asymptotic optimal achievable transmission rates of the dense coding task under different encoder and decoder combinations. Surprisingly, our results reveal that the transmission rate cannot be improved even when the decoder is relaxed from one-way local operations and classical communication (LOCC) to two-way LOCC, separable measurements, and partial transpose positive measurements of the bipartite system. Second, depending on the class of allowed decoders with certain locality restrictions, we relax the class of encoding operations to superquantum encoders in the general probability theory framework and derive dense coding transmission rates under this setting. For example, when the decoder is fixed to a separable measurement, theoretically, a positive operation is allowed as an encoding operation. Remarkably, even under this superquantum relaxation, the transmission rate still cannot be lifted. This fact highlights the universal validity of our analysis on practical dense coding beyond quantum theory.

关键词： Quantum channels Quantum communication Quantum entanglement Resource theories

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Structured Multi-Track Accompaniment Arrangement via Style Prior Modelling

arXiv

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arXiv 2023年

作者： Zhao, Jingwei Xia, Gus Wang, Ziyu Wang, Ye Institute of Data Science NUS Singapore School of Computing NUS Singapore Integrative Sciences and Engineering Programme NUS Graduate School Singapore Machine Learning Department MBZUAI United Arab Emirates Computer Science Department NYU Shanghai China

In the realm of music AI, arranging rich and structured multi-track accompaniments from a simple lead sheet presents significant challenges. Such challenges include maintaining track cohesion, ensuring long-term coherence, and optimizing computational efficiency. In this paper, we introduce a novel system that leverages prior modelling over disentangled style factors to address these challenges. Our method presents a two-stage process: initially, a piano arrangement is derived from the lead sheet by retrieving piano texture styles;subsequently, a multi-track orchestration is generated by infusing orchestral function styles into the piano arrangement. Our key design is the use of vector quantization and a unique multi-stream Transformer to model the long-term flow of the orchestration style, which enables flexible, controllable, and structured music generation. Experiments show that by factorizing the arrangement task into interpretable sub-stages, our approach enhances generative capacity while improving efficiency. Additionally, our system supports a variety of music genres and provides style control at different composition hierarchies. We further show that our system achieves superior coherence, structure, and overall arrangement quality compared to existing baselines. © 2023, CC BY.

关键词： Vector quantization

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Scaling with MgO thickness of the proximity exchange field in a Fe/MgO/Si contact

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Physical Review B 2023年第2期107卷 024411-024411页

作者： R. Jansen A. Spiesser Y. Fujita H. Saito K. Hamaya S. Yuasa Research Center for Emerging Computing Technologies National Institute of Advanced Industrial Science and Technology (AIST) Tsukuba Ibaraki 305-8568 Japan Center for Spintronics Research Network Graduate School of Engineering Science Osaka University Toyonaka 560-8531 Japan Research Center for Magnetic and Spintronic Materials National Institute for Materials Science (NIMS) Tsukuba Ibaraki 305-0047 Japan

The scaling of the proximity exchange field, which ferromagnetic Fe exerts on the spin accumulation at the MgO/Si interface of Fe/MgO/Si tunnel contacts, was investigated using the inverted Hanle effect. Data as a function of the applied bias voltage shows that the exchange field varies with bias in the same way as the square of the tunnel spin polarization (TSP). The decay of the exchange field with increasing MgO thickness tMgO is rather weak and nonexponential, which we argue to be related to the improved epitaxial quality of the MgO, and the, consequently, improved spin-filtering properties at larger MgO thickness. Using the TSP data to correct for this yields an exponential decay of the exchange field with tMgO with a decay length of λ=−0.42 nm. Comparing this to the decay length for the tunnel conductance (λ=−0.21 nm) suggests that the exchange interaction is dominated by two-step tunneling processes.

关键词： Exchange interaction Spin accumulation Spin current Spintronics Interfaces Magnetic tunnel junctions Tunnel junctions

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Fusion of Mixture of Experts and Generative Artificial Intelligence in Mobile Edge Metaverse

arXiv

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arXiv 2024年

作者： Liu, Guangyuan Du, Hongyang Niyato, Dusit Kang, Jiawen Xiong, Zehui Jamalipour, Abbas Mao, Shiwen Kim, Dong In The College of Computing and Data Science The Energy Research Institute @ NTU Interdisciplinary Graduate Program Nanyang Technological University Singapore The College of Computing and Data Science Nanyang Technological University Singapore The School of Automation Guangdong University of Technology China The Pillar of Information Systems Technology and Design Singapore University of Technology and Design Singapore The Department of Electrical and Computer Engineering Auburn University Auburn United States The School of Electrical and Computer Engineering University of Sydney Australia The Department of Electrical and Computer Engineering Sungkyunkwan University Korea Republic of

In the digital transformation era, Metaverse offers a fusion of virtual reality (VR), augmented reality (AR), and web technologies to create immersive digital experiences. However, the evolution of the Metaverse is slowed down by the challenges of content creation, scalability, and dynamic user interaction. Our study investigates an integration of Mixture of Experts (MoE) models with Generative Artificial Intelligence (GAI) for mobile edge computing to revolutionize content creation and interaction in the Metaverse. Specifically, we harness an MoE model's ability to efficiently manage complex data and complex tasks by dynamically selecting the most relevant experts running various sub-models to enhance the capabilities of GAI. We then present a novel framework that improves video content generation quality and consistency, and demonstrate its application through case studies. Our findings underscore the efficacy of MoE and GAI integration to redefine virtual experiences by offering a scalable, efficient pathway to harvest the Metaverse's full potential. © 2024, CC BY.

关键词： Augmented reality

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Predicting Microsatellite Instability from Histology Images with Dilated Neighborhood Attention Transformer in Colorectal Cancer

Predicting Microsatellite Instability from Histology Images ...

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IEEE Symposium on Bioinformatics and Bioengineering (BIBE)

作者： Yun-Chi Chen Tung Chao Wimaya Nitya Phandita Tzu-Yu Sun Huai-Zhi Wang Yi-Hsien Hsieh Le-Yin Hsu Che Lin Graduate Institute of Biomedical Electronics and Bioinformatics National Taiwan University (NTU) Taipei Taiwan College of Bioresources and Agriculture NTU Taipei Taiwan Department of Computer Science and Information Engineering National Taiwan University of Science and Technology Taipei Taiwan Graduate Institute of Communication Engineering NTU Taipei Taiwan Data Science Degree Program NTU Taipei Taiwan Department of Electrical Engineering NTU Taipei Taiwan Center for Advanced Computing and Imaging in Biomedicine NTU Taipei Taiwan Smart Medicine and Health Informatics Program NTU Taipei Taiwan

ISBN: (数字)9798331518622

ISBN: (纸本)9798331518639

Microsatellite instability (MSI) is a pivotal genetic marker influencing the efficacy of immunotherapy in colorectal cancer. Traditional MSI examination often requires additional genetic or immunohistochemical tests, whereas histology images, widely available in colorectal cancer diagnosis, offer a valuable alternative for MSI prediction. Although Transformer-based models have demonstrated promising outcomes in predicting MSI from histology images, they are hampered by traditional local attention mechanisms that struggle to capture long-range interdependencies and establish a comprehensive global receptive field. In this study, we introduce DiNAT-MSI, a novel framework for histology-based MSI prediction that incorporates the Dilated Neighborhood Attention Transformer (DiNAT). This model enhances global context recognition and substantially expands receptive fields, all without additional computational burden. Our results demonstrate that DiNAT-MSI achieves a superior patientwise AUROC compared to ResNet18 and Swin Transformer, along with commendable explainability. Our work not only illustrates a more accessible diagnostic tool for leveraging histological data but also underscores the potential of Transformerbased models with sophisticated attention designs in advancing precision medicine for colorectal cancer patients.

关键词： Attention mechanisms Histopathology Computational modeling Precision medicine Small satellites Immunotherapy Colorectal cancer Predictive models Transformers Genetics

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A resource-efficient quantum-classical hybrid algorithm for energy gap evaluation

arXiv

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arXiv 2023年

作者： Yang, Yongdan Li, Ying Xu, Xiaosi Yuan, Xiao Graduate School of China Academy of Engineering Physics Beijing100193 China Center on Frontiers of Computing Studies Peking University Beijing100871 China School of Computer Science Peking University Beijing100871 China

Estimating the eigenvalue or energy gap of a Hamiltonian H is vital for studying quantum many-body systems. Particularly, many of the problems in quantum chemistry, condensed matter physics and nuclear physics investigate the energy gap between two eigenstates. Hence, how to efficiently solve the energy gap becomes an important motive for researching new quantum algorithms. In this work, we propose a hybrid non-variational quantum algorithm that uses the Monte Carlo method and real-time Hamiltonian simulation to evaluate the energy gap of a general quantum many-body system. Compared to conventional approaches, our algorithm does not require controlled real-time evolution, thus making its implementation much more experimental friendly. Since our algorithm is non-variational, it is also free from the "barren plateaus" problem. To verify the efficiency of our algorithm, we conduct numerical simulations for the Heisenberg model and molecule systems on a classical emulator. Copyright © 2023, The Authors. All rights reserved.

关键词： Energy gap

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Model Ownership Protection for Healthcare Consumer Electronics in Federated Edge Learning

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IEEE Transactions on Consumer Electronics 2024年

作者： Wang, Rong Liang, Junchuan Feng, Chaosheng Chakraborty, Chinmay Alqahtani, Fayez Yu, Keping Sichuan Normal University College of Computer Science Chengdu610101 China Sichuan Normal University Visual Computing and Virtual Reality Key Laboratory of Sichuan Chengdu610068 China Birla Institute of Technology Mesra India King Saud University College of Computer and Information Sciences Software Engineering Department Riyadh12372 Saudi Arabia Hosei University Graduate School of Science and Engineering Tokyo184-8584 Japan

With the rising demand for intelligent services and privacy protection in consumer artificial intelligence (AI), federated edge learning has emerged as a beacon for privacy-preserving distributed machine learning. This non-centralized approach offers distinct data privacy advantages, making it especially suitable for applications with strict privacy policies, such as consumer-centric healthcare systems. However, consumer electronics/devices in federated edge learning might unlawfully violate model ownership by duplicating or distributing models. Another concern is that a central server, if compromised, can become a conduit for model leakage, whether for profit motives or due to external intrusions, thus exacerbating the model ownership violation. To our knowledge, few works have focused on model ownership protection for federated edge learning in the consumer-centric healthcare domain. In this paper, we introduce a federated model ownership protection (FedMOP) approach. FedMOP is a model ownership safeguard and a formidable fortress built upon two core innovations. First, FedMOP can swiftly embed and replace watermarks during local training, allowing for the accurate identification and subsequent flagging of deceitful consumer devices. Second, FedMOP ensures that the server only accesses encrypted versions of the federated model, keeping the model a secret. Extensive evaluations demonstrate the robustness and effectiveness of FedMOP. We hope that FedMOP can light the way for surmounting similar ethical challenges in the broader realm of consumer-edge AI. © 1975-2011 IEEE.

关键词： ethical AI Federated edge learning healthcare consumer electronicss model ownership protection

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Generative Self-Supervised Learning for Cyberattack-Resilient EV Charging Demand Forecasting

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IEEE Transactions on Intelligent Transportation Systems 2025年

作者： Li, Duo Leung, Man-Fai Tang, Junqing Wang, Yonggang Hu, Jia Wang, Sheng Newcastle University Department of Civil and Geospatial Engineering Newcastle upon TyneNE1 7RU United Kingdom Anglia Ruskin University School of Computing and Information Science Faculty of Science and Engineering CambridgeCB1 1PT United Kingdom Peking University School of Urban Planning and Design Shenzhen Graduate School Shenzhen518055 China Chang'an University School of Transportation Engineering Xi'an710064 China Tongji University College of Transportation Engineering Shanghai201804 China

The Electric Vehicle (EV) market is experiencing unprecedented growth. Accurate prediction of EV charging demand is essential for transportation system operations, such as real-time traffic management, route optimization, and station utilization planning. However, Cyber threats can compromise the accuracy of charging demand predictions, leading to significant disruptions in transportation services, e.g., suboptimal station management, unexpected congestion at charging facilities, and degraded service quality for EV users. This study introduces Generative Multi-task Self-supervised Learning for Prediction (GenS2-P), a cyberattack-resilient framework designed to ensure reliable charging demand predictions under adversarial conditions. GenS2-P incorporates a Denoising/Reconstruction AutoEncoder (DRAE) and a spatio-temporal prediction model to tackle the dual challenges of data poisoning and DoS attacks. By leveraging generative self-supervised learning and multi-task learning, GenS2-P effectively extracts spatio-temporal patterns to denoise and reconstruct data corrupted by cyberattacks. Experimental evaluations using real-world EV charging data demonstrate that GenS2-P significantly reduces prediction errors and mitigates cyberattack-induced disruptions. This improved prediction reliability enables more effective charging infrastructure management and supports robust transportation system operations even under adverse conditions. © 2000-2011 IEEE.

关键词： Self-supervised learning

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