Quantum Computation And Sensing On Network
Speaker: Yuki Takeuchi, NTT Basic Research Laboratories
Abstract: Quantum computers are expected to outperform modern computers (i.e., classical computers) including supercomputers. From the high expectations, several companies and universities have devoted huge efforts toward the realization of full-fledged quantum computers. On the other hand, due to their physical sizes and the necessity of a cryogenic environment, it should be hard to personally own first-generation full-fledged quantum computers. A possible approach to overcome this issue is blind quantum computation, which is a secure cloud quantum computing protocol. By using it, a client, who would like to solve some hard problem, but his/her quantum capability is weak, can delegate the hard problem to a remote quantum computer without revealing what the answer is. Recently, we have extended it to quantum sensing, i.e., have proposed secure cloud quantum sensing. Quantum sensors also have several advantages compared with modern sensors (i.e., classical sensors) in terms of sensitivity, spatial resolution, and so on. In short, the combination of quantum information processing and network should achieve a society where anyone can enjoy quantum advantages from anywhere.
In this talk, first, I introduce some basic concepts of quantum information processing. Then, I explain the current status of cloud quantum protocols and review our recent theoretical and experimental results.
Biography: Yuki Takeuchi received a Ph.D. in science from Osaka University in 2018. He joined NTT Communication Science Laboratories as a research associate the same year and was a researcher from 2019 to 2023. Since April 2023, he has been an associate distinguished researcher. In October 2023, he also joined NTT Basic Research Laboratories as the same position. He has engaged in the theoretical investigation of quantum information processing. He received IPSJ Computer Science Research Award for Young Scientists from the Information Processing Society of Japan (IPSJ) and Young Scientist Award of the Physical Society of Japan from the Physical Society of Japan. He is a member of IPSJ and the Physical Society of Japan.
From 5G To 6G: Towards A Ubiquitous Network With Intelligent Air Interface
Speaker: Chu-Hsiang Huang, Qualcomm
Abstract: Fifth generation (5G) cellular communication system provides a flexible system architecture that enables wireless communication technology to penetrate different verticals and connect various devices. Since 5G devices began to appear in the market, we have seen connected systems implemented across multiple use cases in different scenarios, e.g., vehicular network, high speed train, broadcast, and even satellite communications. However, emerging application scenarios under different conditions and with diverse objectives bring new challenges to traditional wireless system design methodology, which mainly relies on abstract models to capture the environment accurately and develop algorithms accordingly. Applying machine learning to develop AI models applicable to various application scenarios becomes a promising direction for designing wireless communication systems. Therefore, creating a new generation of machine learning-based intelligent air interface, which can adapt to different conditions, is a key technology for successfully developing and deploying wireless communication systems to cover all the use cases envisioned by 5G, 6G, and beyond. In this tutorial, we start with the flexible system architecture in the 5G standard, and explain how connected systems in different scenarios can be developed from the 5G system architecture. Then we show how machine learning can help to develop AI models in various wireless communication system use cases, and the challenge to design an intelligent air interface leveraging the vast capability of machine learning methodology, to provide a promising vision to the future 6G systems.
- Introduction to 5G (40min)
- Diverse applications of 5G technology part 1: application overview and sidelink (50min)
- Diverse application of 5G technology part 2: high speed train and other applications (30min)
- Intelligent interface: (50min)
- Q&A: (10min)
Biography: Chu-Hsiang Huang received B.S. and M.S. degrees in Electrical Engineering from National Taiwan University, Taiwan in 2007 and 2009, respectively. and his Ph.D. degree in Electrical Engineering from University of California, Los Angeles in 2015. He is now with Qualcomm Technologies, Inc. as a RAN working group delegate for 3GPP Standard Organization. Besides representing Qualcomm in 3GPP standard meetings, he is working on product development projects including multi-user interference mitigation, energy efficient receiver and demodulation algorithm as a staff engineer. He was a research assistant for NTU-INTEL research center in Taiwan in 2010. His research interest includes robust inference on unreliable hardware, probabilistic graphical model and machine learning algorithm.
Recent Developments In Millimeter-Wave Imaging For Short-Range Sensing
Speaker: Stavros Vakalis, University of South Florida
Abstract: Millimeter-wave imaging and sensing are becoming increasingly important for applications such as security screening, wireless health monitoring, and wireless communications. This presentation will discuss recent advancements in millimeter-wave imaging and sensing utilizing hardware-software co-design and novel architectures utilizing incoherent signals and sparse interferometric antenna arrays.
Biography: Stavros Vakalis received the Diploma degree in electrical and computer engineering from the National Technical University of Athens, Athens, Greece, in 2017, and the Ph.D. degree in electrical engineering from Michigan State University in 2022, respectively. In 2022, he joined the Department of Electrical Engineering, University of South Florida, Tampa, FL, USA, as an Assistant Professor. His current research interests include wireless microwave and millimeter-wave systems, millimeter-wave imaging, radar, signal processing, and wireless communications.
Trustworthy Machine Learning Systems Under Adversarial Environments
Speaker: Ning Wang, University of South Florida
Abstract: Modern AI systems, particularly with the rise of big data and deep learning in the last decade, have greatly improved our daily life and at the same time created a long list of controversies. Generative ML models used for art creation have been used by fraudsters to generate deepfakes; well-trained language models have been shown to exhibit generalizability deficiency and intrinsic bias; machine learning (ML) models have been demonstrated to leak sensitive information about the data owners. Meanwhile, AI systems are often subject to malicious and stealthy subversion that jeopardizes their efficacy, represented by model poisoning in the training and adversarial data generation in the testing. It is evident that security, privacy, and robustness have become more important than ever for AI to gain wider adoption and societal trust. This talk will introduce evasion attack, poisoning attack, inference attack, and their corresponding countermeasures.
Biography: Ning Wang is a an assistant professor in the Department of Computer Science and Engineering at University of South Florida. She received her Ph.D degree in Computer Engineering from Virginia Tech in 2023. Her research mainly focuses on trustworthy artificial intelligence (AI), with interests in adversarial machine learning (ML), federated learning, anomaly detection, intrusion detection, differential privacy, and intelligent IoT. Ning has published her research in top venues for computer networking and security, including INFOCOM, ACSAC, AsiaCCS, CNS, MILCOM and TDSC. Her research goal is to solve security and privacy challenges in AI systems and develop ML-based solutions for critical security applications.