Zebo Yang

I am an Assistant Professor in the Department of Electrical Engineering and Computer Science at Florida Atlantic University. I received my Ph.D. in Computer Science from Washington University in St. Louis in May 2025, advised by Dr. Raj Jain. My research interests include quantum computing, quantum optimization, networking, and machine learning. My current work focuses on advancing scalable quantum computation through distributed quantum computing and quantum networks. I have also worked on network performance and decentralization, with an emphasis on resource management and routing, and applications of blockchain systems and artificial intelligence.

Prior to this, I worked as a Senior Software Engineer at companies such as Tencent, Baidu, and DJI between 2011 and 2017. I received my bachelor degree in computer engineering from Guangdong University of Foreign Studies, Guangzhou, China in 2012 and my master degree in computer engineering from Waseda University, Tokyo, Japan in 2019.

We are hiring self-motivated Ph.D., Master’s, and undergraduate students. If you’re interested, feel free to send me your resume.

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Selected Publications

2025

Layer-Wise Security Framework and Analysis for the Quantum Internet

Zebo Yang, Ali Ghubaish, Raj Jain, and 5 more authors

IEEE Journal on Selected Areas in Communication, 2025

Abstract arXiv

With its significant security potential, the quantum internet is poised to revolutionize technologies like cryptography and communications. Although it boasts enhanced security over traditional networks, the quantum internet still encounters unique security challenges essential for safeguarding its Confidentiality, Integrity, and Availability (CIA). This study explores these challenges by analyzing the vulnerabilities and the corresponding mitigation strategies across different layers of the quantum internet, including physical, link, network, and application layers. We assess the severity of potential attacks, evaluate the expected effectiveness of mitigation strategies, and identify vulnerabilities within diverse network configurations, integrating both classical and quantum approaches. Our research highlights the dynamic nature of these security issues and emphasizes the necessity for adaptive security measures. The findings underline the need for ongoing research into the security dimension of the quantum internet to ensure its robustness, encourage its adoption, and maximize its impact on society.

2024

Asynchronous Entanglement Routing for the Quantum Internet

Zebo Yang, Ali Ghubaish, Raj Jain, and 2 more authors

AVS Quantum Science, 2024

Abstract arXiv HTML

With the emergence of the Quantum Internet, the need for advanced quantum networking techniques has significantly risen. Various models of quantum repeaters have been presented, each delineating a unique strategy to ensure quantum communication over long distances. We focus on repeaters that employ entanglement generation and swapping. This revolves around establishing remote end-to-end entanglement through repeaters, a concept we denote as the "quantum-native" repeaters (also called "first-generation" repeaters in some literature). The challenges in routing with quantum-native repeaters arise from probabilistic entanglement generation and restricted coherence time. Current approaches use synchronized time slots to search for entanglement-swapping paths, resulting in inefficiencies. Here, we propose a new set of asynchronous routing protocols for quantum networks by incorporating the idea of maintaining a dynamic topology in a distributed manner, which has been extensively studied in classical routing for lossy networks, such as using a destination-oriented directed acyclic graph (DODAG) or a spanning tree. The protocols update the entanglement-link topology asynchronously, identify optimal entanglement-swapping paths, and preserve unused direct-link entanglements. Our results indicate that asynchronous protocols achieve a larger upper bound with an appropriate setting and significantly higher entanglement rate than existing synchronous approaches, and the rate increases with coherence time, suggesting that it will have a much more profound impact on quantum networks as technology advances.
Multi-tree Quantum Routing in Realistic Topologies

Zebo Yang, Ali Ghubaish, Raj Jain, and 2 more authors

IEEE Communications Magazine, 2024

Abstract arXiv HTML

In entanglement distribution networks, communication between two nodes necessitates the generation of end-to-end entanglement by entanglement swapping at intermediate nodes. Efficiently creating end-to-end entanglements over long distances is a key objective. In our prior study on asynchronous routing, we enhanced these entanglement rates by leveraging solely the local knowledge of the node’s entanglement links. This was achieved by creating a tree structure, particularly a destination-oriented directed acyclic graph (DODAG) or a spanning tree, eliminating synchronous operations and conserving unused entanglement links. In this article, we present a multi-tree approach with multiple DODAGs designed to improve end-to-end entanglement rates in large-scale networks, specifically catering to a range of network topologies, including grids and barbells, as well as realistic topologies found in research testbeds like ESnet and Internet2. Our simulations show a marked improvement in end-to-end entanglement rates for specific topologies compared to the single-tree method. This study underscores the promise of asynchronous routing schemes in quantum networks, highlighting the effectiveness of asynchronous routing across different network topologies and proposing a superior routing tactic.
HDRL-IDS: A Hybrid Deep Reinforcement Learning Intrusion Detection System for Enhancing the Security of Medical Applications in 5G Networks

Ali Ghubaish, Zebo Yang, and Raj Jain

In 2024 International Conference on Smart Applications, Communications and Networking (Best Paper Award) , 2024

Abstract HTML

The evolution of next-generation wireless networks, particularly the integration of Multi-access Edge Computing (MEC) in 5G, is set to revolutionize the infrastructure of secure systems. This is exemplified in the Internet of Medical Things (IoMT) field, where benefits such as remote surgeries and diagnostics become increasingly common, especially in pandemic scenarios. However, incorporating MEC services into the 5G framework significantly enlarges the network’s vulnerability to traditional security breaches and introduces new, sophisticated types of attacks. These emerging threats, often undetectable by conventional methods, necessitate the development of an adaptive Intrusion Detection System (IDS) capable of identifying such complex security challenges with minimal human intervention. To tackle these issues, this paper introduces a novel Hybrid Deep Reinforcement Learning IDS (HDRL-IDS), rooted in an actor-critic methodology, designed to detect more complex security threats adaptively and autonomously with limited human oversight. Our HDRL-IDS combines the analysis of both network and host features, effectively leveraging the advantages of both Network IDS (NIDS) and Host IDS (HIDS). Empirical results indicate that our HDRL-IDS outperforms traditional NIDS and HIDS in threat detection efficacy. Furthermore, we present a novel dataset derived from an emulated 5G testbed with integrated MEC services to facilitate advanced research and development in the field, specifically for applications designed to address intricate attacks and scenarios demanding high reliability.

2023

A Survey and Comparison of Post-Quantum and Quantum Blockchains

Zebo Yang, Haneen Alfauri, Behrooz Farkiani, and 3 more authors

IEEE Communications Surveys & Tutorials, 2023

Abstract arXiv HTML

Blockchains have gained substantial attention from academia and industry for their ability to facilitate decentralized trust and communications. However, the rapid progress of quantum computing poses a significant threat to the security of existing blockchain technologies. Notably, the emergence of Shor’s and Grover’s algorithms raises concerns regarding the compromise of the cryptographic systems underlying blockchains. Consequently, it is essential to develop methods that reinforce blockchain technology against quantum attacks. In response to this challenge, two distinct approaches have been proposed. The first approach involves post-quantum blockchains, which aim to utilize classical cryptographic algorithms resilient to quantum attacks. The second approach explores quantum blockchains, which leverage the power of quantum computers and networks to rebuild the foundations of blockchains. This paper aims to provide a comprehensive overview and comparison of post-quantum and quantum blockchains while exploring open questions and remaining challenges in these domains. It offers an in-depth introduction, examines differences in blockchain structure, security, privacy, and other key factors, and concludes by discussing current research trends.
A Survey of Important Issues in Quantum Computing and Communications

Zebo Yang, Maede Zolanvari, and Raj Jain

IEEE Communications Surveys & Tutorials, 2023

Abstract HTML PDF

Driven by the rapid progress in quantum hardware, recent years have witnessed a furious race for quantum technologies in both academia and industry. Universal quantum computers have supported up to hundreds of qubits, while the scale of quantum annealers has reached three orders of magnitude (i.e., thousands of qubits). Quantum computing power keeps climbing. Race has consequently generated an overwhelming number of research papers and documents. This article provides an entry point for interested readers to learn the key aspects of quantum computing and communications from a computer science perspective. It begins with a pedagogical introduction and then reviews the key milestones and recent advances in quantum computing. In this article, the key elements of a quantum Internet are categorized into four important issues, which are investigated in detail: a) quantum computers, b) quantum networks, c) quantum cryptography, and d) quantum machine learning. Finally, the article identifies and discusses the main barriers, the major research directions, and trends.
LEMDA: A Novel Feature Engineering Method for Intrusion Detection in IoT Systems

Ali Ghubaish, Zebo Yang, Aiman Erbad, and 1 more author

IEEE Internet of Things Journal, 2023

Abstract arXiv HTML

Intrusion detection systems (IDS) for the Internet of Things (IoT) systems can use AI-based models to ensure secure communications. IoT systems tend to have many connected devices producing massive amounts of data with high dimensionality, which requires complex models. Complex models have notorious problems such as overfitting, low interpretability, and high computational complexity. Adding model complexity penalty (i.e., regularization) can ease overfitting, but it barely helps interpretability and computational efficiency. Feature engineering can solve these issues; hence, it has become critical for IDS in large-scale IoT systems to reduce the size and dimensionality of data, resulting in less complex models with excellent performance, smaller data storage, and fast detection. This paper proposes a new feature engineering method called LEMDA (Light feature Engineering based on the Mean Decrease in Accuracy). LEMDA applies exponential decay and an optional sensitivity factor to select and create the most informative features. The proposed method has been evaluated and compared to other feature engineering methods using three IoT datasets and four AI/ML models. The results show that LEMDA improves the F1 score performance of all the IDS models by an average of 34% and reduces the average training and detection times in most cases.

2022

Decentralization Using Quantum Blockchain: A Theoretical Analysis

Zebo Yang, Tara Salman, Raj Jain, and 1 more author

IEEE Transactions on Quantum Engineering, 2022

Abstract HTML PDF

Blockchain technology has been prominent recently due to its applications in cryptocurrency. Numerous decentralized blockchain applications have been possible due to blockchains’ nature of distributed, secured, and peer-to-peer storage. One of its technical pillars is using public-key cryptography and hash functions, which promise a secure, pseudoanonymous, and distributed storage with nonrepudiation. This security is believed to be difficult to break with classical computational powers. However, recent advances in quantum computing have raised the possibility of breaking these algorithms with quantum computers, thus, threatening the blockchains’ security. Quantum-resistant blockchains are being proposed as alternatives to resolve this issue. Some propose to replace traditional cryptography with postquantum cryptography—others base their approaches on quantum computer networks or quantum internets. Nonetheless, a new security infrastructure (e.g., access control/authentication) must be established before any of these could happen. This article provides a theoretical analysis of the quantum blockchain technologies that could be used for decentralized identity authentication. We put together a conceptual design for a quantum blockchain identity framework and give a review of the technical evidence. We investigate its essential components and feasibility, effectiveness, and limitations. Even though it currently has various limitations and challenges, we believe a decentralized perspective of quantum applications is noteworthy and likely.
A Survey of Blockchain Applications in the FinTech Sector

Tara Renduchintala, Haneen Alfauri, Zebo Yang, and 2 more authors

Journal of Open Innovation: Technology, Market, and Complexity, 2022

Abstract HTML

FinTech has proven its true potential in traditional financial offerings by delivering digital financial services to individuals worldwide. The pandemic has accelerated how people interact with financial services and has resulted in long-term changes to societies and economies. FinTech has expanded access to financial services and has made such changes possible. FinTech or Financial Technology refers to using new technologies for financial services. Artificial Intelligence, Blockchain, and cloud computing are a few technologies currently being applied to FinTech. In this paper, we consider FinTech, which partly uses blockchain technology. Blockchain technology plays a vital role in the financial sector as it ultimately lifts trust and the need for third-party verification by using consensus-based verification. This survey provides a comprehensive summary of the most relevant blockchain-based FinTech implementations and an overview of FinTech sectors and segments. For each segment, we provide a critique and a discussion on how each blockchain implementation contributes to solving the majority of problems faced by FinTech companies and researchers. This research aims to direct the future of financial solutions by providing an outline of the applications of blockchain technology and distributed ledger technology (DLT) for FinTech. We discuss various implementations, limitations, and challenges of blockchain-based FinTech applications. We conclude this work by exploring possible strengths, weaknesses, opportunities, and threats (SWOT) analysis and future research directions.

2021

Trust xai: Model-agnostic explanations for ai with a case study on iiot security

Maede Zolanvari, Zebo Yang, Khaled Khan, and 2 more authors

IEEE internet of things journal, 2021

Abstract arXiv HTML

Despite artificial intelligence (AI)’s significant growth, its “black box” nature creates challenges in generating adequate trust. Thus, it is seldom utilized as a standalone unit in IoT high-risk applications, such as critical industrial infrastructures, medical systems, financial applications, etc. Explainable AI (XAI) has emerged to help with this problem. However, designing appropriately fast and accurate XAI is still challenging, especially in numerical applications. Here, we propose a universal XAI model, named the transparency relying upon statistical theory (TRUST), which is model-agnostic, high performing, and suitable for numerical applications. Simply put, TRUST XAI models the statistical behavior of the AI’s outputs in an AI-based system. Factor analysis is used to transform the input features into a new set of latent variables. We use mutual information (MI) to rank these variables and pick only the most influential ones on the AI’s outputs and call them “representatives” of the classes. Then, we use multimodal Gaussian (MMG) distributions to determine the likelihood of any new sample belonging to each class. We demonstrate the effectiveness of TRUST in a case study on cybersecurity of the Industrial Internet of Things (IIoT) using three different cybersecurity data sets. As IIoT is a prominent application that deals with numerical data. The results show that TRUST XAI provides explanations for new random samples with an average success rate of 98%. Compared with local interpretable model-agnostic explanations (LIME), a popular XAI model, TRUST is shown to be superior in the context of performance, speed, and the method of explainability. In the end, we also show how TRUST is explained to the user.