10 papers
D-FROST: Decentralized Federated pRompt-tuning via Optimal tranSporT for Non-IID and Imbalanced Data
Quan Minh Nguyen, Hoang M. Ngo, Trong Nghia Hoang +1
Prompt tuning provides a parameter-efficient way to adapt foundation models (FMs) by freezing the pretrained backbone and updating only a small set of learnable prompts. This prope…
CutBackdoor: A Circuit Cut Triggered Backdoor Attack on Variational Quantum Algorithms
Ahatesham Bhuiyan, Hoang Ngo, Cheng Chu +4
Variational Quantum Algorithms (VQAs) are a leading paradigm for near-term quantum computing, combining parameterized quantum circuits with classical optimization across quantum ch…
Q-ANCHOR: Federated Quantum Learning with ZNE-guided Correction
Hoang M. Ngo, Quan Nguyen, Wanli Xing +1
Quantum Federated Learning (QFL) offers a promising framework to train quantum models across distributed clients while keeping data strictly local. Due to its simplicity and low co…
Leveraging Soft Prompts for Privacy Attacks in Federated Prompt Tuning
Quan Minh Nguyen, Min-Seon Kim, Hoang M. Ngo +3
Membership inference attacks (MIAs) pose a serious privacy threat in federated learning (FL). While MIAs have been extensively studied in standard FL, the recent shift toward feder…
Q-ShiftDP: A Differentially Private Parameter-Shift Rule for Quantum Machine Learning
Hoang M. Ngo, Nhat Hoang-Xuan, Quan Nguyen +3
Quantum Machine Learning (QML) promises significant computational advantages, but preserving training data privacy remains challenging. Classical approaches like differentially pri…
Guaranteeing Privacy in Hybrid Quantum Learning through Theoretical Mechanisms
Hoang M. Ngo, Tre' R. Jeter, Incheol Shin +3
Quantum Machine Learning (QML) is becoming increasingly prevalent due to its potential to enhance classical machine learning (ML) tasks, such as classification. Although quantum no…
QUPID: A Partitioned Quantum Neural Network for Anomaly Detection in Smart Grid
Hoang M. Ngo, Tre' R. Jeter, Jung Taek Seo +1
Smart grid infrastructures have revolutionized energy distribution, but their day-to-day operations require robust anomaly detection methods to counter risks associated with cyber-…
FIDDLE: Reinforcement Learning for Quantum Fidelity Enhancement
Hoang M. Ngo, Tamer Kahveci, My T. Thai
Quantum computing has the potential to revolutionize fields like quantum optimization and quantum machine learning. However, current quantum devices are hindered by noise, reducing…
CHARME: A chain-based reinforcement learning approach for the minor embedding problem
Hoang M. Ngo, Nguyen H K. Do, Minh N. Vu +3
Quantum annealing (QA) has great potential to solve combinatorial optimization problems efficiently. However, the effectiveness of QA algorithms is heavily based on the embedding o…
QOMIC: Quantum optimization for motif identification
Hoang M. Ngo, Tamim Khatib, My T. Thai +1
Network motif identification problem aims to find topological patterns in biological networks. Identifying non-overlapping motifs is a computationally challenging problem using cla…