About Me

Hello！ My name is Yifeng Peng and I am currently a second year PhD Candidate at Stevens Institute of Technology advised by Prof. Ying Wang, co-advised by Prof. Yuxuan Du (Nanyang Technological University), Samuel Yen-Chi Chen (Wells Fargo) and Prof. Zhiding Liang (Rensselaer Polytechnic Institute). And I have my undergraduate study at University of Electronic Science and Technology of China and University of Glasgow advised by Prof. Jienan Chen and Prof. Bo Yi.

Research Interests

My research focuses on two main areas:

Variational Quantum Algorithms (VQA/VQE): Design and theoretical analysis of parameterised quantum circuits for eigenvalue estimation, combinatorial optimisation, and quantum‐enhanced model training on NISQ hardware.
Machine-Learning Methods for Quantum Systems: Application of reinforcement learning, graph neural networks, and Bayesian optimisation to circuit synthesis, noise mitigation, error correction, and device calibration.
Quantum-Enhanced Machine Learning: Development of quantum feature maps, kernels, and neural networks that exploit superposition and entanglement to reduce sample complexity and accelerate inference under realistic noise models.

AI–Quantum Co-Design: Formulating novel learning paradigms that tightly integrate classical AI models with non-classical resources (superposition, entanglement) to enhance expressivity, sample efficiency, and trainability on NISQ devices.
Quantum-Accelerated Optimisation for AI: Investigating variational and quantum-inspired heuristics (e.g., QAOA-style schedulers, quantum annealing mappings) to speed up large-scale hyper-parameter search, neural architecture search, and combinatorial reasoning tasks central to modern AI.
AI Techniques for Quantum Hardware: Deploying Large Language Model (LLM) to automate circuit compilation, noise suppression, error-mitigation strategies, and adaptive calibration, thereby improving quantum processor fidelity and throughput.

Yifeng Peng, Xinyi Li, Zhemin Zhang, Samuel Yen-Chi Chen, Zhiding Liang, Ying Wang
Breaking Through Barren Plateaus: Reinforcement Learning Initializations for Deep Variational Quantum Circuits

Yifeng Peng, Xinyi Li, Zhemin Zhang, Samuel Yen-Chi Chen, Zhiding Liang, Ying Wang
Can Classical Initialization Help Variational Quantum Circuits Escape the Barren Plateau?

Xinyi Li, Yifeng Peng, Ying Wang
FiCo: A Fingerprinting-based Two-step Learning-to-learn Approach Combing Vibration and 5G Communication for UAV Classification

One Paper Accepted by IEEE Transactions on Quantum Engineering , 10th Octorber 2024

Yifeng Peng, Xinyi Li, Sudhanshu Arya, Ying Wang
CoCo: A CBOW-Based Framework for Synergistic Vulnerability Detection in Partial and Discontinuous Logs for NextG Communications

Yifeng Peng, Xinyi Li, Ying Wang
QRNG-DDPM: Enhancing Diffusion Models through Fitting Mixture Noise with Quantum Random Number

Yifeng Peng, Xinyi Li, Jingda Yang, Sudhanshu Arya, Ying Wang
RAFT: A Real-Time Framework for Root Cause Analysis in 5G and Beyond Vulnerability Detection

Yifeng Peng, Jingda Yang, Sudhanshu Arya, Ying Wang
SmiLe Net: A Supervised Graph Embedding-based Machine Learning Approach for NextG Vulnerability Detection

One Paper Accepted by IEEE Access , 20th Octorber 2023

Yifeng Peng, Xinyi Li, Sudhanshu Arya, Ying Wang
DEFT: A Novel Deep Framework for Fuzz Testing Performance Evaluation in NextG Vulnerability Detection