4 citations · 4 across the 7 of their papers we have counts for
8 papers
Lifting: Quantum State Discrimination Framework for Mid-Circuit Measurements
Chi Zhang, Neel Vora, Yilun Xu +3
Quantum computing promises new capabilities beyond classical computation. Mid-circuit measurements (MCMs) enable error correction and outcome-dependent control, but their reliabili…
Backdoor Threats in Variational Quantum Circuits: Taxonomy, Attacks, and Defenses
Lei Jiang, Fan Chen
Variational quantum algorithms (VQAs) are a central paradigm for noisy intermediate-scale (NISQ) quantum computing, yet their reliance on predesigned and pretrained variational qua…
QASM-Eval: A Dataset to Train and Evaluate LLMs on OpenQASM-3 Beyond Quantum Circuits
Zhenxiao Fu, Lei Jiang, Fan Chen
Quantum computing remains in the Noisy Intermediate-Scale Quantum (NISQ) era, with performance constrained by noise. Addressing this limitation requires hardware-facing capabilitie…
CO2-Meter: A Comprehensive Carbon Footprint Estimator for LLMs on Edge Devices
Zhenxiao Fu, Chen Fan, Lei Jiang
LLMs have transformed NLP, yet deploying them on edge devices poses great carbon challenges. Prior estimators remain incomplete, neglecting peripheral energy use, distinct prefill/…
QAgent: An LLM-based Multi-Agent System for Autonomous OpenQASM programming
Zhenxiao Fu, Lei Jiang, Yilun Xu +2
Programming quantum circuits at the OpenQASM level is essential for achieving hardware-aware optimization and reliable execution on noisy intermediate-scale quantum (NISQ) devices,…
CopyQNN: Quantum Neural Network Extraction Attack under Varying Quantum Noise
Zhenxiao Fu, Leyi Zhao, Xuhong Zhang +3
Quantum Neural Networks (QNNs) have shown significant value across domains, with well-trained QNNs representing critical intellectual property often deployed via cloud-based QNN-as…
LLMCO2: Advancing Accurate Carbon Footprint Prediction for LLM Inferences
Zhenxiao Fu, Fan Chen, Shan Zhou +2
Throughout its lifecycle, a large language model (LLM) generates a substantially larger carbon footprint during inference than training. LLM inference requests vary in batch size,…
Quantum Neural Network Extraction Attack via Split Co-Teaching
Zhenxiao Fu, Fan Chen
Quantum Neural Networks (QNNs), now offered as QNN-as-a-Service (QNNaaS), have become key targets for model extraction attacks. Existing methods use ensemble learning to train subs…