1 citations · 1 across the 6 of their papers we have counts for
6 papers
SQGen: Structured Quantum Image Generation with Latent-Modulated Quantized Tensor Trains
Guang Lin, Qibin Zhao
Generating images directly from quantum systems is an attractive but unresolved goal on NISQ hardware. Existing quantum generators face several coupled obstacles: barren plateaus t…
Spectral Anatomy of Quantum Gaussian Process Kernels
Jian Xu, Chao Li, Guang Lin +4
Two recent results have reshaped quantum Gaussian processes (QGPs). On the one hand, \citet{lowe2025assessing} rule out the exponential speedups claimed by HHL-based QGP regression…
Structured Unitary Tensor Network Representations for Circuit-Efficient Quantum Data Encoding
Guang Lin, Toshihisa Tanaka, Qibin Zhao
Encoding classical data into quantum states is a central bottleneck in quantum machine learning: many widely used encodings are circuit-inefficient, requiring deep circuits and sub…
Fit for Purpose? Deepfake Detection in the Real World
Guangyu Lin, Li Lin, Christina P. Walker +2
The rapid proliferation of AI-generated content, driven by advances in generative adversarial networks, diffusion models, and multimodal large language models, has made the creatio…
Model-Free Adversarial Purification via Coarse-To-Fine Tensor Network Representation
Guang Lin, Duc Thien Nguyen, Zerui Tao +3
Deep neural networks are known to be vulnerable to well-designed adversarial attacks. Although numerous defense strategies have been proposed, many are tailored to the specific att…
Large Language Model Sentinel: LLM Agent for Adversarial Purification
Guang Lin, Toshihisa Tanaka, Qibin Zhao
Over the past two years, the use of large language models (LLMs) has advanced rapidly. While these LLMs offer considerable convenience, they also raise security concerns, as LLMs a…