3 papers
quant-ph2026
From Membership-Privacy Leakage to Quantum Machine Unlearning
Junjian Su, Runze He, Guanghui Li +4
Quantum machine learning (QML) has the potential to achieve quantum advantage for specific tasks by combining quantum computation with classical machine learning (ML). In classical…
quant-ph2025
Topology-Driven Quantum Architecture Search Framework
Junjian Su, Jiacheng Fan, Shengyao Wu +3
The limitations of Noisy Intermediate-Scale Quantum (NISQ) devices have motivated the development of Variational Quantum Algorithms (VQAs), which are designed to potentially achiev…
cs.LG2025
QGAN-based data augmentation for hybrid quantum-classical neural networks
Run-Ze He, Jun-Jian Su, Su-Juan Qin +2
Quantum neural networks converge faster and achieve higher accuracy than classical models. However, data augmentation in quantum machine learning remains underexplored. To tackle d…