20 citations · 20 across the 6 of their papers we have counts for
8 papers
Quantum Computing for Network Security Classification: Near-Term Classification and Long-Term Memory Efficiency
Yuqing Li, Poonam Bala Nehru, Yunpeng Zhang +4
Quantum computing has already been explored in several network-security applications. However, how quantum computing may contribute to network-security classification in both the n…
Empirical Scaling Laws of Quantum Vision Transformers
Yuqing Li, Fengyi Gao, Xin Jin +3
Quantum machine learning (QML) is a promising application of quantum computing, yet when and how practical learning models can achieve quantum advantage remains poorly understood.…
When AI meets quantum information: A comprehensive review
Min Chen, Yu Gan, Xin Jin +15
Artificial intelligence (AI) and quantum information (QI) are rapidly co-evolving. AI is becoming a practical tool for learning, designing, controlling, and verifying quantum syste…
Quantum-Resistant Quantum Teleportation
Xin Jin, Nitish Kumar Chandra, Mohadeseh Azari +4
We propose a quantum-resistant quantum teleportation (QRQT) framework protected by post-quantum cryptography (PQC) to secure the classical correction channel, which is vulnerable t…
Quantum-Resistant Networks Using Post-Quantum Cryptography
Xin Jin, Nitish Kumar Chandra, Mohadeseh Azari +2
Quantum networks rely on both quantum and classical channels for coordinated operation. Current architectures employ entanglement distribution and key exchange over quantum channel…
Learning with errors may remain hard against quantum holographic attacks
Yunfei Wang, Xin Jin, Junyu Liu
The Learning with Errors (LWE) problem underlies modern lattice-based cryptography and is assumed to be quantum hard. Recent results show that estimating entanglement entropy is as…
Quantum repeaters enhanced by vacuum beam guides
Yu Gan, Mohadeseh Azari, Nitish Kumar Chandra +4
The development of large-scale quantum communication networks faces critical challenges due to photon loss and decoherence in optical fiber channels. These fundamentally limit tran…
Stochastic noise can be helpful for variational quantum algorithms
Junyu Liu, Frederik Wilde, Antonio Anna Mele +3
Saddle points constitute a crucial challenge for first-order gradient descent algorithms. In notions of classical machine learning, they are avoided for example by means of stochas…