Challenges
Datasets
Workspaces
Papers
Discussions
Leaderboard
Log inSign up
Challenges
Datasets
Workspaces
Papers
Discussions
Leaderboard
Blog
Job Board
Q3AS

© 2026 Aqora Quantum S.A.S.

TermsPrivacyLegal Notice

International Conference on Agents and Artificial Intelligence (ICAART)

Adversarial Robustness of Quantum–Enhanced Graph Attention Networks

Yaswitha Gujju, Romain Harang, Tetsuo Shibuya, Qibin Zhao

Published Jan 1, 2026• 0 citations
Publisher
DOI
10.5220/0014650600004052

Related papers

Enhancing adversarial robustness of neural networks via quantum computing

Xiangyu Shi, Yunzhe Tian, Yike Li +5 more

Future Generation Computer Systems•Apr 21, 2026•3 citations

Towards quantum enhanced adversarial robustness in machine learning

Maxwell T. West, Shu-Lok Tsang, Jia Shun Low +5 more

arXiv:2306.12688•Nature Machine Intelligence•May 25, 2023•79 citations

Quantum-Enhanced Adversarial Robustness in Artificial Intelligence

Jaydip Sen

•Jan 1, 2026•0 citations

Image classification and adversarial robustness analysis based on hybrid quantum–classical convolutional neural network

Shui-Yuan Huang, Wan-Jia An, De-Shun Zhang +1 more

Optics Communications•Feb 1, 2023•86 citations

Quantum heterogeneous graph attention network

Wenjie Liu, Bohan Du, Qingshan Wu

Pattern Recognition•Oct 21, 2026•0 citations

Fault-Tolerant Quantum Communication over r-Robust Graph Topologies under Adversarial and Noisy Conditions

Suresh Kumar Jha, Siddhanta Kumar Singh, Aditya Narayan Hati

Abstract The realization of scalable quantum communication networks faces critical challenges from decoherence, noise, and adversarial attacks. This paper presents a comprehensive framework for fault-tolerant quantum communication using \textit{r}-robust graph topologies, which ensure strong connectivity and resilience in the presence of both random failures and Byzantine adversaries. We formalize \textit{r}-robustness in single-layer and multiplex quantum networks and derive theoretical bounds on fidelity, consensus success, and error propagation. A hybrid quantum-classical protocol is proposed that leverages multi-path entanglement and quantum certificate-based identity enforcement. Extensive simulations using Qiskit and NetworkX show that \textit{r}-robust networks improve average entanglement fidelity by up to 26\%, achieve over 95\% consensus success under 30\% adversarial nodes, and exponentially suppress error rates. Compared to ring, scale-free, and random graphs, \textit{r}-robust topologies provide superior robustness with moderate resource overhead. Our results establish a graph-theoretic foundation for constructing secure, resilient, and NISQ-compatible quantum networks capable of sustaining high-performance communication in noisy and adversarial environments.

•Jan 8, 2026•0 citations
Abstract The realization of scalable quantum communication networks faces critical challenges from decoherence, noise, and adversarial attacks. This paper presents a comprehensive framework for fault-tolerant quantum communication using \textit{r}-robust graph topologies, which ensure strong connectivity and resilience in the presence of both random failures and Byzantine adversaries. We formalize \textit{r}-robustness in single-layer and multiplex quantum networks and derive theoretical bounds on fidelity, consensus success, and error propagation. A hybrid quantum-classical protocol is proposed that leverages multi-path entanglement and quantum certificate-based identity enforcement. Extensive simulations using Qiskit and NetworkX show that \textit{r}-robust networks improve average entanglement fidelity by up to 26\%, achieve over 95\% consensus success under 30\% adversarial nodes, and exponentially suppress error rates. Compared to ring, scale-free, and random graphs, \textit{r}-robust topologies provide superior robustness with moderate resource overhead. Our results establish a graph-theoretic foundation for constructing secure, resilient, and NISQ-compatible quantum networks capable of sustaining high-performance communication in noisy and adversarial environments.