Identifying vulnerable nodes and detecting malicious entanglement patterns to handle st-connectivity attacks in quantum networks

Abstract

Problems in distributed system security often map naturally to graphs. The concept of centrality assesses the importance of nodes in a graph. It is used in various applications. Cooperative game theory has also been used to create nuanced and flexible notions of node centrality. However, the approach is often computationally complex to implement classically. We describe a quantum approach to approximating the importance of quantum nodes that maintain a target connection in a quantum network. We detail a method for quickly identifying high-importance nodes that can be targeted by adversaries. The approximation method relies on quantum subroutines for st-connectivity, approximating Shapley values, and finding the maximum of a list. We consider a malicious actor targeting a subset of nodes to perturb the system functionality. Our method identifies the nodes that are most important in keeping nodes s and t connected. Once we have identified high-importance nodes, we require methods to identify when those nodes are compromised. We describe how Quantum Support Vector Machine (QSVM) classifiers can be used to detect malicious behavior in quantum networks. In particular, we describe the detection of entanglement attacks in quantum repeaters. We show that our initial assessment approach can be complemented by QSVM classifiers to identify and report anomalous situations related to malicious manipulation of entanglement swapping. Finally, we explore the potential complexity benefits of our quantum approach compared with classical and probabilistic methods. We also release all the simulation code in a companion GitHub repository.