aiqbench analysis
This study establishes a hardware feasibility framework for applying shallow QAOA algorithms to real-world DDoS mitigation, demonstrating that routing overhead and backend architecture significantly impact solution quality and runtime. The authors explicitly avoid claiming quantum advantage, noting that current classical heuristics can solve the benchmark graphs, thereby focusing instead on creating a reproducible pipeline for comparing quantum processor architectures. For a tech audience, the key takeaway is the necessity of reporting MaxCut cost, security metrics, and runtime as separate indicators when evaluating cybersecurity-relevant quantum optimization workloads.
Hardware-Aware QAOA for Honeypot Traffic Partitioning on 100+ Qubit IBM Quantum Processors
arXiv:2606.09469v1 Announce Type: new Abstract: Denial-of-service (DoS) and distributed denial-of-service (DDoS) mitigation requires separating malicious traffic from benign traffic while minimizing disruption to legitimate users. Prior work proposed mapping honeypot traffic partitioning to a weighted MaxCut problem and solving the resulting graphs with variational quantum algorithms. We extend this proof of principle direction with a reproducible event-level honeypot-to-QUBO pipeline, labeled temporal bipartite benchmark graphs with 16, 32, 66, and 110 event nodes, QAOA executions on IBM quantum hardware, classical heuristic baselines, a noiseless matrix product state reference, and a routing overhead analysis across quantum processor architectures. The largest benchmark is a 110-node, 181-edge instance executed on three IBM backends. Our results show that a shallow QAOA can execute real traffic partitioning workloads at the utility scale, while backend architecture and routing overhead affect objective quality, security metrics, and observed runtime. Because simple classical heuristics can solve the current labeled benchmark graphs, these experiments are not a quantum advantage claim. Instead, we deliberately use a fixed, shallow QAOA implementation to enable controlled comparisons across problem sizes and hardware architectures. This work establishes a hardware feasibility and architecture benchmark framework, and demonstrates that MaxCut cost, security quality, routing overhead, and runtime must be reported as separate metrics for cybersecurity relevant quantum optimization.
Abstract:Denial-of-service (DoS) and distributed denial-of-service (DDoS) mitigation requires separating malicious traffic from benign traffic while minimizing disruption to legitimate users. Prior work proposed mapping honeypot traffic partitioning to a weighted MaxCut problem and solving the resulting graphs with variational quantum algorithms. We extend this proof of principle direction with a reproducible event-level honeypot-to-QUBO pipeline, labeled temporal bipartite benchmark graphs with 16, 32, 66, and 110 event nodes, QAOA executions on IBM quantum hardware, classical heuristic baselines, a noiseless matrix product state reference, and a routing overhead analysis across quantum processor architectures. The largest benchmark is a 110-node, 181-edge instance executed on three IBM backends. Our results show that a shallow QAOA can execute real traffic partitioning workloads at the utility scale, while backend architecture and routing overhead affect objective quality, security metrics, and observed runtime. Because simple classical heuristics can solve the current labeled benchmark graphs, these experiments are not a quantum advantage claim. Instead, we deliberately use a fixed, shallow QAOA implementation to enable controlled comparisons across problem sizes and hardware architectures. This work establishes a hardware feasibility and architecture benchmark framework, and demonstrates that MaxCut cost, security quality, routing overhead, and runtime must be reported as separate metrics for cybersecurity relevant quantum optimization.
arXiv:2606.09469 [quant-ph]
(or arXiv:2606.09469v1 [quant-ph] for this version)
https://doi.org/10.48550/arXiv.2606.09469 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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