The Meta-Quantum Ensemble (MQE) framework fuses Quantum Support Vector Machines and Quantum Neural Networks through a classical Random Forest meta-learner to boost intrusion detection on imbalanced IoT traffic. Tested on the TON_IoT and CICIDS2017 datasets, the approach yields modest gains in low false-positive rate metrics, but performance varies sharply by dataset and fusion method. This arXiv preprint (v1, May 2026) remains unpeer-reviewed and lacks details on qubit counts, circuit depths, or training sample sizes, limiting claims of robustness. Related work in Quantum Science and Technology (2024) on QSVM for anomaly detection similarly notes noise-induced instability on current hardware, while a 2023 IEEE Transactions on Information Forensics and Security study of classical ensembles on CICIDS2017 achieved comparable FPR reductions without quantum overhead. The original coverage overlooks how meta-level fusion merely postpones the core NISQ-era barrier: exponential error rates that could let sophisticated adversaries still evade detection in live enterprise settings. Cybersecurity teams may face short-term job shifts toward hybrid quantum-classical monitoring roles, yet widespread adoption hinges on error-corrected hardware unlikely before 2030, leaving classical systems dominant for immediate data safety needs.