The paper presents a non‑invasive, cloud‑based migration strategy that protects legacy smart HVAC controllers from quantum attacks without modifying the devices or vendor cloud. It introduces a Raspberry Pi 4B gateway that performs post‑quantum key encapsulation (ML‑KEM‑768) and authentication (ML‑DSA‑65) using AES‑256‑GCM, completing handshakes in 2.48 ms and sustaining 443 sessions per second. Extensive side‑channel testing shows no timing leakage or man‑in‑the‑middle vulnerabilities, and the architecture is vendor‑agnostic, becoming redundant once native PQC support is adopted.
By Mahedee Zaman Moon, Kaysarul Anas Apurba, Md Hasibul Hasan, Sk Md Mizanur Rahman
arXiv:2606. 14515v1 Announce Type: cross Abstract: Internet of Medical Things (IoMT) devices operate under strict resource constraints while handling highly sensitive health data, making security and privacy critical concerns.
By Taym Alshoghri, Deemah H. Tashman, Mohammad Reza Gerami, Soumaya Cherkaoui
The paper investigates how quantum network controllers should estimate the end‑to‑end fidelity of entanglement paths, comparing analytical computation, simulation, and learned surrogate models. It highlights that while simple scenarios allow analytical composition, stochastic memory effects require simulation, and lightweight learned models can approximate simulation outputs with lower runtime cost. The authors propose a compute/simulate/learn framework and demonstrate it with a simulation‑based proof of concept, outlining open challenges for real‑world deployment.
By Anis Bekri, Amar Abane, Van Sy Mai, Abdella Battou, Saddek Bensalem
arXiv:2609.37551v1 Announce Type: cross
Abstract: Emerging optical-network applications increasingly use received data for inference and control rather than exact source reproduction, creating an opp...
By Hussein Jammal, Andrea Bianco, Cristina Rottondi
arXiv:2609.25044v1 Announce Type: cross
Abstract: This paper presents a quantum semantic communication (QSemCom) framework combining quantum machine learning (QML) and semantic communication (SemCom)...
By Melek Krichen, Nikhitha Nunavath, Riccardo Bassoli, Soumaya Cherkaoui
The broadcast nature of wireless channels exposes radio-frequency (RF) networks to anomalous and malicious transmissions, making anomaly detection a fundamental requirement for secure spectrum management. Quantum Kitchen Sinks (QKS) offer a lightweight hybrid quantum feature map suitable for near-term quantum devices, yet their behavior on structured signal data remains poorly understood.
arXiv:2607. 13897v1 Announce Type: new Abstract: The broadcast nature of wireless channels exposes radio-frequency (RF) networks to anomalous and malicious transmissions, making anomaly detection a fundamental requirement for secure spectrum management.
By Abdallah Aaraba, Alexis Vieloszynski, Remon Polus, Ola Ahmad, Soumaya Cherkaoui
arXiv:2604. 06135v2 Announce Type: replace-cross Abstract: Efficient data loading remains a bottleneck for near-term quantum machine learning.
By Basil Kyriacou, Viktoria Patapovich, Maniraman Periyasamy, Alexey Melnikov
arXiv:2605.25066v2 Announce Type: replace-cross
Abstract: As machine-learning workloads move onto quantum hardware they inherit a trust problem classical AI deployments have largely solved and quantu...
By Esra Yeniaras
The paper investigates the role of quantum computing in network‑security classification through two experiments. First, it evaluates near‑term quantum‑kernel support vector machines on datasets such as KDD Cup 1999, CICIDS2017, and BoT‑IoT, finding that quantum kernels can match or sometimes improve classical baselines, though classical RBF kernels often remain stronger. Second, it explores long‑term memory efficiency using quantum oracle sketching (QOS), showing that quantum methods can achieve comparable accuracy with a smaller effective memory footprint than explicit storage, suggesting a potential advantage in memory‑efficient data access for streaming classification tasks.
By Yuqing Li, Poonam Bala Nehru, Yunpeng Zhang, Danindu Gammanpilage, Xin Jin, Zeguan Wu, Junyu Liu
This paper introduces an autonomous optical wireless receiver that uses on-device TinyML to diagnose faults in real time. Built on an ESP32‑S3 microcontroller, it extracts key channel metrics—BER, signal voltage, SNR, and ambient light—and classifies four operational conditions with a 74.68% accurate INT8 TensorFlow Lite Micro model. The approach eliminates external processing, enabling deterministic, low‑latency fault detection for optical IoT nodes.
By Sarthak Bhattarai, Sheshadri Shikhar Bandyopadhyay, Suprabh Dhakal, Jitendra Kumar Manandhar
The study evaluates quantum machine learning (QML) models for network intrusion detection against well-tuned classical baselines across four standard datasets, using a leakage-controlled protocol and noise simulation. It introduces a quantum-attribution audit to determine whether any performance gains are truly due to quantum effects. While most tuned classical models match or surpass QML, two quantum approaches— a quantum-kernel SVM and a small hybrid circuit—show statistically significant advantages on specific metrics and tasks.
By Syeda Anshrah Gillani, Mirza Samad Ahmed Baig, Shahid Munir Shah, Asher Ali, Hamzah Siddiqui