We are pleased to announce that our paper, “Learning Gaussian Processes with Randomized Quantum Local Kernels,” has been published in International Conference on Quantum Communications, Networking, and Computing (QCNC 2026).
We are pleased to announce that our Lincs Lab member Wissal Hamhoum, a PhD student at Polytechnique Montréal, has successfully passed her PhD Comprehensive Exam. The examination committee composed of …
Congratulations to Wissal Hamhoum on Successfully Passing the PhD Comprehensive Exam Read More »
On April 17, 2026, Lincs lab hosted a workshop titled “Advances in Quantum Machine Learning for Network Anomaly Detection and Time-Series Forecasting” at Polytechnique Montréal, with both onsite and online participats from 13 universities and to industry professionals across various companies.
Reflections on our Quantum ML Workshop Read More »
We are pleased to announce that our paper, “Trustworthy AI-Driven Dynamic Hybrid RIS: Joint Optimization and Reward Poisoning-Resilient Control in Cognitive MISO Networks,” has been published in IEEE Transactions on Network and Service Management.
New Publication in IEEE Transactions on Network and Service Management Read More »
O-RAN is fundamentally data-driven. Its intelligent control loops—enabled by its Near-Real-Time and Non-Real-Time RAN Intelligent Controllers (RICs)— support continuous monitoring, closed-loop control, and policy-driven optimization. These capabilities create a natural foundation for machine learning, allowing networks to learn from data, adapt to dynamic conditions, and make informed decisions in real time.
Making Open RAN Intelligent: The Role of Machine Learning Read More »
Quantum optimization shows potential as a powerful tool for enhancing resource allocation efficiency in Open Radio Access systems (Open RAN) A D-Wave quantum computer equipped with a quantum annealing processor. Photo credit: D-Wave Quantum Inc. Open Radio Access Networks (Open RAN) are emerging as a key architectural approach in next-generation mobile networks. One of
New paper accepted in IEEE Transactions on Network and Service Management Read More »
We are pleased to announce that our paper, “ML-Enabled Open RAN: A Comprehensive Survey of Architectures, Challenges, and Opportunities,” has been accepted for publication in IEEE Communications Surveys & Tutorials (Early Access).
New Paper Published in IEEE Communications Surveys & Tutorials Read More »
As wireless networks evolve toward the 6G era, they face growing challenges in managing the immense number of connected devices while maintaining energy efficiency and spectrum utilization. In a recent IEEE IWCMC 2025 publication, “Quantum-Aided Active User Detection for Energy-Efficient CD-NOMA in Cognitive Radio Networks,” Deemah H. Tashman and Prof. Soumaya Cherkaoui propose a quantum-enhanced approach for detecting active users in Cognitive Radio Networks – a key step in optimizing communication and reducing interference.
As modern communication networks grow in complexity, traditional anomaly detection techniques face challenges in identifying sophisticated threats such as distributed denial-of-service (DDoS) or stealth attacks. In our latest study, we explore how quantum machine learning can offer a new frontier for cybersecurity analytics.
In our recent paper, “Enhancing Network Anomaly Detection with Quantum GANs and Successive Data Injection for Multivariate Time Series”
Quantum GANs for Smarter Network Anomaly Detection Read More »
We extend our warmest congratulations to Prof. Soumaya Cherkaoui and Deemah H. Tashman, PhD fellow at LincsLab, on their recent publication titled:
“Quantum-Aided Active User Detection for Energy-Efficient CD-NOMA in Cognitive Radio Networks”, presented at the 2025 International Wireless Communications and Mobile Computing Conference (IWCMC) and published by IEEE.
Congratulations to Prof. Soumaya and Deemah on their new IWCMC 2025 publication Read More »
