The second workshop on Quantum Machine Learning for Next-Generation Networks

Scope and topics

       Quantum computers are emerging as powerful tools for enhancing traditional artificial intelligence (AI) models, potentially making them more lightweight and significantly faster. Recently, major companies such as Google, IBM, and other leading R&D teams have demonstrated breakthroughs showing that quantum computers can solve complex problems in minutes, tasks that would take traditional computers years to complete. While traditional AI models like deep learning (DL) and machine learning (ML) are widely known and used, the advent of the quantum era raises a pivotal question: how will quantum machine learning (QML) reshape the landscape of current AI, DL, and ML models and elevate their capabilities? 

       As next-generation networks (NGN) expand, particularly in multi-agent and ubiquitous intelligence scenarios, ensuring sustainability, efficiency, and scalability becomes ever more pressing.  In this vision, the seamless infusion of intelligence into everyday objects via wireless connectivity enables the devices to gather, interpret, and act upon environmental data. This creates a pervasive, context-aware computing experience that extends across multiple devices and locations, effectively bringing Space-Air-Ground Integrated Network (SAGIN) into the physical world.  For example, a smart thermostat can adjust temperature based on occupancy or weather, traffic management systems adjust their policies based on real-time traffic conditions, or streetlights adapt brightness based on surrounding environmental factors.  However, the connected networks must handle complex, rapidly changing conditions to maintain robust performance, resilience, and adaptability. Concurrently, AI/ML-based optimization techniques bolster resource allocation, refine network operations, and facilitate instantaneous decision-making in highly dynamic environments. Complementary technologies like generative AI and quantum machine learning (QML) further augments these capabilities by optimizing learning capability, particularly when paired with edge computing for massive amount of data handling and real-time response. Meanwhile, implementing robust security and privacy measures (e.g., blockchain) safeguards trust and secure communications within ecosystems of autonomous governments and personalized public services. Specific practical and standardization research, such as testbeds, implementations, and industry-oriented, trustworthy solutions, along with high-quality datasets for reliable communications and sustainable NGNs using QML, are also crucial considerations.

      Hosted at IEEE GLOBECOM 2026, this half-day workshop aims to explore federated, multi-layered, and interdisciplinary approaches, including QML, AI/ML, and robust security solutions for sustainable NGNs. Emphasizing the synergy among collaborative and adaptive QML models, AI/ML optimization, and secure, privacy-focused frameworks, this workshop will explore innovative strategies for building versatile, high-performance NGN systems. The program features paper presentations as well as Keynotes by prominent leaders from academia and industry panel discussions with QML/quantum AI, and their potential in our future life. We invite novel research papers and review articles that address themes such as trust, safety, efficient quantum AI models for resource management, edge computing, security and privacy, or quantum-enabled solutions, and blockchain-driven QML, ultimately contributing to the evolution of energy-efficient, resilient, and NGN networks but not limited to):

• QML and Entanglement Routing for Networking Technologies and Security in NGNs.

• QML and Quantum Optimization for Resource Allocation/Service Offloading for NGNs

• QML for Context-aware Edge Intelligence and NGN Personalized Applications

• QML Privacy Preservation Implementations and Platforms in NGNs

• QML Datasets and Hardware/Software Testbeds for NGNs

• QML Adversarial Attacks and Security Defense for Trusted NGNs

• QML-enabled Digital Twin Optimization and Secure Communications

• QML for O-RAN, cloud computing platforms, and 6G applications 

• Hybrid QML-Classical Models and Optimization for Digital Twins/Semantic Communications

• Large Wireless Models and Trustable Quantum LLM Solutions for NGNs.

• Multi-agent and Distributed/Federated/Meta QML for NGNs.

• Industrial/Commercial/Standardization-Oriented Strategies for QML-driven NGNs.

• Energy Efficiency, Sustainable Communications, Smart Contracts in QML Green-Driven NGNs.

•  6G UIoT-enabled Digital Twin Optimization and Secure Communications.

•  Quantum Transformers, Quantum Programming, and Emerging technologies for 6G NGNs

• Quantum Networks, Quantum Optimization, and Quantum Engineering for 6G NGNs

• Quantum AI: Vision and Breakthrough Applications in NGNs

• Security and privacy for QML in NGNs

• QKD, PQC, and Quantum Circuits for NGNs

Paper Submission Link

https://edas.info/N35345 

Submission policy

All submissions must be written in English in the standard IEEE two-column conference format and are limited to a maximum paper length of six (6) printed pages (10-point font) including figures and references. EDAS is configured to not permit the uploading of review manuscripts that exceed 6 pages.  All submissions will undergo a peer review process, and accepted papers presented by one of the authors at the workshop will be published in the IEEE GLOBECOM 2025 proceedings and IEEE Xplore. Once accepted, the final manuscript may have a 7th page, but such papers will incur an overlength page charge of US$100. 

Important Dates:

Workshop Paper Submission: 12 Aug  2026

Paper Acceptance Notification: 20 September 2026

Camera Ready: 30 September 2026

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This workshop is technically sponsored by the CHIST-ERA CALL 2023 Project SHIELD and Taiwan NSTC Project No. 112-2221-E-194-017-MY3, and all the members of the workshop organizing committee.