This workshop aims to advance the translation of quantum circuits into deployable, domain-driven intelligent systems by gathering methods, systems, and applications in Quantum Machine Learning (QML). We emphasize hybrid pipelines, reliability under noise and errors, interpretability, security and privacy, and benchmarking that links algorithms to real hardware and latency constraints. Submissions spanning theory, software/hardware co-design, and case studies in finance, healthcare, transportation, climate, cybersecurity, networks, and IoT are especially encouraged.
Cross-cutting QML Methods
– Hybrid quantum-classical learning pipelines
– Quantum representation learning & embeddings
– Interpretable & trustworthy QML
– Robust QML under noise
– Benchmarking & reproducibility for QML
– Hardware-aware QML
– Security and Privacy in QML
Applied QML
– Quantum Finance
– Quantum Healthcare
– Transportation & Mobility
– Climate & Weather
– Cybersecurity
– Materials & Chemistry
– Telecom & Networks
– Smart Cities & IoT
Optimization & Reliability
– QML architecture/search
– Training quantum Reinforcement Learning agents
– Variational optimization at scale
– Error mitigation and correction for QML
Generative & Foundation Models
– Quantum GANs & VAEs for simulation/synthetic data
– Generative agents for QML pipeline design
– Multimodal & retrieval-augmented QML
Dr. Nouhaila Innan, Research Team Lead and Postdoctoral Associate, eBRAIN Lab, CQTS, New York University Abu Dhabi, UAE.
Dr. Alberto Marchisio, Research Team Lead at eBRAIN Lab, New York University Abu Dhabi, UAE.
Dr. Muhammad Kashif, Research Team Lead and Postdoctoral Associate, eBRAIN Lab, CQTS, New York University Abu Dhabi, UAE.
Prof. Muhammad Shafique, Professor of Electrical and Computer Engineering, Director of eBRAIN Lab and iCAS Lab, New York University Abu Dhabi, UAE.