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
Muhammad Usman
(CSIRO, University of Melbourne, AU)
Samuel Yen-Chi Chen
(Wells Fargo, US)
Giovanni Acampora
(University of Naples Federico II, IT)
Pierre-Emmanuel Emeriau
(Quandela, FR)
Amir Pourabdollah
(Nottingham Trent University, UK)
Akash Kundu
(Delft University of Technology and QuTech, NL)
Antonello Rosato
(Sapienza University of Rome, IT)
Nouhaila Innan
(New York University Abu Dhbai, UAE)
Alberto Marchisio
(New York University Abu Dhbai, UAE)
Muhammad Kashif
(New York University Abu Dhbai, UAE)
Muhammad Shafique
(New York University Abu Dhbai, UAE)