Quantum Deep Learning: A Comprehensive Review

About this event

Date: Dec 5 2026 10:00 am - Noon EST Title: Quantum Deep Learning: A Comprehensive Review Abstract: Quantum deep learning (QDL) explores the use of both quantum and quantum-inspired resources to determine when deep learning's core capabilities, such as expressivity, generalization, and scalability, can be enhanced based on specific resource constraints. Distinct from broader quantum machine learning, QDL emphasizes compositional depth at the pipeline level and the integration of quantum or quantum-inspired components within end-to-end workflows. This review provides an operational definition of QDL and introduces a taxonomy comprising four primary paradigms: hybrid quantum-classical models, quantum deep neural networks, quantum algorithms for deep learning primitives, and quantum-inspired classical algorithms. Theoretical principles are connected to advanced architectures, software toolchains, and experimental demonstrations across superconducting, trapped-ion, photonic, semiconductor spin, and neutral-atom systems, as well as quantum annealers. Claims of quantum advantage are critically assessed by distinguishing provable complexity-theoretic separations from empirical observations. The analysis characterizes trade-offs between model expressivity, trainability, and classical simulability, while systematically detailing the bottlenecks imposed by optimization landscapes, input-output access models, and hardware constraints. Applications are surveyed in domains encompassing image classification, natural language processing, scientific discovery, quantum data processing, and quantum optimal control, underscoring fair benchmarking against optimized classical counterparts and a comprehensive assessment of resource requirements. This review serves as a tutorial entry point for graduate students while guiding readers to specialized literature. It concludes with a verification-aware roadmap to transition QDL from near-term demonstrations to scalable and fault-tolerant implementations. Link to the paper: [https://arxiv.org/abs/2603.06644](https://arxiv.org/abs/2603.06644 "https://arxiv.org/abs/2603.06644") Speaker: Dr. Yanjun Ji has an interdisciplinary research background in Physics and Computer Science. In 2020, she completed her Master's degree in Theoretical Physics at Saarland University. In 2024, she earned her Ph.D. in Computer Science at the University of Stuttgart. She is currently a postdoctoral researcher at Forschungszentrum Jülich. She is committed to interdisciplinary research at the cutting-edge convergence of quantum computing and artificial intelligence. Her research encompasses both foundational theoretical investigations and the development of practical applications, aiming to serve society and benefit humanity.