His research explores the role of quantum machine learning in Earth Observation data classification, advancing the intersection of quantum computing and geospatial artificial intelligence.

As quantum computing continues to evolve, its integration with Earth Observation presents new opportunities, but also significant challenges. While classical machine learning methods remain dominant, understanding how quantum approaches can complement or extend existing techniques is an open research question. In this context, the dissertation explores the potential and limitations of quantum machine learning for remote sensing applications.

Exploring Quantum Machine Learning for Earth Observation

Dr. Fan’s work provides a systematic study of quantum machine learning methods tailored to Earth Observation tasks. The research addresses key aspects including model design, data representation, and scalability within the constraints of current quantum hardware.

The dissertation makes several main contributions:

Hybrid model design for Earth Observation.
The work develops and evaluates hybrid quantum–classical learning architectures, demonstrating how quantum components can be integrated into existing pipelines for image classification.

Resource-efficient data representation.
Approaches to encoding remote sensing data into quantum states are investigated, with particular attention to reducing computational overhead while maintaining relevant spatial information.

Learning strategies under practical constraints.
Methods for improving model transferability and training efficiency are proposed, enabling more robust performance across different datasets and tasks.

Outlook on quantum-enabled geospatial analysis.
The dissertation discusses the role of near-term quantum devices and outlines possible directions toward scalable and practically relevant quantum Earth Observation systems.

Through these contributions, the work contributes to a better understanding of how quantum machine learning can be leveraged in geospatial data analysis and highlights directions for future research in this emerging field.

Examination Committee

• Prof. Xiaoxiang Zhu (Technical University of Munich, PhD supervisor)
• Prof. Benjamin Busam (Technical University of Munich), chair of the committee
• Prof. Silvia Liberata Ullo (University of Sannio)
• Prof. Mihai Datcu (University POLITEHNICA of Bucharest)

The group warmly congratulates Dr. Fan Fan on this important academic milestone and wish him every success in his future research career.