Quantum Computing in Remote Sensing and Earth Observation

The lecture series aims to provide a practical introduction to quantum computation, tailored for non-specialists, with a focus on Earth observation (EO) applications. The first lecture will provide the fundamentals, highlighting the potential and limitations for EO applications. The series will then follow a hands-on approach, gradually introducing theoretical concepts along with their practical implementation with Python and Pennylane through four guided coding sessions. Participants will build quantum circuits, explore quantum algorithms, and apply quantum machine learning (quantum kernels, hybrid quantum-classical neural networks) to real-world tasks like semantic segmentation of remote sensing images, ensuring students gain direct experience with state-of-the-art QML models for EO.

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Learning Outcomes

  • Understand core principles, motivations, and limitations of quantum computing
  • Implement and run quantum circuits with Python and Pennylane
  • Design and apply quantum machine learning models for Earth observation

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Agenda

Introduction to Quantum Computation

  • What is quantum computation
  • NISQ vs fault-tolerant quantum computers
  • Applications to Earth observation

Hands-on Session 1: Hello Quantum World

  • Setting up the environment
  • Getting started with quantum computing libraries

Hands-on Session 2: Quantum Algorithms with Pennylane

  • Qubits, Bloch sphere representation, circuits, basic operations
  • Examples of quantum circuits
  • Hardware backends

Hands-on Session 3: Quantum Kernels for Earth observation

  • Fidelity quantum kernels for semantic segmentation of remote sensing images

Hands-on Session 4: Hybrid Quantum-Classical Neural Networks for Earth observation

  • Examples of hybrid quantum-classical neural networks for semantic segmentation of remote sensing images: quanvolutional network, quantum U-net
Meet

Instructors

Artur Miroszewski

Biography:

Artur Miroszewski received the Ph.D. degree in theoretical physics from the National Centre for Nuclear Research, Otwock, Poland, in 2021. He has been a Postdoctoral Researcher with the Jagiellonian University, Krakow, Poland. He is currently an Internal Research Fellow at the Φ-lab, European Space Research Institute (ESRIN), ESA. His main research interests include the application of kernel methods for classification tasks. He currently serves as chair of the IEEE Geoscience and Remote Sensing Society Quantum Earth Science and Technology Technical Committee. He co-organized a three-day Quantum Computing for Space Applications workshop held in Kraków, Poland, in 2024 and 2025. He co-organized a quantum computing workshop at the IEEE Indian Geoscience and Remote Sensing Symposium 2025 in Bhubaneswar, India.

Amer Delilbasic

Biography:

Amer Delilbasic is a research scientist at the Simulation and Data Lab “AI and ML for Remote Sensing”, Jülich Supercomputing Centre, Forschungszentrum Jülich, Germany. He received his B.Sc. and M.Sc. degrees in Italy, both cum laude, in Information and Communication Engineering from the University of Trento in 2019 and 2021, respectively, and his PhD degree in Computational Engineering from the University of Iceland in 2026. His research focuses on machine learning and optimization methods based on quantum computing and high performance computing for Earth observation. He has co-authored several articles in leading journals and international conferences in these areas. In 2025, he received the Best Paper Award at the QUEST-IS Conference, held in Paris, France. In 2021, his proposal was selected for funding under the Open Space Innovation Platform of the European Space Agency (ESA). He has also been a Visiting Researcher at the Φ-lab, European Space Research Institute (ESRIN), ESA. He currently serves as co-lead of the Quantum Computing for Earth Observation working group within the IEEE Geoscience and Remote Sensing Society Quantum Earth Science and Technology Technical Committee. He co-organized a quantum computing workshop at the IEEE Indian Geoscience and Remote Sensing Symposium 2025 in Bhubaneswar, India.