Quantum Computing in Remote Sensing and Earth Observation

The lecture series aims to provide a foundational introduction to quantum computing, tailored for non-specialists. It begins with a presentation of the basics of finite-dimensional quantum mechanics and introduces fundamental concepts such as Hilbert space, operators, quantum states, and measurement. Building upon this essential groundwork, the series delves into the core principles of quantum computation. Topics covered include an overview of existing models of quantum computation, architectures of quantum devices, and the fundamental components of universal quantum computation models, including gates and circuits. Additionally, the lectures offer a concise review of renowned quantum computing algorithms known for their proven efficiency compared to classical counterparts. Transitioning to practical applications, the series introduces quantum machine learning, focusing on Quantum Kernel Machines and Quantum Neural Networks. Through hands-on sessions, students will have the opportunity to design and implement quantum machine learning models using simulators. The series culminates with an overview of the current quantum computing landscape and a discussion on future prospects in the field.

Learning Outcomes

  • Grasp the basic principles underlying quantum mechanics and quantum computing
  • Understand the advantages and limitations of quantum computation, enabling them to navigate through various terms and concepts in the field
  • Comprehend the design and real-life applications of quantum kernels and quantum neural networks


Introduction to quantum mechanics

  • Review on complex numbers
  • Hilbert space
  • Postulates of quantum mechanics
  • Unitary evolution
  • Measurement

Introduction to quantum computation

  • Quantum computing models
  • Current quantum computing hardware
  • Quantum circuits
  • Examples of quantum algorithms

Quantum machine learning

  • What is Quantum Machine Learning
  • Machine learning with adiabatic quantum devices
  • Quantum kernel machines
  • Quantum neural networks

Quantum algorithms for Remote Sensing

  • Creating a Quantum Machine Learning model for remote sensing dataset (Hands-on session)
  • Current challenges and future of Quantum Machine Learning
  • Other quantum algorithms for Remote Sensing and Earth Observation


Artur Miroszewski


Artur Miroszewski is a postdoctoral researcher at Jagiellonian University. He obtained his Ph.D. in 2021 from the National Centre for Nuclear Research, Warsaw, Poland, in the field of theoretical physics. His doctoral thesis investigated the potential existence of quantum gravitational effects in the early universe, proposing a primordial singularity avoidance scenario known as the Big Bounce. His research also explored possible observational signatures of this scenario within the gravitational waves spectrum. Currently, Artur is actively involved in a European Space Agency project focusing on the exploration of quantum machine learning applications for satellite data analysis. His primary focus revolves around the utilization of quantum kernel methods for classification tasks.

Gregor Czelusta


Grzegorz is a doctoral candidate at the Faculty of Physics, Astronomy, and Applied Computer Science at Jagiellonian University in Krakow. He earned his master's degree from the same department, with a thesis on a quantum gravity model of Causal Dynamical Triangulations. Grzegorz's current research involves using quantum algorithms to simulate physics phenomena at the Planck scale. He is investigating the relationship between the quantum structure of space-time and quantum information. Besides his PhD research, he is also participating in projects related to quantum machine learning in satellite data analysis as well as cryptography, both quantum and post-quantum.