Università degli Studi del Sannio di Benevento

Artificial Intelligence for Earth Observation powered by High Performance Computing and Quantum Computing



This course introduces cutting-edge HPC systems and emerging quantum computing technologies for Machine Learning (ML) and DL methods to address EO applications that deal with large and complex RS datasets.


Deep Learning (DL) is emerging as the leading Artificial Intelligence (AI) technique owing to the current convergence of scalable computing capability (i.e., High-Performance Computing (HPC) and Cloud computing), easy access to large volumes of data, and the emergence of new algorithms enabling robust training of large-scale deep Neural Networks (NNs). HPC systems are an effective solution to the challenges posed by big data analytics. In particular, applications which collect and store massive amounts of data can profit from them significantly. Modern Earth Observation (EO) programs, governments and space agencies are opening their archives (e.g., ESA’s Copernicus, NASA’s Landsat), making massive volumes of RS data available to everyone. Present and upcoming space missions use small satellite constellations to acquire data with higher spatial, spectral or temporal resolution. Other sources, such as ground and airborne sensors also provide a continuous stream of data. In this context, the main challenge is to identify efficient approaches to extract interpretable information and knowledge from this pool of data, possibly in near-real time and by integrating between different disciplines. This course will introduce students to cutting-edge HPC systems and emerging quantum computing technologies for Machine Learning (ML) and DL methods to address EO applications that deal with large and complex RS datasets.


  • To provide an overview of the state-of-the-art and future AI supercomputers
  • To highlight the importance of AI for EO applications
  • To understand parallel strategies for high performance distributed deep learning
  • To highlight the potential of cutting-edge quantum computing technologies and their future impact in EO applications

Learning Outcomes

After completion of the course, the students/learners will be able to:

  • Describe different HPC and Quantum Computing systems and estimate their potential for applications with data intensive computing requirements
  • Parallelize and scale DL models on supercomputers equipped with GPUs
  • Execute ML algorithms on real quantum computers

Course Outline

This course will start with an overview of the computational requirements emerging from EO applications and will present state-of-the-art and future computing systems and hardware technologies that are resulting from the current convergence of AI with HPC. The course will recap important ML and DL concepts and algorithms and explain how to enhance the processing on supercomputers.  The course will also cover the basics of quantum information processing and physical principles of the quantum computers. It will introduce elements of quantum ML and their future potentials.

Lecture 1: Course Introduction

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Lecture 2.1: Levels of Parallelism and High Performance Computing

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Lecture 2.2: Getting started with a Supercomputer

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Lecture 2.3: Introduction to MPI

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Lecture 3: Deep Convolutional Neural Networks (CNNs)

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Lecture 4: Distributed Deep Learning

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Lecture 5.1: Quantum Computing Technologies

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Lecture 5.2: Quantum Support Vector Machine (SVM) with Quantum Annealing

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Lecture 5.3: Quantum SVM with Quantum Circuits

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