Quantum computing for
Earth Observation
Introduction to Quantum Computing and its Ecosystem
Quantum Computing (QC) offers the potential to solve problems that classical computers (and even supercomputers) have a hard time dealing with. QC is a completely new computing paradigm and it could be a game-changer in fields such as cryptography, artificial intelligence and drug discovery. In this talk, we will explore the basics of QC and delve into the most promising algorithms currently being developed for Noisy Intermediate Scale Quantum (NISQ) devices. We will also examine the current state of quantum hardware and software available today and discuss the European ecosystem of projects involving quantum computing, highlighting key players and notable developments in this field. The goal of this talk is to provide a clear understanding of the current state of the Quantum Computing ecosystem and its potential for future advancements.
Instructors
Riccardo Mengoni
Biography:
Riccardo Mengoni obtained his master's degree in theoretical physics at University of Camerino. He then moved to University of Verona for his PhD, where he worked on Quantum Machine Learning. In summer 2018 he was selected for the USRA Quantum Academy that gave him the opportunity to be a NASA intern working on Quantum Annealing applications. Currently he is a Quantum Computing (QC) specialist at CINECA High Performance Computing (HPC) center working on QC and its integration with HPC."
ESA’s Quantum Computing for Earth Observation (QC4EO) Initiative: Current Activities and Perspectives
The AI-enhanced Quantum Initiative for EO is a recent initiative from ESA Earth Observation Programmes to assess the potential of Quantum Computing for Earth Observation (QC4EO). Indeed, quantum computing has the potential to improve performance, decrease computational costs and solve previously intractable problems in EO by exploiting quantum phenomena. In this talk we will present our current activities for discovering possible synergies between QC and EO, exploring first promising use-cases, and gathering both communities to prepare the ground for the opportunities which will arise with quantum computing developments.
Instructors
Bertrand Le Saux
Biography:
Bertrand Le Saux (Ms. Eng. 1999, MSc. 1999 INP Grenoble, PhD 2003 Univ. Versailles / Inria, Dr. Habil. 2019 Univ. Paris-Saclay) is a scientist with the European Space Agency ESRIN/Phi-lab, in Frascati (IT). He is working on data-driven techniques for visual understanding, with a background in machine learning and computer vision. He's interested in tackling practical problems that arise in Earth observation, to bring solutions to current environmental and societal challenges. He has been a researcher at CNR/ISTI Pisa (IT), Univ. of Bern (CH), ENS Cachan (FR) and ONERA (FR). He was co-chair [2015-2017] and chair [2017-2019] for the IEEE GRSS technical committee on image analysis and data fusion (IADF TC). He is currently an associate editor of the Geoscience and Remote Sensing Letters. He is also a co-organiser of the CVPR / EarthVision workshop series.
Is Space ready for the Quantum Leap? A Thales Alenia Space Perspective on Quantum Technologies for Earth Observation
Applied quantum mechanics represent an incredible opportunity for the whole space sector, enabling novel applications and unprecedented performances, transversally over the Space, Ground, and User Segment. The main issue lays in the broadness of the quantum technologies domain, featured by an extremely high variance in terms of maturity and complexity of the solutions. This section indeed aims in exploring what Thales Alenia Space, leader in the Earth Observation (EO) Satellite System Integration, sees as potentials technologies impacting its product portfolio in the upcoming years, showing the EO missions in which we see a tangible impact. This will be made by analyzing the state-of-the-art Quantum Technologies and progresses, critically evaluating their applicability, potentials, and limits, discriminating between short-term, to long-term impact over the EO Space Sector.
Instructors
Tommaso Catuogno
Biography:
Tommaso Catuogno, System Engineer at Thales Alenia Space, belongs to the Observation and Navigation domain (DON-I). He is now the Technical reference in the domain for Quantum Technologies within the R&D of the DON-I domain. In this context he is the Reference interface for TAS over the Quantum Computing Spoke of the Italian National Center in High-Performance Computing. Regarding his background, He achieved his M.Sc graduation at Scuola Superiore Sant'Anna in High-Performance Computing and Communications, pursuing a curriculum that allowed his specialization in Optical communications and Photonic technologies. He has been working as researcher at Scuola Superiore Sant’Anna, exploring the field of Capacity estimation problems on the nonlinear optical channel, designing Entropy Estimators for its estimation. He has been part of the R&D team in Optical & Photonic Systems at Ericsson Research, where he focused on Quantum Technologies, attempting to identify the potentials and limitations preventing the industrial deployment of these technologies. In this context, he filed a plethora of patents identifying novel schemes for applying Quantum Technologies to the industrial sector, contributing altogether to the writing of two Technical manuals on the subject.
Instructors
Mattia Verducci
Biography:
Mattia Verducci received hisB.Sc. and M.Sc. degrees in Physics from the University “La Sapienza” of Rome,in 2014 and 2017, respectively. He specialized in experimental quantum opticsworking in the Quantum Information Lab in Rome, dealing with quantum simulationof Anderson localization with integrated photonics. He is currently working inThales Alenia Space as system engineer in the R&D area of the DomainObservation and Navigation Italy (DON-I). Here, his main mission is to help thecompany to enable new ground and space solution for EO and navigation based oncutting-edge technologies like quantum and photonic processing. He hasconsolidated experience in the field of machine learning and big dataanalytics, gained from previous work experiences.