Quantum computing for space applications 2025

19 Nov 2025, 00:00 → 21 Nov 2025, 17:00 Europe/Warsaw

H-0-11 (Jagiellonian University)

Description:

This workshop builds upon the success of last year’s event (https://indico.global/event/10051/), continuing the exploration of quantum computing’s potential in space applications, with a particular emphasis on satellite data analysis and quantum machine learning. It provides a platform for experts from academia and industry to exchange ideas, review recent advancements, and discuss the latest challenges and opportunities in the field. By fostering collaboration, the workshop aims to drive innovation and bridge the gap between theoretical progress and practical implementation, inspiring new approaches to utilizing quantum technologies for space exploration and satellite data processing.

Invited speakers

• Michal Belina (IT4Innovations)
• Piotr Czarnik (Jagiellonian University)
• Amer Delilbasic (Forschungszentrum Jülich / University of Iceland)
• Paolo Gamba (Università di Pavia)
• Piotr Gawron (Nicolaus Copernicus Astronomical Center / AGH University of Krakow)
• Francesco Mauro (University of Sanio)
• Jakub Nalepa (KP Labs / Silesian University of Technology)
• Tomasz Pecyna (Poznan Supercomputing and Networking Center)
• Enrico Prati (Università degli Studi di Milano Statale)
• Alessandro Sebastianelli (Euro-Mediterranean Center on Climate Change)
• Agata Maria Wijata (Silesian University of Technology)

 

Venue:

The workshop will take place at the Faculty of Physics, Astronomy, and Applied Computer Science, Jagiellonian University, Kraków.

Organising Committee: Artur Miroszewski, Jakub Mielczarek 

Find out more about the IEEE GRSS Quantum Earth Science and Technology Technical Committee (QUEST TC) !

Quantum Earth Science and Technology

Wednesday 19 November

08:30 → 09:30 Registration: Conference opening

Conveners: Artur Miroszewski, Jakub Mielczarek (Jagiellonian University)

08:30 Registation Opening

09:00 Workshop Briefing

Speaker: Artur Miroszewski

09:15 QUEST turns ONE! Birth of a special Technical Committee

Speaker: Silvia Liberata Ullo

09:30 → 11:00 Morning session: Wednesday morning session

09:30 Inserting Quantum Computing into AI chains: experiences in Hybrid Quantum Machine Learning for EO

Quantum Computing for Earth Observation (QC4EO) is a rapidly emerging, cutting-edge research field. Within this innovative context. In our research we introduced Quanv4EO, a novel quanvolutional approach to pre-process EO data, by extracting detailed feature maps from EO imagery. The resulting features are fed into a classical neural network (NN) to perform specific tasks. This proposed framework has been extensively validated on multiple EO benchmarks. On the EuroSAT dataset, it maintains an accuracy of 96% while drastically reducing the complexity of the subsequent NN, from tens of millions to just a few thousand trainable parameters. When integrated with an Attention U-Net for building segmentation, it results in a 93% parameters reduction with the same accuracy. For turbidity prediction with ΦSat-2 data, the parameters reduction is 98% with improved metrics. This framework aims at effectively replacing NN structures with millions of parameters with more agile QC layers, facilitating efficient, high-performance analytics for EO.

Speaker: Paolo Ettore Gamba

10:30 Quantum Difffusion Models in Earth Observation

Quantum diffusion models are rapidly advancing generative remote-sensing analytics by harnessing quantum computation to deliver higher-quality, more realistic satellite images, while converging faster and with substantially fewer trainable parameters than comparable classical diffusers. In this context, we first realise a fully-quantum latent diffusion model (QLDM) whose denoiser is a variational-quantum circuit acting on a 10-dimensionaI latent code. On EuroSAT, focusing on three land-cover classes Forest, Herbaceous Vegetation and SeaLake, QLDM lowers the Fréchet Inception Distance (FID) and Kernel Inception Distance (KID) by 21.5 % and 29.9 %, respectively, relative to a parameter-matched classical latent diffuser. Yet the severe spatial compression intrinsic to latent processing limits fine-grained detail. To restore image fidelity, we develop a hybrid quantum-classical architecture, the Quanvolutional Conditioned U-Net (QCU-Net), which inserts entangling quantum layers both at the U-Net bottleneck and as a quanvolutional filter early in the encoder. Trained on the full ten-class EuroSAT RGB set, QCU-Net reaches an FID of 2.57 and a KID of 0.0008, representing 64 % and 76 % improvements over the best classical diffusion baseline (FID = 7.22; KID = 0.0034), and boosts class-conditioning accuracy to 81.7 % versus 62.2 % for the classical model. Thèse gains confirm that embedding quantum circuits within the feature-extraction pipeline yields richer spatial-spectral representations than latent-space quantum processing alone.

Speaker: Francesco Mauro

11:00 → 11:30 Coffee break

11:30 → 13:00 Morning session: Wednesday morning session II

11:30 Advancing Quantum Computing for Space at FZJ

Forschungszentrum Jülich (FZJ) is actively engaged in developing quantum computing infrastructure and methods relevant to space applications. This talk provides an overview of the Jülich UNified Infrastructure for Quantum computing (JUNIQ) and outlines current research directions, with an emphasis on use cases in Earth observation.

Speaker: Amer Delilbasic

12:00 Integrating Quantum Machine Learning into Earth Observation Analytics: The Journey from the Clouds to the Soil

Satellite imagery is growing faster than our ability to analyze it. While artificial intelligence (AI) keeps advancing, Earth Observation (EO) still faces practical barriers tied to massive, high-dimensional data. In this talk, we discuss the practical challenges involved in processing EO data and building AI models for EO applications. We then explore how Quantum Machine Learning (QML) can fit into the broader landscape of EO data analytics, sharing our experience applying QML to real EO use cases, the lessons learned, and the intuition guiding the development of new QML methods for tasks such as feature (band) selection in hyperspectral data.

Speaker: Jakub Nalepa

12:30 Experimental Large-Scale Quantum Approximate Optimization For Ising Problems

Quantum Approximate Optimization (QAO) is an umbrella term for various methods that attempt to use quantum devices to find solutions to classical combinatorial optimization problems. QAO is widely considered one of the leading candidates for potentially useful quantum computation. In this talk, I will discuss some of our recent efforts on the experimental implementation of the QAO algorithms (most experiments were performed last year). Results include relatively performant optimization (see Table IV in [4]) of fully-connected spin glass systems on 82-qubit systems (on Rigetti's QPU) using p=1 QAOA implemented in conjunction with Noise-Directed Adaptive Remapping (NDAR) meta-algorithm that we developed in [1]. The talk will be based on (to a varying degree of detail) on the following works: [1] Filip B Maciejewski, Jacob Biamonte, Stuart Hadfield, Davide Venturelli, Improving Quantum Approximate Optimization by Noise-Directed Adaptive Remapping, Quantum 9, 1906 (2025). [2] Filip B. Maciejewski, Bao G. Bach, Maxime Dupont, P. Aaron Lott, Bhuvanesh Sundar, David E. Bernal Neira, Ilya Safro, Davide Venturelli, A Multilevel Approach for Solving Large-Scale QUBO Problems with Noisy Hybrid Quantum Approximate Optimization, 2024 IEEE High Performance Extreme Computing Conference (HPEC), 1-10 (2024). [3] Filip B Maciejewski, Stuart Hadfield, Benjamin Hall, Mark Hodson, Maxime Dupont, Bram Evert, James Sud, M Sohaib Alam, Zhihui Wang, Stephen Jeffrey, Bhuvanesh Sundar, P Aaron Lott, Shon Grabbe, Eleanor G Rieffel, Matthew J Reagor, Davide Venturelli, Design and execution of quantum circuits using tens of superconducting qubits and thousands of gates for dense Ising optimization problems, Physical Review Applied 22 (4), 044074 (2024). Other references: [4] Amira Abbas, et al, Challenges and opportunities in quantum optimization, Nature Reviews Physics volume 6, pages 718–735 (2024)

Speaker: Filip Maciejewski

13:00 → 14:30 Lunch

14:30 → 15:30 After-lunch session

14:30 Trapped Ions Quantum Computing

Speakers: Bartosz Grygielski, Grzegorz Czelusta

15:00 Optical alignment of entanglement sources on satellites using reinforcement learning

Quantum entanglement distributed using satellites enable quantum communication across the globe. Sources that produce entanglement are subject to misalignment due to degradation over a period and outer space conditions. In this work, we ensure optimal alignment of the entanglement source in automation for efficient generation of entangled photon pairs using a heuristic and reinforcement learning approach.

Speaker: Akshata Shenoy

15:30 → 16:00 Coffee break

16:00 → 17:30 Discussion Panel

Thursday 20 November

09:30 → 11:00 Morning session: Thursday morning session

09:30 Where is the fricking quantum advantage?

We have been looking for applications of quantum computing in Earth observations for almost a decade, yet nothing useful has been found. I believe the field is extremely important and interesting, but we should focus on investigating unconventional ideas—those that touch the foundations of quantum mechanics on one hand and explore computational methods that have emerged around quantum information in recent decades on the other.

Speaker: Piotr Gawron

10:30 HPC-QC integration for scalable space data processing

This presentation discusses how the convergence of high-performance computing (HPC) and quantum computing (QC) can enable new levels of scalability and efficiency in data-intensive computational workflows. By integrating classical and quantum resources within unified architectures, such hybrid approaches have the potential to significantly improve the processing and analysis of large and complex datasets. These capabilities may become particularly relevant in domains where massive data volumes, real-time processing, and advanced analytical precision are essential — including emerging areas of research and technology development related to space data.

Speaker: Tomasz Pecyna

11:00 → 11:30 Coffee break

11:30 → 13:00 Morning session: Thursday morning session II

11:30 Introduction to ITInnovations

In the talk, I will introduce IT4Innovations, the Czech Republic’s national supercomputing center, located at VSB – Technical University of Ostrava. I will provide an overview of the center’s mission, infrastructure, and its strategic role within the European high-performance computing (HPC) and quantum computing (QC) ecosystem.

The talk covers the architecture and evolution of supercomputers, including key systems operated by IT4Innovations, as well as the center’s involvement in hosting LUMI, one of Europe’s most powerful supercomputers, and VLQ, a quantum computer installed at IT4Innovations in collaboration with EuroHPC Joint Undertaking.

The presentation highlights the computational capabilities of these systems, their access models for researchers and industry, and their integration into European initiatives such as EuroHPC, AI Factories, and the CLARA Center for Artificial Intelligence and Quantum Computing. It also outlines IT4Innovations’ contributions to scientific excellence, innovation, and interdisciplinary research, particularly in areas such as artificial intelligence, data analytics, quantum computing, and neuroscience.

This session aims to showcase how IT4Innovations supports cutting-edge research and technological development, fostering collaboration across academia, industry, and European HPC infrastructure.

Speaker: Michal Belina

12:30 Tuning the Shape and Structure of Quantum Machine Learning Circuits for Optimal Image Recognition

The enormous amount of data from Earth observations requires machine processing. Quantum computers have the potential to be very helpful in this. When recognizing images using quantum machine learning (QML), classification accuracy is obviously very important. This talk will, among other things, show how the shape and structure of the parameterized quantum circuit (ansatz) used affects this accuracy and also the learning speed of the QML model.

Speaker: Jiří Tomčala

13:00 → 14:30 Lunch

14:30 → 15:30 After-lunch session: Thursday Afternoon

14:30 Data Augmentation for Quantum Machine Learning

Quantum machine learning faces several bottlenecks that impede its empirical and theoretical computational advantage. One critical challenge is encoding classical data to quantum states. In this work, we present a novel data augmentation strategy applied after encoding, resulting in faster convergence with less data on quantum machine learning models. We demonstrate its effectiveness on generative diffusion-inspired models, showing that even limited datasets can be utilized for learning distributions.

Speaker: Mariia Baidachna

15:00 Quantum Gravimetry

Speaker: Adam Ciesielski

15:30 → 16:00 Coffee break

16:00 → 17:30 Discussion Panel

Friday 21 November

09:30 → 11:00 Morning session: Friday Morning Session

09:30 Is there room for quantum advantage in quantum computing for space science and technology

I discuss a number of space applications ranging from job shop scheduling problems, exoplanet observation, earth observation, material design and others in order to address the impact of quantum computing in terms of - but not limited to - quantum speed-up, The role of the different quantum computing hardware technologies is discussed.

Speaker: Enrico Prati

10:30 Quantum Computing for Earth Observation: Current Perspectives and Future Directions

Quantum computing is emerging as a powerful paradigm for advancing Earth observation, enabling novel approaches to data analysis, image reconstruction, and modeling of complex geophysical processes. Recent research has demonstrated how quantum machine learning and hybrid quantum–classical architectures can potentially enhance the efficiency and interpretability of satellite data workflows. This talk outlines the current state of quantum methods applied to Earth observation and discusses the role of open collaboration and community-driven development in accelerating progress. Particular attention is given to how these advancements can be extended toward broader Earth system and climate modeling applications, fostering a convergence between quantum technologies and environmental science.

Speaker: Alessandro Sebastianelli

11:00 → 11:30 Coffee break

11:30 → 13:00 Morning session: Friday Morning Session II

11:30 Introduction to Quantum Error Mitigation

Speaker: Piotr Czarnik

12:30 TRACKING THE INVISIBLE ENEMY: METHANE DETECTION

Methane detection and monitoring are crucial for environmental surveillance and emissions management, as methane is a potent greenhouse gas with a global warming potential 28 times higher than carbon dioxide. This task poses practical challenges due to variable concentrations, irregular shapes of methane plumes, and the need for resource-frugal algorithms for onboard satellite processing, given the limited downlink bandwidth for large hyperspectral images. Machine learning (ML) methods—ranging from classical to deep and quantum approaches—offer global scalability for methane detection, but their deployment requires the ability to generalize to target data.

Speaker: Agata Wijata

13:00 → 14:30 Lunch

14:30 → 15:00 Workshop closing

15:00 → 16:00 Discussion Panel: Loose discussions, farewell