GMV showcases the advances it has made by applying quantum computing to the optimization of Earth observation satellite imaging

Quantum computing is a new frontier in the fields of science and technology, harboring the potential to solve complex problems and revolutionize industry. Although still at an early stage, advances in the creation of qubits and quantum algorithms are bringing quantum computing ever closer within reach.

For example, the space sector is faced with the problem of selecting the optimal subset of images to be captured by a satellite, the solution to which is extraordinarily complex due to a combinatorial explosion. To make matters worse, requests for imagery often arrive over time, which in practice means solving several instances of the problem in short time periods. Overcoming this type of problem more efficiently can provide a significant competitive advantage, as well as paving the way for solving more complex problems in the future, such as those involving multi-satellite constellations, which require even longer computation times.

At the QBN Meeting on Quantum Computing & Applications, GMV data scientist Antón Makarov presented the results of the research we are spearheading to solve this optimization problem using quantum computing. This work is part of the CUCO project, the first major business-led quantum computing project at national level, which aims to advance the state of the art of quantum algorithms and apply this knowledge to a series of proofs of concept in various strategic sectors: energy, finance, space, defense, and logistics. The CUCO project has been funded by the Centre for the Development of Industrial Technology (CDTI) and is supported by the Spanish Ministry of Science and Innovation under the Recovery, Transformation and Resilience Plan.

The event was hosted by Quantum Spain and Qilimanjaro Quantum Tech at the Barcelona Supercomputing Center and involved the participation of fellow CUCO project partners, such as BBVA and Repsol.