CUCO project: Optimizing satellite image acquisition

CUCO is the first major national and corporate quantum computing project. This project aims to advance the state of the art of quantum algorithms and apply this knowledge to a series of proofs of concept in different strategic sectors of the Spanish economy, such as energy, finance, space, defense and logistics. Research will revolve around use cases in Earth observation, the fight against climate change, environmental protection, information traceability throughout supply chains, optimization and simulation of complex financial calculations, and signals intelligence, to name a few. The project has been subsidized 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.

Use case: Space

The field of Earth observation tackles a wide range of problems, generally by analyzing images taken in different radiation or electromagnetic emission bands. However, prior to analysis, satellite operators are faced with a resource optimization problem, which can be described as follows:

Given a set of images requested for a satellite orbit pass, the goal is to determine which subset of images should be taken in that orbit pass while optimizing certain measure(s) (benefit, importance, capacity, etc.).

In almost all cases, taking the full set of requested images is not feasible, since the satellite orbit is fixed and there are a number of constraints that limit the possible image combinations that can be captured. For example, some images cannot be taken with the same camera because of restrictions relating to the maneuvering time, capture time, geographical proximity, etc.

This problem pertains to the field of combinatorial optimization. Specifically, it can be regarded as a binary linear optimization problem with constraints, which we know belongs to the NP complexity class. We can start to grasp the extent of this complexity by looking at a very simple example: suppose we have to choose a subset of 30 requested images. This means that we can take a single image, any combination of two images, any combination of three images, and so on until we have taken all 30. We can write this as:

Of these more than a billion possible solutions, we have to discard those that do not fit the constraints of the problem, which, of course, is no small feat. Finally, we have to assess each of the remaining solutions to find the one that maximizes value.

With only 30 images, the problem is already quite complex. In practice, where the problems to be solved involve thousands of images, it is easy to see why any classical algorithm is inefficient, making it necessary to discard exact methods outright and forcing recourse to (meta) heuristic algorithms, whose execution times and guarantees could be improved by quantum algorithms.

One of the most promising quantum computing paradigms today is quantum annealing (QA), which is particularly well suited for optimization problems. Broadly speaking, the way it works is the quantum analogue of throwing a ball from the top of a mountain range: the ball will naturally seek the state of minimum energy, a valley. Our task is therefore to prepare this ball and mountain range mathematically to represent the problem we want to solve and run quantum algorithms, harnessing concepts such as superposition, entanglement, coherence, and tunneling to solve the problem more efficiently than with classical computation.

This problem was not chosen out of the blue. After a lengthy assessment of over 15 different use cases, it was identified as an everyday issue in the aerospace industry. Optimizing image acquisition is a problem that every satellite operator has to deal with on a regular basis. What’s more, image requests often arrive over time, and in practice it is necessary to solve several problems in a single plan, including new images as they arrive. Reducing the execution time of these algorithms can offer a competitive advantage, as well as paving the way for solving future problems with multiple satellites, which require even longer computation times.

Our common challenge

We are witnessing a so-called second quantum revolution, which is all about leveraging the enormous advances made in recent years in our ability to manipulate matter at the quantum level. These advances are driving rapid developments in the field of quantum computing, and these will have a massive impact on artificial intelligence, i.e. understanding computing technologies under this umbrella to solve problems of all kinds related to perception, interaction, understanding, simulation, prediction, recommendation, optimization, and so on. Artificial intelligence is the key technology in digital transformation. AI’s ability to model, infer, decide, and act will make it possible to efficiently orchestrate autonomous mobility, precisely match energy production to instantaneous consumption, perfectly synchronize logistics chains to production and supply needs, match food production to demand, and optimize many, many more processes with a social, economic, or environmental impact.

To be ready when all these technologies are fully deployed, capabilities need to be built now. Organizations will need to develop a unique and complex mix of talent to create impactful applications. The combination of knowledge and application disciplines required in this new field is extremely tough to create and replicate, and needs sustained consolidation time.

This project within the CDTI Missions Program is helping to create a collaborative platform of leading-edge capabilities in quantum computing that will accelerate the deployment of applications with a sustainable impact on strategic industries in Spain.

To achieve this overarching objective, the CUCO project is built around the following four key pillars:

• A focus on creating a portfolio of complementary and meaningful business cases. The current limitations of quantum computing platforms condition the delivery of practical solutions from a commercial viewpoint. This has prompted the selection of complementary and relevant business cases that address problems in multiple sectors and can scale in complexity. In this way, as the hardware evolves, the quantum technology-native algorithmic solutions developed in the project will be able to scale easily and handle the full complexity of the problems addressed.
• Development of quantum computing-native technology for the type of problems in the business cases. This involves creating proprietary technology for data coding and algorithmic approaches to solve the problems posed by the business cases: optimization, machine learning, and simulation, both quantum-native and hybrid and quantum-inspired.
• A strengthened, specialized and open community for the conceptualization, identification, and development of end-to-end solutions. Dense interaction among the teams of participating companies and public research bodies, or collaboration with other institutions in an open scheme, is a way to accelerate learning and build a stronger ecosystem geared towards quantum applications with an impact on industry.
• Contribution to closing the market gap. The market success of applications, products, and services derived from quantum computing technologies requires compact ecosystems that help to assemble the supply and demand for specific solutions based on these technologies. The project is aimed precisely at domains where the potential demand for these types of solutions has strengths: more compact value chains, greater number of agents in the application value chain, better competitive standing, direct access to global drivers, capacity to influence regulatory and standardization aspects, associated policies, etc. It is thus expected to narrow the market gap from the earliest stages.

Strategically, by the end of the project, the first capabilities with critical mass for the development of quantum computing solutions will have been developed, helping to build a unique ecosystem in this field in Spain.

This article is part of a three-part series aimed at describing several of the use cases that have been selected by the project partners and that will be studied over the next three years.

Authors of the articles:

• Antón Makarov Samusev, Data Scientist at GMV
• Rosalia Esquivel Méndez, Project Manager at GMV
• Esther Villar Rodríguez, Team Leader: Quantum Technologies at TECNALIA Research & Innovation
• Guillermo Gil Aguirrebeitia, Impact Development: Quantum Technologies at TECNALIA Research & Innovation
• Jorge Luis Hita, Quantum Computing Researcher at BBVA
• Carlos García Meca, Research Director at DAS Photonics