Quantum AI is an interdisciplinary field that combines the power of quantum computing with the ability of AI to learn from data. This merger seeks to create more efficient and intelligent tools than those that currently exist.

Quantum AI promises profound transformations in how problems will be addressed and appropriate solutions designed and is an area of research and application with growing potential.

New opportunities for research and practical application of these technologies will be shown, such as the resolution of complex problems, more efficient machine learning and robust artificial intelligence systems.

Presenter:

• Pol Torres, Line Manager, Applied Artificial Intelligence (AAI), Eurecat

Participants:

• Queralt Portell, Quantum data scientist, GMV

• Jordi Riu, Lead Engineer, Qilimanjaro Quantum Tech, S.L.

• Pablo Lauret, Pre-Sales Engineer, Multiverse Computing

www.gmv.com

Queralt Portell has a degree in mathematics and physics from the Autonomous University of Barcelona. She has made several internships in research centers and in the company Qilimanjaro oriented to the world of quantum computing, both from the hardware and the software angle. She joined the BDA department of GMV in 2023 as a quantum data scientist and is currently studying the use of quantum computing for artificial intelligence applications.

CUCO is the first major quantum computing project in Spain and is led by GMV. It seeks to advance in the state of the art of quantum algorithms and apply them in a series of use cases for different sectors of the Spanish economy. GMV is working on its own lines of research applying Quantum Machine Learning and Quantum Optimization techniques at the industrial field of Earth observation. The three use cases studied are: the dentification of photovoltaic plants in satellite images, optimization in the taking of satellite images and the short-term prediction of wind speed and direction in areas of interest.

www.qilimanjaro.tech

Jordi Riu holds a degree in Engineering Physics from UPC and a Master’s in Artificial Intelligence from the same university. He has 5 years of experience developing quantum algorithms for optimization problems in various industrial sectors, as well as applications in Quantum Machine Learning. Before joining Qilimanjaro, he worked for 2 years as a technology consultant at Accenture. He currently leads the consulting and applications team at Qilimanjaro.

In this presentation, the interaction between Artificial Intelligence (AI) and Quantum Computing will be discussed. It will showcase how AI techniques are optimizing current NISQ quantum computing, and how future quantum computing can enhance AI, including examples of how this interaction is creating opportunities for businesses and how to overcome current challenges. This presentation will serve as a guide to understanding how these two technologies are shaping the future of computing.

https://multiversecomputing.com/

Pablo Lauret has an integrated masters degree in Mathematical Physics from the University of Edinburgh, with a masters thesis in phenomenological particle physics. Prior to joining Multiverse Computing, he worked at the Irish Centre for High-End Computing in Galway, Ireland. There, he worked as part of a team of researchers developing quantum-classical hybrid ML models for natural language processing tasks, and also served as a HPC user support specialist. As a Pre-Sales Engineer, his responsibilities include providing technical expertise during the sales process, engaging with potential customers and working with the product team to relay client feedback.

Despite great progress in the development of quantum computers, there remain substantial technical challenges to be resolved.

This, paired with the cost and scarce resources of these devices, raises the following question: can we take advantage of the properties of quantum computation today without the need to rely on quantum hardware? The answer is yes, through the use of a mathematical framework known as Tensor Networks (TNs). As the global leader in value-based TN-powered solutions, Multiverse Computing has successfully implemented TNs across a wide array of industries, including finance, energy, defense, and manufacturing.

In this presentation, TNs will be introduced and a particular application in the field of model compression will be presented (CompactifAI), to showcase the power of this novel, promising technology.

www.eurecat.org

Pol Torres holds a degree and a PhD in Physics from the UB and UAB respectively. He has more than 10 years of experience in the development of simulation algorithms and predictive models in the industrial, energy and materials science fields. During his career he has collaborated in various international research groups such as the Energy Conversion and Storage group at the Denmark Technical University (Denmark), the Electronic Engineering group at the Birck Nanotechnology Center (USA) or the Thermal Energy Engineering group at The University of Tokyo (Japan). He currently leads the energy research line in the Applied Artificial Intelligence unit at Eurecat.