Next NEASQC webinar: Managing uncertainty in AI with Quantum Computing

In October, we will present some outcomes of the NEASQC use case on Quantum rule-based systems (QRBS) for breast cancer detection.

Emeritus Professor Vicente Moret-Bonillo and PhD student Samuel Magaz-Romero from Universidade da Coruña will share their findings, which are applicable to our use case, as well as to many other domains.

With the rise of big data and AI systems, which have taken over most industries in recent years, a problem has emerged that can be difficult to address: uncertainty. From incomplete data to disagreements between experts, uncertainty is a key aspect of information when solving any reasoning problem. Here we present a new approach: using the intrinsic probabilistic nature of quantum computing to represent and manage uncertain information, we develop several models that allow the implementation of AI systems in quantum machines. With this proposal, users can model their systems classically, defining the different facts and rules that make up their inference process, and translate them seamlessly into a quantum circuit that operates with uncertainty. In this webinar, we will present the theoretical basis of the work, reviewing the elements of classical AI systems and how we can bring these structures into the world of quantum computing.