Month: April 2024

AILAB-Udine is proud to be one of the organizers of the European Summer School on Quantum AI, (EQAI), which has been held yearly since 2022. Find the latest updated information on the official website:http://eqai.eu Or subscribe to the official google group to be notified of any major update!https://groups.google.com/g/eqai The summer school will take place in Lignano Sabbiadoro (Italy) on September 02 – 06, 2024.All the speakers will be there in person to make the experience more immersive and interactive! Find out more about the past editions at: https://eqai.eu/past-editions/ EQAI 2023 – Details The summer school will take place in Udine (Italy) on May 29 – June 1, 2023, but it can also be followed remotely. The main topic of this edition is “Quantum Machine and Deep Learning”. Deadlines for application are: ▶ on-site: April 29, 2023, ▶ remote: May 15, 2023. All the speakers will be there in person to make the experience more immersive and interactive. The program will […]

Quantum machine learning (QML) is an emerging field that combines the principles of quantum computing with classical machine learning techniques. It aims to exploit the unique properties of quantum systems, characterised by their ability to perform computations exponentially faster than classical devices, to improve the processing and analysis of complex data. This advance makes QML a very promising frontier. However, in the current Noisy Intermediate-Scale Quantum (NISQ) era, characterised by the presence of noise in quantum devices that limits their scalability, it is crucial to develop specific techniques to fully exploit quantum capabilities. This research focuses on the development and optimisation of QML models suitable for NISQ environments. A key aspect of our work is to adapt established optimisation methods from classical machine learning, such as batch normalisation and regularisation, for use in Quantum Neural Networks (QNNs). The project also explores the potential of Quanvolutional Neural Networks and Quantum Kernel methods. These efforts are aimed at improving the functionality and […]