4th European Summer School
on Quantum AI
September 01 – 05, 2025
Lignano Sabbiadoro, Italy
Organized by
This school is supported by the Department Strategic Plan (PSD) of the University of Udine—Interdepartment Project on Artificial Intelligence (2020-25).
Motivation
Quantum computing is a rapidly evolving field with the potential to transform many areas of science and technology. Quantum Machine Learning, which is the application of quantum computing to ML tasks, is an emerging field that has already shown promising results in some applications, and a promising future for Artificial Intelligence at large. These technologies could allow incredible advancements in various fields, tackling various issues such as complex physical simulations and optimization problems.
This summer school aims at helping students and researchers to learn about the latest developments in this field, and provide a platform to discuss new ideas and applications.
It is crucial to study ways to develop efficient and effective Quantum ML algorithms to take advantage of this new technology, and these technologies have the potential to revolutionize the way we approach problems and make significant progress in areas such as drug discovery and material science, and optimization.
Quantum ML is an interdisciplinary field that requires expertise from multiple areas, including physics, mathematics, information theory, and computer science. By bringing together experts in these fields and quantum enthusiasts from both academia and industry, the summer school aims to foster collaboration and cross-disciplinary thinking.
There are also a lot of open challenges in quantum ML. While small-scale quantum ML is possible, scaling it to larger datasets is extremely challenging due to limited physical memory and the noisiness of current quantum computers. Therefore, it is of utmost importance to spread knowledge about this topic and involve new researchers who can contribute to its progress.
Objective
Following the success of the first editions, this summer school aims to provide an objective and clear overview, as well as an in-depth analysis, of the state-of-the-art research in Quantum Machine Learning and Deep Learning.
The courses will be delivered by world renowned experts in the field, from both academia and industry, and will cover both theoretical and practical aspects of real problems.
The school aims to provide a stimulating opportunity for young researchers and Ph.D. students, but also quantum enthusiasts coming from the industry and outside of academia. The participants will benefit from direct interaction and discussions with experts in this field. Participants will also have the possibility to present the results of their research, and to interact with their scientific peers, in a friendly and constructive environment.
A brief context
Over the last century, the introduction of computers has drastically transformed the world of science, technology, and society. The first computer, developed around the 20th century, was incapable of performing computations on its own. However, today, compact devices can solve complex problems instantaneously and accurately, given the appropriate inputs and instructions. While computers and their components have been continually optimized for performance, speed, and size, we are now approaching the point where the only way to enhance their computational power is to work at atomic levels. Although this presents both tremendous potential and substantial challenges, quantum computing offers a solution. This new kind of computing, which is based on quantum mechanics, utilizes subatomic particles, such as atoms, electrons, and photons, as bits, exploiting their probabilistic nature. Quantum computers can solve any problem that classical computers can and vice versa, but they can do so with reasonably lower time complexities, leading to what is known as “Quantum Supremacy.” Since in classical machine learning, the model performance is related to the size of the training dataset and the allotted training time, quantum machine learning enables us to train models on larger datasets while overcoming current time constraints. While QML is still a nascent field, there have already been some promising examples where it has shown significant potential, e.g., for image classification and part-of-speech tagging.