EQAI 2022
European Summer School
on Quantum AI

Quantum Machine Learning: from Fundamentals to Applications

September 12-16, 2022 Udine, Italy

Ph.D. opportunities. If you are a prospective Ph.D. student interested in Quantum Machine Learning, Artificial Intelligence, and Deep Learning Research at the University of Udine, please contact us (Giuseppe Serra – giuseppe.serra@uniud.it)


Quantum Machine Learning is the most promising, sustainable future for Artificial Intelligence.

The application of Quantum Computing to ML/AI is one of the most exciting prospective applications of quantum research. It stems from a combination of physics, mathematics, information theory and computer science and has the potential to provide high computational power, less energy consumption and exponential speedup over classical computers.
This will allow incredible advancements in various fields, tackling various issues such as complex physical simulations and optimization problems.

It is crucial to study ways to develop efficient and effective Quantum ML algorithms to take advantage of this new technology.
There are also a lot of open challenges: the output of quantum computations is noisy and current quantum computers have a really limited memory. It is therefore of utmost importance to spread knowledge about this topic and involve new researchers who can contribute to its progress.


This summer school aims to provide both an objective and clear overview and an in-depth analysis of the state-of-the-art research in Quantum Machine Learning and Quantum AI.
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. 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

The advent of computers has revolutionized science, technology and society massively in the last century. The first computer around the 20th century was not capable of performing computations on its own, while nowadays pocket-sized pieces of technology can instantly and accurately solve complex problems (given the right inputs and sets of instructions).
Computers and their physical parts have been constantly optimized in terms of performance, speed and size, but we are reaching the point where the only way to pack more computational power in them is to work at atomic levels. This unlocks both incredible potential and great challenges.
Quantum Computing is a new kind of computing based on Quantum mechanics, that uses subatomic particles (e.g. atoms, electrons, photons) as bits and exploits their probabilistic nature. They can solve any problem that a classical computer can, and vice-versa. But quantum computers can solve such problems in reasonably and exponentially lower time complexities (“Quantum Supremacy”). This exponential speedup is a game-changer in the field of machine learning, where the performance of the models is often tied to the size of the training dataset and the time allotted to the training process. The more data and time are given to a model, the better the results. Nowadays, models are often trained on smaller sets of data because of time constraints, but a Quantum Machine Learning model would allow us to train models on larger datasets without taking exponential time.