Main Goals. The main goal of the course is to provide the student with knowledge of the basic notions regarding the design and implementation of quantum algorithms and quantum computing architectures for machine learning and artificial intelligence, in order to deal with variational quantum circuits and quantum neural networks learning. The problems related to the design, implementation and testing of quantum computing architectures and quantum machine learning computational models will be considered, for the solution of both supervised and unsupervised learning problems such as optimization, prediction, clustering and classification, in real-world applications concerning signal, data and information processing. All of this also through systematic laboratory activity, during which the methodologies relating to the design and implementation of quantum computing architectures as well as quantum machine learning models such as quantum neural networks will be taken into consideration.

Prerequisites. Fundamentals of Physics and Mathematics as per the basic courses of the three-year (Bachelor) Degree.

Expected Learning Outcomes. Study of computational models, circuits and architectures along their universality, as well as on the explanation of the main algorithmic techniques exploiting quantum physics using model abstraction, in order to solve hard computational problems. The fundamentals of data-driven learning approaches will be acquired for applications to real-world problems, with specific implementations using quantum circuits and quantum neural networks along with the use of existing software platforms. The student will understand how to gain quantum advantage in applications related to data-driven learning problems such as time series analysis, Hyperdimensional Computing, and eXplainable AI, considering several real domains pertaining to energy, aerospace, earth observation, behavioral analysis, bioengineering, finance, fraud detection, and so forth. Learning skills regard autonomous and self-managed study activity during the development of monothematic homework for didactic and/or experimental investigation, i.e., in a vertical way on some specific theoretical and applicative topics using, for instance, available cloud-based quantum systems like IBM’s Quantum Experience Platform, as well as quantum simulators like Qiskit, Pennylane and Flax.