Electronics Engineering

KNOWLEDGE AND UNDERSTANDING The student will acquire 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. This will be based on the 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. CAPABILITY TO APPLY KNOWLEDGE AND UNDERSTANDING Solution of problems related to the design, implementation and testing of quantum computing architectures and quantum machine learning computational models 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. The main objective is to provide the student with the ability to understand and achieve 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. MAKING AUTONOMOUS JUDGEMENTS Through a systematic laboratory activity, during which the methodologies related to the design and implementation of quantum computing architectures and quantum machine learning models such as quantum neural networks will be considered, the student will integrate the acquired knowledge to manage the complexity of inductive learning mechanisms and the actual limits imposed by currently adopted Noisy Intermediate-Scale Quantum (NISQ) devices, even starting from the limited information due to the practical organization of the course. COMMUNICATE SKILLS Quantum technologies and quantum information processing algorithms are rapidly evolving, considering the actual scenario based on near-term devices and hybrid quantum-classical approaches. Following this course, the student will be able to communicate the knowledge acquired to specialist and non-specialist interlocutors in the fields of research and work in which she/he will carry on the subsequent scientific and/or professional activities, also considering technological and sustainability issues. LEARNING SKILLS The adopted teaching methodology requires an 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.