Electronics Engineering

MASSIMO PANELLA
ANTONELLO ROSATO

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.

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.

Basic knowledge of electronics and signal processing systems.

Introduction to Quantum Computing
Introduction to quantum computing, mathematical formalisms. Data encoding. Elementary unitary transformations, Quantum Gate Arrays. Main quantum optimization algorithms, adiabatic approaches, Quantum Approximate Optimization Algorithm (QAOA). Variational approaches, Quantum Machine Learning and Quantum Neural Networks. Quantum RNN and Quantum GRU.

Practical exercises in Quantum Computing
Implementation of deep learning and quantum computing algorithms in Python with Qiskit, Pennylane, and JAX. Time series prediction using quantum models. Quantum Generative Models and quantum optimization on cloud platforms.

Review of machine learning methods
Introduction to machine learning and data-driven modeling: data preparation; generalization; regularization and structural optimization. Overview of the main clustering and classification methods; time series prediction.

Neural Networks and Deep Learning
Overview of shallow feed-forward and recurrent neural networks. Introduction to Deep Learning, specific problems and solutions (double descent, vanishing/exploding gradient, barren plateau, dropout, ensembling, weight initialization). Deep feed-forward and recurrent neural networks. Generative and diffusive systems (GAN, VAE, etc.).

Introduction to Hyperdimensional Computing
Fundamental concepts of hyperdimensional computing: data representation using high-dimensional vectors and their sparse distribution properties and robustness. Vector symbolic architectures and computational models for learning and memory. Binding and superposition methods for encoding complex information. Associative memory management and hybrid Quantum-HDC approaches. HDC-based learning strategies for classification, regression, and eXplainable AI problems.

Case Studies
Energy prediction from renewable sources, smart grids, and distributed energy systems. Radar, satellite, and multispectral signal processing using quantum neural networks. Analysis and design of complex digital circuits using quantum and HDC-based optimization. Implementation of machine learning algorithms in HDC-based embedded systems. Practical applications of HDC in industrial and information engineering, time series analysis, anomaly detection, and resource-constrained embedded systems. Design of semiconductor devices and smart sensors for environmental observation. Biometrics, behavioral analysis, and digital signal classification using quantum circuits and neural networks.

M. Schuld and F. Petruccione, Supervised Learning with Quantum Computers, Springer Nature, Switzerland, 2018

M. Schuld and F. Petruccione, Supervised Learning with Quantum Computers, Springer Nature, Switzerland, 2018
A.F. Kockum, et al., Lecture notes on quantum computing, arXiv preprint [2311.08445], 2025
O. Simeone, An Introduction to Quantum Machine Learning for Engineers, arXiv preprint [2205.09510], 2022
R. de Wolf, Quantum Computing: Lecture Notes, arXiv preprint [1907.09415], 2023
C.C. Aggarwal, Neural Networks and Deep Learning, Springer Cham, Svizzera, 2023

Teaching in presence.
In cases of force majeure (health emergencies, etc.) the remote and/or blended methods will also be activated as per current rules.

Compulsory attendance is not required.

Oral questions on the topics of the course.

Practical and theoretical questions as reported in the texts and manuals provided.

• General and contextual concepts on Quantum Computing [20 hours]


• Applications of Quantum Computing [10 hours]


• General and contextual concepts on Neural Networks [20 hours]


• Applications of Neural Networks [10 hours]