Quantum machine learning: development and evaluation of the Multiple Aggregator Quantum Algorithm

Orazi, Filippo (2022) Quantum machine learning: development and evaluation of the Multiple Aggregator Quantum Algorithm. [Laurea magistrale], Università di Bologna, Corso di Studio in Artificial intelligence [LM-DM270]
Documenti full-text disponibili:
[img] Documento PDF (Thesis)
Disponibile con Licenza: Salvo eventuali più ampie autorizzazioni dell'autore, la tesi può essere liberamente consultata e può essere effettuato il salvataggio e la stampa di una copia per fini strettamente personali di studio, di ricerca e di insegnamento, con espresso divieto di qualunque utilizzo direttamente o indirettamente commerciale. Ogni altro diritto sul materiale è riservato

Download (1MB)


Human society has always been shaped by its technology, so much that even ages and parts of our history are often named after the discoveries of that time. The growth of modern society is largely derived from the introduction of classical computers that brought us innovations like repeated tasks automatization and long-distance communication. However, this explosive technological advancement could be subjected to a heavy stop when computers reach physical limitations and the empirical law known as Moore Law comes to an end. Foreshadowing these limits and hoping for an even more powerful technology, forty years ago the branch of quantum computation was born. Quantum computation uses at its advantage the same quantum effects that could stop the progress of traditional computation and aim to deliver hardware and software capable of even greater computational power. In this context, this thesis presents the implementation of a quantum variational machine learning algorithm called quantum single-layer perceptron. We start by briefly explaining the foundation of quantum computing and machine learning, to later dive into the theoretical approach of the multiple aggregator quantum algorithms, and finally deliver a versatile implementation of the quantum counterparts of a single hidden layer perceptron. To conclude we train the model to perform binary classification using standard benchmark datasets, alongside three baseline quantum machine learning models taken from the literature. We then perform tests on both simulated quantum hardware and real devices to compare the performances of the various models.

Tipologia del documento
Tesi di laurea (Laurea magistrale)
Autore della tesi
Orazi, Filippo
Relatore della tesi
Correlatore della tesi
Corso di studio
Ordinamento Cds
Parole chiave
quantum computing,qiskit,quantum variational algorithm,MAQA,quantum aggregator,artificial intelligence,machine learning,quantum single layer perceprton
Data di discussione della Tesi
4 Febbraio 2022

Altri metadati

Statistica sui download

Gestione del documento: Visualizza il documento