Introduction to Quantum Machine Learning (2020/2021)

Course code
cod wi: DT000081
Name of lecturer
Alessandra Di Pierro
Alessandra Di Pierro
Number of ECTS credits allocated
Academic sector
Language of instruction
A.A. 20/21 dottorato dal Oct 1, 2020 al Sep 30, 2021.

Lesson timetable

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Learning outcomes

The goal of this course is to show what benefits current and future quantum technologies can provide to machine learning, focusing on algorithms that are challenging with classical computers.
In particular, the students will be given the adequate notions and knowledge to be able to distinguish between quantum computing paradigms relevant for machine learning; identify problems in machine learning that would benefit from using quantum resources; implement learning algorithms on quantum computers using the available public platforms.


1 Introduction & Motivation
2 Quantum Computing
3 Machine Learning (ML)
4 Quantum Machine Learning: Main Approaches
5 ML with a Quantum Annealer
6 Classical-Data-Quantum-Processing Approaches
7 Quantum Neural Network
8 Quantum Programming Languages and Quantum Software Toolchain

Reference books
Author Title Publisher Year ISBN Note
Phillip Kaye, Raymond Laflamme, Michele Mosca An Introduction to Quantum Computing (Edizione 1) Oxford University Press 2006
Eric R. Johnston Programming quantum computers : essential algorithms and code samples (Edizione 1) O'Reilly 2019 1-4920-3967-5
Michael A. Nielsen, Isaac L. Chuang Quantum Computation and Quantum Information (Edizione 2) Cambridge University Press 2010 978-1-107-00217-3
Jack D. Hidary Quantum Computing: An Applied Approach (Edizione 1) Springer 2019 978-3-030-23922-0
Peter Wittek Quantum Machine Learning: What Quantum Computing Means to Data Mining (Edizione 1) Academic Press 2014 9780128009536
Maria Schuld, Francesco Petruccione Supervised Learning with Quantum Computers (Edizione 1) Springer, Cham 2018 978-3-319-96423-2

Assessment methods and criteria

Seminar on a topic in quantum machine learning