Dottorato Interateneo in Matematica

Quantum Computing

Settembre 2017


Alessandra Di Pierro

Cicli in cui è offerta

32° ciclo
32° Ciclo


In this course we provide an introduction to the interdisciplinary field of Quantum Computing.
Besides the standard theory based on quantum circuits, a new theory of quantum computation is also presented, which is called Topological Quantum Computation. This is based on the important discovery of some topological properties of matter that just recently have been brought to the attention of the public by the 2016 Nobel prize for physics awarding to Thouless, Haldane and Kosterlitz.



Seminario collegato al dottorato

Supervised Discriminative Classification in Quantum Machine Learning
Quantum Machine Learning is a recent area of research initiated by the demonstrations of quantised variants of standard machine learning algorithms such as the Quantum Support Vector Machine (SVM) by Rebentrost, Mohseni & Lloyd and the Quantum K-Means algorithm of Aïmeur, Brassard and Gambs. The development of the quantum SVM can be regarded as particularly significant in that the classical SVM constitutes the exemplar instance of a  supervised binary classifier, i.e. an entity capable of learning an optimal discriminative decision hyperplane from labeled vectors. We explore this classifier in detail along with its Kernelised variants, as well as investigating an ensemble-based enhancement to enable variance-resilient quantum machine learning.
David Windridge - Middlesex University London

Convegno collegato al dottorato

Workshop on Quantum Techniques in Machine Learning - QTML 2017

Quantum Computing (QC) has been for a long time known only for a restricted set of applications where it allows for the achievement of an exponential speed up over the classical computer (e.g. the simulation of quantum physics and chemistry, and the factorisation of large numbers). Recently, however, new developments have opened up opportunities for the application of quantum algorithms to the field of Machine Learning (ML) that may solve problems such as clustering, classification, and pattern matching faster than their classical counterparts. This includes new algorithmic techniques based on Topological Quantum Computation which seem to be especially suitable for kernel-based pattern recognition. The prospects that near term quantum devices could be able to solve computationally hard problems in ML has given rise to Quantum Machine Learning (QML) as a research field in its own right at the intersection between QC and ML. It includes quantum optimisation where theoretical and empirical analysis of quantum annealing approaches are currently subject of intense study.

The QTML 2017 workshop aims to set up a common ground where students and leading researchers in both Quantum Computing and Machine Learning can meet and exchange ideas on the topics of the workshop and discuss how the results from one field can help solving the problems in the other field and viceversa.

Di Pierro Alessandra