Tommaso Faorlin | University of Innsbruck (original) (raw)

Drafts by Tommaso Faorlin

Nowadays, both data visualisation and clustering techniques are essential and widely used in the ... more Nowadays, both data visualisation and clustering techniques are essential and widely used in the unsupervised machine learning framework. They provide information on how data distribute in space and how they can be clustered efficiently to group together alike behaviours. To accomplish this task, several algorithms have been designed in the past years and are continually being improved and rethought today. Within this work, we implement the t-distributed stochastic neighbour embedding (t-SNE) for data visualisation and the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) to identify clumps of similar data. Consequently, we provide an analysis of their performances on different high-dimensional datasets and explain how they entangle together to carry out unsupervised machine learning tasks.

The generation of good random numbers impacts basic research and applications beyond pure academi... more The generation of good random numbers impacts basic research and applications beyond pure academic interests: random numbers are required for countless applications, such as cryptography and simulations. For most applications, it is of outmost importance to know if a set of numbers is truly random, pseudo-random or contains some residual correlations. Tensor networks are powerful data structures that spring from quantum many-body physics and are now increasingly applied to machine learning applications. This thesis plans to explore the intersection between these two fields, applying quantum-inspired machine learning to random number generation. We aim to perform and characterise novel statistical checks comparing and characterising the statistical quality of different number sets: correlated, pseudo-random, and quantum-random.

A brief introduction on quantum Fourier transform (QFT)

Books by Tommaso Faorlin

We are six students enrolled in the Physics of Data master degree at the University of Padova, It... more We are six students enrolled in the Physics of Data master degree at the University of Padova, Italy. From this year on, our master provides lectures on Information Theory and Inference, held by two international expert scientists that have been working on these topics for several years. Since the arguments tackled seemed very interesting, we decided to put the efforts together and work on some high quality notes for the whole course, to allow the other students to study and have a different point of view on the various arguments treated. We hope you will enjoy our vegetable soup with melt cheese.

Conference Presentations by Tommaso Faorlin

Weekly assignments on quantum simulations, written in Fortran, to tackle different quantum systems.

Talks by Tommaso Faorlin

Me and my colleague studied in depth two papers from Attanasi et al. about the 'Information trans... more Me and my colleague studied in depth two papers from Attanasi et al. about the 'Information transfer and behavioural inertia in starling flocks', as a final project for the course in Statistical Mechanics of Complex Systems (prof. Amos Maritan, University of Padua). These are the slide used in a final presentation.

Nowadays, both data visualisation and clustering techniques are essential and widely used in the ... more Nowadays, both data visualisation and clustering techniques are essential and widely used in the unsupervised machine learning framework. They provide information on how data distribute in space and how they can be clustered efficiently to group together alike behaviours. To accomplish this task, several algorithms have been designed in the past years and are continually being improved and rethought today. Within this work, we implement the t-distributed stochastic neighbour embedding (t-SNE) for data visualisation and the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) to identify clumps of similar data. Consequently, we provide an analysis of their performances on different high-dimensional datasets and explain how they entangle together to carry out unsupervised machine learning tasks.

The generation of good random numbers impacts basic research and applications beyond pure academi... more The generation of good random numbers impacts basic research and applications beyond pure academic interests: random numbers are required for countless applications, such as cryptography and simulations. For most applications, it is of outmost importance to know if a set of numbers is truly random, pseudo-random or contains some residual correlations. Tensor networks are powerful data structures that spring from quantum many-body physics and are now increasingly applied to machine learning applications. This thesis plans to explore the intersection between these two fields, applying quantum-inspired machine learning to random number generation. We aim to perform and characterise novel statistical checks comparing and characterising the statistical quality of different number sets: correlated, pseudo-random, and quantum-random.

A brief introduction on quantum Fourier transform (QFT)

We are six students enrolled in the Physics of Data master degree at the University of Padova, It... more We are six students enrolled in the Physics of Data master degree at the University of Padova, Italy. From this year on, our master provides lectures on Information Theory and Inference, held by two international expert scientists that have been working on these topics for several years. Since the arguments tackled seemed very interesting, we decided to put the efforts together and work on some high quality notes for the whole course, to allow the other students to study and have a different point of view on the various arguments treated. We hope you will enjoy our vegetable soup with melt cheese.

Weekly assignments on quantum simulations, written in Fortran, to tackle different quantum systems.

Me and my colleague studied in depth two papers from Attanasi et al. about the 'Information trans... more Me and my colleague studied in depth two papers from Attanasi et al. about the 'Information transfer and behavioural inertia in starling flocks', as a final project for the course in Statistical Mechanics of Complex Systems (prof. Amos Maritan, University of Padua). These are the slide used in a final presentation.