Loading...
Projects / Programmes source: ARIS

From classical to quantum machine learning through tensor networks

Research activity

Code Science Field Subfield
1.07.00  Natural sciences and mathematics  Computer intensive methods and applications   

Code Science Field
1.01  Natural Sciences  Mathematics 
Keywords
machine learning, quantum devices, quantum computing, tensor networks, many-body quantum systems
Evaluation (rules)
source: COBISS
Points
3,940.11
A''
1,058.25
A'
2,075.56
A1/2
2,677.65
CI10
9,333
CImax
1,942
h10
46
A1
14.39
A3
2.61
Data for the last 5 years (citations for the last 10 years) on February 25, 2024; A3 for period 2018-2022
Data for ARIS tenders ( 04.04.2019 – Programme tender, archive )
Database Linked records Citations Pure citations Average pure citations
WoS  200  10,425  9,529  47.65 
Scopus  218  11,764  10,804  49.56 
Researchers (5)
no. Code Name and surname Research area Role Period No. of publicationsNo. of publications
1.  33106  PhD Enej Ilievski  Physics  Researcher  2020 - 2024  35 
2.  15295  PhD Marko Robnik Šikonja  Computer science and informatics  Researcher  2020 - 2024  417 
3.  56518  PhD Antonio Federico Zegarra Borrero  Physics  Researcher  2021 - 2024 
4.  21369  PhD Marko Žnidarič  Physics  Researcher  2020 - 2024  142 
5.  30657  PhD Bojan Žunkovič  Physics  Head  2020 - 2024  33 
Organisations (2)
no. Code Research organisation City Registration number No. of publicationsNo. of publications
1.  1539  University of Ljubljana, Faculty of Computer and Information Science  Ljubljana  1627023  16,002 
2.  1554  University of Ljubljana, Faculty of Mathematics and Physics  Ljubljana  1627007  33,797 
Abstract
Machine learning is a data-driven field, which needs massive computing resources. Quantum computation, on the other hand, can provide exponential speedups for some classical algorithms. Therefore, it is natural to combine the strengths of both fields to solve outstanding problems in industry and research. The project has three goals. The first goal is to use machine learning methods for the description of many-body quantum systems. In this part of the project, we will tackle some of the notable problems of many-body quantum mechanics with new tools that are emerging by adopting neural networks to quantum mechanical problems. The second goal is to use methods from many-body quantum mechanics to describe machine learning problems. We will address the problems of adversarial examples, uncertainty, and generalization from a new perspective, which is motivated by the success of tensor networks for a description of many-body quantum systems. The third and most ambitious goal is to combine the knowledge from quantum mechanics and machine learning to find novel applications of noisy intermediate-scale quantum devices with significant speedups for known classical algorithms. We will apply a combination of successful quantum-mechanical tools and advanced machine learning tools to find useful quantum algorithms that could demonstrate applied quantum advantage.
Views history
Favourite