Abstract
Machine learning (ML) techniques have seen increasing use in recent years in complementing traditional HPC solutions to physical systems problems. While the scientific community has been rapidly adopting such techniques, it is still unclear how different ML techniques compare in terms of accuracy. In this paper we address this question by designing and training a neural network and comparing its performance to traditional classification models using as a case study a non-interacting quantum system on a graph structure. We build a classifier with the ability to distinguish extended from localized quantum states based on their different structure and compare it with other commonly used ML classifiers. Our results show high accuracy for certain ML models in most cases, whereas others are less effective.
Supported by the Greek Research Technology Development and Innovation Action “RESEARCH - CREATE - INNOVATE”, Operational Programme on Competitiveness, Entrepreneurship and Innovation 2014–2020, Grant T1E\(\Delta \)K-04819.
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Stamatiou, G.T., Magoutis, K. (2021). Investigating the Use of Machine Learning Techniques in a Random Physical System. In: Barzen, J. (eds) Service-Oriented Computing. SummerSOC 2021. Communications in Computer and Information Science, vol 1429. Springer, Cham. https://doi.org/10.1007/978-3-030-87568-8_7
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