Identifying clusters in data is an important task in many fields. In this paper, we consider situations in which data live in a physical world, so we first have to collect the images using sensors before clustering them. Using sensors enhanced by quantum entanglement, we can image surfaces more accurately than using purely classical strategies. However, it is not immediately obvious whether the advantage we gain is robust enough to survive data-processing steps such as clustering. It has previously been found that using quantum-enhanced sensors for imaging and pattern recognition can give an advantage for supervised learning tasks, and here we demonstrate that this advantage also holds for an unsupervised learning task, namely clustering.

Quantum-Enhanced Cluster Detection in Physical Images / Pereira, JL; Banchi, L; Pirandola, S. - In: PHYSICAL REVIEW APPLIED. - ISSN 2331-7019. - ELETTRONICO. - 19:(2023), pp. 054031-054045. [10.1103/PhysRevApplied.19.054031]

Quantum-Enhanced Cluster Detection in Physical Images

Banchi, L;
2023

Abstract

Identifying clusters in data is an important task in many fields. In this paper, we consider situations in which data live in a physical world, so we first have to collect the images using sensors before clustering them. Using sensors enhanced by quantum entanglement, we can image surfaces more accurately than using purely classical strategies. However, it is not immediately obvious whether the advantage we gain is robust enough to survive data-processing steps such as clustering. It has previously been found that using quantum-enhanced sensors for imaging and pattern recognition can give an advantage for supervised learning tasks, and here we demonstrate that this advantage also holds for an unsupervised learning task, namely clustering.
2023
19
054031
054045
Pereira, JL; Banchi, L; Pirandola, S
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Utilizza questo identificatore per citare o creare un link a questa risorsa: https://hdl.handle.net/2158/1330034
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