RT Journal Article
T1 Fraud detection with a single-qubit quantum neural network
A1 Peña Tapia, Elena
A1 Scarpa, Giannicola
A1 Pozas Kerstjens, Alejandro
AB This paper presents, via an explicit real-world example, a hands-on introduction to the field of quantum machine learning. We focus on the case of learning with a single qubit, using data re-uploading techniques. After a discussion of the relevant background in quantum computing and machine learning, and an overview of state of the art methods in QML, we provide a thorough explanation of the data re-uploading models that we consider, and implement the different proposed formulations in toy and real-world datasets using the qiskit quantum computing SDK. Interestingly, the results show that single-qubit classifiers can achieve a performance that is on-par with classical counterparts under the same set of training conditions. While this cannot be understood as a proof of the advantage of quantum machine learning, it points to a promising research direction, and raises a series of questions that we outline.
YR 2022
FD 2022
LK https://hdl.handle.net/20.500.14352/72764
UL https://hdl.handle.net/20.500.14352/72764
LA eng
NO Ministerio de Ciencia e Innovación (MICINN)
NO Comunidad de Madrid
DS Docta Complutense
RD 7 abr 2025