2026-06-27T11:04:07Zhttps://docta.ucm.es/rest/oai/request

oai:docta.ucm.es:20.500.14352/727642023-08-27T11:13:24Zcom_20.500.14352_14col_20.500.14352_15

Peña Tapia, Elena Scarpa, Giannicola Pozas Kerstjens, Alejandro 2023-06-22T12:31:47Z 2023-06-22T12:31:47Z 2022 https://hdl.handle.net/20.500.14352/72764 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. eng open access Fraud detection with a single-qubit quantum neural network journal article