Year: 2023
Type: article
Abstract: This paper presents, via an explicit example with a real-world dataset, a hands-on introduction to the field of quantum machine learning (QML). 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 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. We find that, as in the case of classical neural networks, the number of layers is a determining factor in the final accuracy of the models. Moreover, and 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.
Source: Physica Scripta
Authors: Elena Peña Tapia, Giannicola Scarpa, Alejandro Pozas-Kerstjens
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
Estimated APC paid: $2,220
Awards: G1175115578, G121809054, G129255482, G1484669290, G1622782223, G1701110065, G1799927510, G1855990336, G2262748287, G2309174360, G2660406951, G279363373, G2967504892, G300979063, G318412399, G34346518, G3480869486, G3518538256, G391183672, G410571670, G4233400527, G425668148, G430777504, G451917667, G480488906, G4886082077, G488842982, G4993905653, G5080475149, G5421974903, G5539308778, G5805502524, G5845768719, G5967599077, G6520718613, G6689034887, G7084143925, G712370837, G7521977119, G8484821915, G897484643, G929308097, G99296377
OpenAlex ID: W4327915825