A didactic approach to quantum machine learning with a single qubit

TL;DR

This paper provides a hands-on, didactic introduction to quantum machine learning using a single qubit and data re-uploading, demonstrating comparable performance to classical models on real datasets.

Contribution

It introduces a practical approach to QML with a single qubit, including implementation details and analysis of data re-uploading models with real-world data.

Findings

Single-qubit classifiers can match classical performance under similar training conditions.

Number of layers significantly impacts model accuracy.

Quantum models show promise but do not yet prove quantum advantage.

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.