A didactic approach to quantum machine learning with a single qubit
- URL: http://arxiv.org/abs/2211.13191v2
- Date: Sat, 8 Apr 2023 13:52:47 GMT
- Title: A didactic approach to quantum machine learning with a single qubit
- Authors: Elena Pe\~na Tapia, Giannicola Scarpa, Alejandro Pozas-Kerstjens
- Abstract summary: We focus on the case of learning with a single qubit, using data re-uploading techniques.
We implement the different proposed formulations in toy and real-world datasets using the qiskit quantum computing SDK.
- Score: 68.8204255655161
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- 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.
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