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On the Applicability of Quantum Machine Learning

This article shows the applicability of both classical and quantum machine learning algorithms on several data sets. Here, the authors developed one of the data sets based on quantum mechanical symmetry properties. The results show that classical machine learning algorithms still perform best in terms of accuracy and runtime, even for specific quantum data sets.


On the Applicability of Quantum Machine Learning


Sebastian Raubitzek, Kevin Mallinger




In this article, we investigate the applicability of quantum machine learning for classification
tasks using two quantum classifiers from the Qiskit Python environment: the variational quantum
circuit and the quantum kernel estimator (QKE). We provide a first evaluation on the performance of
these classifiers when using a hyperparameter search on six widely known and publicly available
benchmark datasets and analyze how their performance varies with the number of samples on two
artificially generated test classification datasets. As quantum machine learning is based on unitary
transformations, this paper explores data structures and application fields that could be particularly
suitable for quantum advantages. Hereby, this paper introduces a novel dataset based on concepts
from quantum mechanics using the exponential map of a Lie algebra. This dataset will be made
publicly available and contributes a novel contribution to the empirical evaluation of quantum
supremacy. We further compared the performance of VQC and QKE on six widely applicable datasets
to contextualize our results. Our results demonstrate that the VQC and QKE perform better than basic
machine learning algorithms, such as advanced linear regression models (Ridge and Lasso). They do
not match the accuracy and runtime performance of sophisticated modern boosting classifiers such
as XGBoost, LightGBM, or CatBoost. Therefore, we conclude that while quantum machine learning
algorithms have the potential to surpass classical machine learning methods in the future, especially
when physical quantum infrastructure becomes widely available, they currently lag behind classical
approaches. Our investigations also show that classical machine learning approaches have superior
performance classifying datasets based on group structures, compared to quantum approaches
that particularly use unitary processes. Furthermore, our findings highlight the significant impact
of different quantum simulators, feature maps, and quantum circuits on the performance of the
employed quantum estimators. This observation emphasizes the need for researchers to provide
detailed explanations of their hyperparameter choices for quantum machine learning algorithms, as
this aspect is currently overlooked in many studies within the field. To facilitate further research in
this area and ensure the transparency of our study, we have made the complete code available in a
linked GitHub repository.


Entropy | Free Full-Text | On the Applicability of Quantum Machine Learning (mdpi.com)

GitHub – Raubkatz/Quantum_Machine_Learning: This is a small repository that shows some basic applications of the quantum machine learning methods from qiskit-machine-learning