TITLE: Binary classifiers for noisy datasets: a comparative study of existing quantum machine learning frameworks and some new approaches 

ABSTRACT: One of the most promising areas of research to obtain practical advantage is
Quantum Machine Learning which was born as a result of cross-fertilisation of
ideas between Quantum Computing and Classical Machine Learning. In this paper,
we apply Quantum Machine Learning (QML) frameworks to improve binary
classification models for noisy datasets which are prevalent in financial
datasets. The metric we use for assessing the performance of our quantum
classifiers is the area under the receiver operating characteristic curve
(ROC/AUC). By combining such approaches as hybrid-neural networks, parametric
circuits, and data re-uploading we create QML inspired architectures and
utilise them for the classification of non-convex 2 and 3-dimensional figures.
An extensive benchmarking of our new FULL HYBRID classifiers against existing
quantum and classical classifier models, reveals that our novel models exhibit
better learning characteristics to asymmetrical Gaussian noise in the dataset
compared to known quantum classifiers and performs equally well for existing
classical classifiers, with a slight improvement over classical results in the
region of the high noise.
One of the most promising areas of research to obtain practical advantage is
Quantum Machine Learning which was born as a result of cross-fertilisation of
ideas between Quantum Computing and Classical Machine Learning. In this paper,
we apply Quantum Machine Learning (QML) frameworks to improve binary
classification models for noisy datasets which are prevalent in financial
datasets. The metric we use for assessing the performance of our quantum
classifiers is the area under the receiver operating characteristic curve
(ROC/AUC). By combining such approaches as hybrid-neural networks, parametric
circuits, and data re-uploading we create QML inspired architectures and
utilise them for the classification of non-convex 2 and 3-dimensional figures.
An extensive benchmarking of our new FULL HYBRID classifiers against existing
quantum and classical classifier models, reveals that our novel models exhibit
better learning characteristics to asymmetrical Gaussian noise in the dataset
compared to known quantum classifiers and performs equally well for existing
classical classifiers, with a slight improvement over classical results in the
region of the high noise.
Binary classifiers for noisy datasets: a comparative study of existing quantum machine learning frameworks and some new approaches
http://arxiv.org/abs/2111.03372v1