Influence of Data Dimensionality Reduction Methods on the Effectiveness of Quantum Machine Learning Models

TL;DR

This paper investigates how data dimensionality reduction impacts the performance evaluation of quantum machine learning models, revealing that it can skew results and affect model assessment accuracy.

Contribution

It provides a comprehensive analysis of the effects of various data reduction methods on quantum machine learning performance metrics.

Findings

Data reduction methods can significantly skew performance metrics.

Accuracy differences ranged from 14% to 48% with and without data reduction.

Certain reduction methods perform better with specific data embedding techniques.

Abstract

Data dimensionality reduction techniques are often utilized in the implementation of Quantum Machine Learning models to address two significant issues: the constraints of NISQ quantum devices, which are characterized by noise and a limited number of qubits, and the challenge of simulating a large number of qubits on classical devices. It also raises concerns over the scalability of these approaches, as dimensionality reduction methods are slow to adapt to large datasets. In this article, we analyze how data reduction methods affect different QML models. We conduct this experiment over several generated datasets, quantum machine algorithms, quantum data encoding methods, and data reduction methods. All these models were evaluated on the performance metrics like accuracy, precision, recall, and F1 score. Our findings have led us to conclude that the usage of data dimensionality reduction methods results in skewed performance metric values, which results in wrongly estimating the actual performance of quantum machine learning models. There are several factors, along with data dimensionality reduction methods, that worsen this problem, such as characteristics of the datasets, classical to quantum information embedding methods, percentage of feature reduction, classical components associated with quantum models, and structure of quantum machine learning models. We consistently observed the difference in the accuracy range of 14% to 48% amongst these models, using data reduction and not using it. Apart from this, our observations have shown that some data reduction methods tend to perform better for some specific data embedding methodologies and ansatz constructions.