Quantum Feature Engineering for Machine Learning: a Novel Approach

EasyChair Preprint 14866

Abstract

Quantum computing offers a promising frontier for advancing machine learning by leveraging quantum principles to enhance feature engineering, a critical step in the data preprocessing phase. Traditional machine learning models rely on classical data representation, often limiting the complexity of feature extraction and transformation. However, quantum feature engineering introduces a new paradigm by utilizing quantum states and operations to create high-dimensional feature spaces, enabling the encoding of richer data patterns that classical systems struggle to capture.

This abstract explores the concept of quantum feature engineering, where quantum algorithms such as quantum Fourier transforms, amplitude encoding, and variational circuits are employed to enhance feature extraction. These quantum features can potentially improve model accuracy, reduce overfitting, and optimize computational resources. We discuss potential applications across various domains, including natural language processing, image recognition, and financial forecasting, where quantum-enhanced features may provide a competitive edge. While the field is still in its infancy, the integration of quantum computing in feature engineering promises significant advancements in the scalability and performance of machine learning models.

Further research is needed to address challenges related to quantum noise, hardware limitations, and the development of hybrid quantum-classical algorithms that can be efficiently implemented on near-term quantum devices.

Keyphrases: Novel Approach, QUANTUM FEATURE ENGINEERING, machine learning

@booklet{EasyChair:14866,
  author    = {Favour Olaoye and Kaledio Potter},
  title     = {Quantum Feature Engineering for Machine Learning: a Novel Approach},
  howpublished = {EasyChair Preprint 14866},
  year      = {EasyChair, 2024}}