Optimizing Feature Selection for Predictive Analytics With Quantum Algorithms

Abstract

Quantum algorithms for predictive analytics is important across various sectors such as finance, healthcare, marketing and supply chain management where accurate predictions can be used to optimise operations and decision-making processes. This chapter addresses Specific quantum machine learning algorithms such as Quantum Support Vector Machines (QSVM), Quantum K-nearest Neighbors (QKNN) and Quantum Neural Networks (QNN). These algorithms enhance classification, regression and complex predictive tasks by accelerating training processes and managing large feature spaces effectively. And, to this, quantum optimization algorithms like Quantum Approximate Optimization Algorithm (QAOA) and Variational Quantum Eigensolver (VQE) are mentioned for their role in optimising model parameters and improving prediction accuracy. In addition, it discusses quantum data encoding methods, such as Quantum Fourier Transform (QFT) and amplitude encoding. Along with this, it acknowledges significant challenges such as hardware limitations, algorithmic complexity and practical integration issues.