Quantum-Inspired Machine Learning: A New Paradigm for Complex Data Processing

Abstract

This study aims to evaluate the effectiveness of quantum-inspired machine learning (QIML) techniques when applied to high-dimensional data processing problems. Instead of relying on quantum hardware, QIML uses concepts such as amplitude encoding and tensor networks to improve the efficiency of traditional models. We compared the performance of QIML models with traditional machine learning models on three real-world datasets in the fields of finance, medical diagnosis, and natural language processing. The study used metrics of accuracy, training speed, and generalization properties for comparison. Our results show that hybrid QIML models achieve up to a 10% improvement in accuracy and a 40% improvement in training speed compared to traditional models. We also discuss current limitations in scalability and computational cost and suggest future research directions for developing QIML as an effective tool for processing complex data.