Quantum Inspired Encoding Strategies for Machine Learning Models: Proposing and Evaluating Instance Level, Global Discrete, and Class Conditional Representations

TL;DR

This paper introduces and compares three quantum-inspired data encoding strategies for classical machine learning, focusing on reducing encoding time while maintaining accuracy and efficiency.

Contribution

It proposes three novel quantum-inspired encoding strategies and evaluates their effectiveness and trade-offs in classical machine learning classification tasks.

Findings

ILS reduces encoding time but may affect accuracy

GDS offers uniform encoding across dataset features

CCVS preserves class-specific information for improved classification

Abstract

In this study, we propose, evaluate and compare three quantum inspired data encoding strategies, Instance Level Strategy (ILS), Global Discrete Strategy (GDS) and Class Conditional Value Strategy (CCVS), for transforming classical data into quantum data for use in pure classical machine learning models. The primary objective is to reduce high encoding time while ensuring correct encoding values and analyzing their impact on classification performance. The Instance Level Strategy treats each row of dataset independently; mimics local quantum states. Global Discrete Value Based encoding strategy maps all unique feature values across the full dataset to quantum states uniformly. In contrast, the Class conditional Value based encoding strategy encodes unique values separately for each class, preserving class dependent information. We apply these encoding strategies to a classification task and assess their impact on en-coding efficiency, correctness, model accuracy, and computational cost. By analyzing the trade offs between encoding time, precision, and predictive performance, this study provides insights into optimizing quantum inspired data transformations for classical machine learning workflows.