Quantum Data Encoding - A Comparative Analysis of Classical-to-Quantum Mapping Techniques and Their Impact on Machine Learning Accuracy

Aug 5, 2026

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This research integrates quantum data embedding techniques into classical machine learning algorithms to evaluate performance improvements and computational implications across a range of models. Classical-to-quantum mapping methods are examined using basis encoding, angle encoding, and amplitude encoding for encoding classical data. Experiments cover Logistic Regression, K-Nearest Neighbors, Support Vector Machines, and ensemble models including Random Forest, LightGBM, AdaBoost, and CatBoost. Results show improved classification accuracy and F1 scores, with ensemble methods balancing gains and overhead, while runtime effects vary by model complexity and feature-representation suitability.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages18

Reading Time45 min

AccessFree to download

Introduction

Research motivation and objectives

Dataset consistency approach

Literature Review

Quantum data encoding and ML foundations

Comparative Methods and Experimental Setup

arXiv :2311 . 10375v1 [ quant-ph] 17 Nov 2023 Quantum Data Encoding: A Comparative Analysis of Classical-to-Quantum Mapping Techniques and Their Impact on Machine Learning Accuracy Minati Rath*1 and Hema Date2 [minati.rath.2019@iimmumbai.ac.in](minati.rath.2019@iimmumbai.ac.in), [hemadate@iimmumbai.ac.in](hemadate@iimmumbai.ac.in) 1 Department of Decision Science, IIM Mumbai, India 2 Department of Decision Science, IIM Mumbai, India Abstract This research explores the integration of quantum data embedding techniques into classical machine learning (ML) algorithms, aiming to assess the performance enhancements and computational implications across a spectrum of models. We explore various classical-to-quantum mapping methods, ranging from basis encoding, angle encoding to amplitude encoding for encoding classical data, we conducted an extensive empirical study encompassing popular ML algorithms, including Logistic Regression, K-Nearest Neighbors, Support Vector Machines, and ensemble methods like Random Forest, LightGBM, AdaBoost, and CatBoost. Our findings reveal that quantum data embedding contributes to improved classification accuracy and F1 scores, particularly notable in models that inherently benefit from enhanced feature representation. We observed nuanced effects on running time, with low-complexity models exhibiting moderate increases and more computationally intensive models experiencing discernible changes. Notably, ensemble methods demonstrated a favorable balance between performance gains and computational overhead. This study underscores the potential of quantum data embedding in enhancing classical ML models and emphasizes the importance of weighing performance improvements against computational costs. Future research directions may involve refining quantum encoding processes to optimize computational efficiency and exploring scalability for real-world applications. Our work contributes to the growing body of knowledge at the intersection of quantum computing and classical machine learning, offering insights for researchers and practitioners seeking to harness the advantages of quantum-inspired techniques in practical scenarios. Machine Learning, Quantum Computing, Quantum Data Encoding, Classification, Prediction 1 Introduction The realm of computing stands at the brink of a significant transformation with the emergence of quantum technologies[1] . Quantum computing, characterized by the exploitation of quantum mechanical phenomena, such as superposition and entanglement, offers the promise of fundamentally altering the way we process and analyze information [2][3] . In this context, the translation of classical data into quantum representations has emerged as a novel and captivating avenue of exploration [4] . Machine learning, which has already revolutionized fields ranging from image recognition to healthcare diagnostics [5][6][7], is expected to reap substantial benefits from the integration of classical and quantum computing. The potential to enhance machine learning models through the utilization of quantum data representations raises a host of intriguing questions and possibilities. How can classical data, which has long served as the backbone of data-driven decision-making, be seamlessly and effectively translated into the quantum realm. What quantum data encoding techniques yield the most promising results for classical machine learning models. [8] This research embarks on an empirical journey to address these questions. The primary objective is to investigate the efficacy of classical data translation into quantum states using a variety of encoding techniques. These techniques encompass a spectrum of classical-to-quantum data mapping methods, including basis encoding, angle encoding and amplitude encoding. The unique impact of each technique on classical machine learning performance when applied to both classical and quantum datasets are rigorously examined. Of particular

FAQs

What is the main goal of the study on quantum data encoding?

The study aims to assess how classical data mapped into quantum representations via different encoding techniques affects classical machine learning performance and computational cost.

Which classical-to-quantum encoding techniques are compared?

The research compares basis encoding, angle encoding, and amplitude encoding for mapping classical data into quantum states.

How does the study evaluate models and what models are included?

It performs extensive empirical testing using popular classical ML algorithms, including Logistic Regression, K-Nearest Neighbors, Support Vector Machines, and ensemble methods such as Random Forest, LightGBM, AdaBoost, and CatBoost.

Aug 5, 2026

Upload by Aurora