Quantum Data Encoding: A Comparative Analysis of Classical-to-Quantum
Mapping Techniques and Their Impact on Machine Learning Accuracy
- URL: http://arxiv.org/abs/2311.10375v1
- Date: Fri, 17 Nov 2023 08:00:08 GMT
- Title: Quantum Data Encoding: A Comparative Analysis of Classical-to-Quantum
Mapping Techniques and Their Impact on Machine Learning Accuracy
- Authors: Minati Rath, Hema Date
- Abstract summary: This research explores the integration of quantum data embedding techniques into classical machine learning (ML) algorithms.
Our findings reveal that quantum data embedding contributes to improved classification accuracy and F1 scores.
- Score: 0.0
- License: http://creativecommons.org/licenses/by-nc-sa/4.0/
- 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.
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