Analysis Of Continuous‐Variable Quantum‐Inspired Feature Encoding And Their Interaction With Classical Learning Models

Continuous‐variable quantum‐inspired feature encodings have attracted increasing attention as structured transformations for hybrid and classical machine learning. Despite growing interest, their practical behavior within classical learning pipelines, particularly their interaction with different model architectures, remains insufficiently understood. In this work, we analyze three continuous‐variable quantum‐inspired feature encoding mechanisms, namely displacement, squeezing, and Instantaneous Quantum Polynomial style embeddings, when used as feature mappings for classical learning models. Using a real‐world telecommunications customer churn dataset, we examine how these encodings reshape feature geometry and interact with linear, distance‐based, and ensemble classifiers under varying levels of dimensionality reduction. The results indicate that smooth Gaussian encodings, such as displacement and squeezing, exhibit stable interactions with neighborhood‐based and ensemble models, while providing limited benefit for strictly linear classifiers. In contrast, Instantaneous Quantum Polynomial style embeddings show comparatively unstable behavior across several classical learning models, suggesting higher sensitivity to discretization and classifier inductive bias. Classical feature representations remain competitive in absolute predictive performance, indicating that the principal value of continuous‐variable quantum‐inspired encodings lies in their representational and feature transformation capabilities rather than in universal accuracy gains. Overall, the study highlights that encoding effectiveness depends strongly on compatibility between the transformed feature space and the downstream learning architecture, offering useful guidance for simulator‐based and hybrid machine learning settings.