From hyperplanes to hyperellipsoids: characterizing the inherent interpretability of linear and single-qubit mixed-state binary classification models

Short summary

This paper compares the inherent interpretability of standard linear binary classification with a single-qubit mixed-state model, showing the quantum approach is essentially an 'ellipsoid version' of linear classification. The authors discuss how each model carries different geometric inductive biases and feature importance assumptions. The work is positioned as an accessible pedagogical bridge for introducing quantum ML to students with no quantum background.

• •Single-qubit mixed-state binary classification is the ellipsoid analog of linear hyperplane classification
• •Each model carries distinct geometric and feature-importance inductive biases
• •Designed as an accessible quantum ML introduction for undergraduate ML courses

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