Q-LIME $\pi$: A Quantum-Inspired Extension to LIME

TL;DR

Q-LIME $$ is a quantum-inspired extension of LIME that encodes feature vectors in quantum states, enabling more efficient local explanations with comparable accuracy and reduced runtime in certain datasets.

Contribution

It introduces a quantum-inspired encoding for LIME, leveraging superposition and interference to improve explanation efficiency in text-based models.

Findings

Achieves near-identical feature rankings to classical LIME.

Demonstrates lower runtime in small- to moderate-dimensional spaces.

Provides a pathway for quantum-enhanced interpretability methods.

Abstract

Machine learning models offer powerful predictive capabilities but often lack transparency. Local Interpretable Model-agnostic Explanations (LIME) addresses this by perturbing features and measuring their impact on a model's output. In text-based tasks, LIME typically removes present words (bits set to 1) to identify high-impact tokens. We propose \textbf{Q-LIME $\pi$} (Quantum LIME $\pi$), a quantum-inspired extension of LIME that encodes a binary feature vector in a quantum state, leveraging superposition and interference to explore local neighborhoods more efficiently. Our method focuses on flipping bits from $1 \rightarrow 0$ to emulate LIME's ``removal'' strategy, and can be extended to $0 \rightarrow 1$ where adding features is relevant. Experiments on subsets of the IMDb dataset demonstrate that Q-LIME $\pi$ often achieves near-identical top-feature rankings compared to classical LIME while exhibiting lower runtime in small- to moderate-dimensional feature spaces. This quantum-classical hybrid approach thus provides a new pathway for interpretable AI, suggesting that, with further improvements in quantum hardware and methods, quantum parallelism may facilitate more efficient local explanations for high-dimensional data.