Q-LIME $π$: A Quantum-Inspired Extension to LIME
This work addresses the need for more efficient interpretability methods in AI, particularly for text-based tasks, but it is incremental as it builds directly on LIME with quantum-inspired techniques.
The paper tackles the problem of improving the efficiency of local interpretable model-agnostic explanations (LIME) for machine learning models by proposing Q-LIME π, a quantum-inspired extension that encodes binary features in quantum states to explore neighborhoods more efficiently. Experiments on IMDb subsets show it achieves near-identical top-feature rankings to classical LIME with lower runtime in small- to moderate-dimensional spaces.
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 $π$} (Quantum LIME $π$), 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 $π$ 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.