Team GPLSI at QClef 2025: Quantum-Inspired Instance Selection and Clustering

This paper presents the participation of the GPLSI team in the Quantum CLEF (QClef) Lab at CLEF 2025, focusing on Task 2 - Instance Selection and Task 3 - Clustering. The QClef Lab explores the applicability of quantum and quantum-inspired techniques to core AI tasks, emphasizing optimization efficiency and data reduction. In Task 2, we propose three multi-paradigm approaches for selecting representative training instances for sentiment classification, leveraging sentiment-aware pairing, local set-based criteria, and classical heuristics. In Task 3, we introduce a single quantum-inspired clustering framework that integrates four distinct pivot selection strategies for document grouping in embedding space. Our methods achieved competitive performance across both tasks. In particular, our LocalSets method achieved the highest effectiveness in Task 2 while substantially reducing the training set, and our FPS-Medoids approach delivered the best results for Task 3 in terms of nDCG@10. Overall, our findings support the potential of annealing-based techniques to deliver effective tradeoffs between performance and computational efficiency in realistic machine learning pipelines.