[2604.09430] On the Representational Limits of Quantum-Inspired 1024-D Document Embeddings: An Experimental Evaluation Framework

Computer Science > Information Retrieval arXiv:2604.09430 (cs) [Submitted on 10 Apr 2026] Title:On the Representational Limits of Quantum-Inspired 1024-D Document Embeddings: An Experimental Evaluation Framework Authors:Dario Maio View a PDF of the paper titled On the Representational Limits of Quantum-Inspired 1024-D Document Embeddings: An Experimental Evaluation Framework, by Dario Maio View PDF HTML (experimental) Abstract:Text embeddings are central to modern information retrieval and Retrieval-Augmented Generation (RAG). While dense models derived from Large Language Models (LLMs) dominate current practice, recent work has explored quantum-inspired alternatives motivated by the geometric properties of Hilbert-like spaces and their potential to encode richer semantic structure. This paper presents an experimental framework for constructing quantum-inspired 1024-dimensional document embeddings based on overlapping windows and multi-scale aggregation. The pipeline combines semantic projections (e.g., EigAngle), circuit-inspired feature mappings, and optional teacher-student distillation, together with a fingerprinting mechanism for reproducibility and controlled evaluation. We introduce a set of diagnostic tools for hybrid retrieval, including static and dynamic interpolation between BM25 and embedding-based scores, candidate union strategies, and a conceptual alpha-oracle that provides an upper bound for score-level fusion. Experiments on controlled corpora of Italian ...