On the Representational Limits of Quantum-Inspired 1024-D Document Embeddings: An Experimental Evaluation Framework
Summary: arXiv:2604.09430v1 Announce Type: cross
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.
Key Contributions
- Experimental Framework: The paper outlines a systematic approach to creating and evaluating quantum-inspired embeddings, focusing on their dimensionality and structural properties.
- Diagnostic Tools: A set of tools for hybrid retrieval is introduced, including methods for combining BM25 and embedding-based scores. These tools aim to enhance the efficacy of retrieval systems.
- Evaluation Across Domains: Experiments are conducted on controlled corpora of Italian and English documents, spanning technical, narrative, and legal domains, providing a comprehensive evaluation of the embeddings.
Experimental Findings
The experiments reveal several critical insights:
- BM25 as a Baseline: The traditional BM25 model remains a strong baseline, outperforming many quantum-inspired approaches in stability and effectiveness.
- Teacher Embeddings: Teacher embeddings contribute to a stable semantic structure, yet their impact on overall retrieval performance varies.
- Quantum-Inspired Embeddings: Standalone quantum-inspired embeddings exhibit weak and unstable ranking signals, indicating potential limitations in their design.
- Distillation Effects: The process of distillation can yield mixed results; while it enhances alignment in some scenarios, it does not consistently improve retrieval performance.
- Hybrid Retrieval Success: By combining lexical and embedding-based signals, hybrid retrieval strategies can achieve competitive results, suggesting that quantum-inspired embeddings may serve better as auxiliary components.
Conclusion
The findings underscore significant structural limitations in the geometry of quantum-inspired embeddings, specifically distance compression and ranking instability. These challenges clarify their role within retrieval systems, positioning them more as supplementary tools rather than standalone solutions.
This research contributes to the ongoing discourse on the viability of quantum-inspired approaches in information retrieval, providing a framework for future investigations and improvements in embedding methodologies.