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Life sciences · Preprint
arXiv · September 9, 2026
Early or partial results. Treat as a signal, not a conclusion.
This is an unreviewed experimental study comparing dimensionality reduction techniques in a hybrid quantum-classical NLP pipeline using the TREC question-classification dataset. The work demonstrates that supervised reduction methods (LDA, NCA) preserve task-relevant information more efficiently than unsupervised PCA, achieving near-baseline accuracy (83.1–85.3%) with only 5 dimensions versus 384–768 in classical approaches. The findings are preliminary and confined to a single benchmark; generalizability to other domains and validity of the quantum processing component remain unvalidated.
Experimental comparison of dimensionality reduction methods; single-dataset proof-of-concept. TREC question-classification dataset; no clinical or patient population. Intervention: Hybrid quantum-classical NLP pipeline with three dimensionality reduction approaches (PCA, NCA, LDA) applied to 768-dimensional sentence embeddings. Compared with: Full 384-dimensional classical baseline; unsupervised (PCA) versus supervised (LDA, NCA) dimensionality reduction.
PCA variance retention at extreme compression: 8.2% (3D), 10.2% (4D), 11.9% (5D), 16.4% (8D) with accuracies 50.3%, 51.2%, 57.9%, 63.4% respectively LDA achieved 85.3% accuracy using only 5 dimensions under leakage-free cross-validation NCA achieved 83.1% accuracy using only 5 dimensions
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Early-stage experimental work on a hybrid quantum-classical system using a single dataset and no peer review; demonstrates feasibility but requires validation and does not yet support clinical or practice recommendations.
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Large language and sentence-embedding models provide rich semantic representations, but their high dimensionality poses a challenge for near-term quantum machine learning (QML), where quantum circuits can process only a limited number of input features. We investigate a hybrid quantum-classical pipeline that transforms high-dimensional sentence embeddings into compact representations for variational quantum classification. The workflow combines a pretrained sentence-embedding model, dimensionality reduction, angle encoding, a variational quantum circuit (VQC), and a classical decision layer. We systematically compare principal component analysis (PCA), neighborhood components analysis (NCA), and linear discriminant analysis (LDA), covering both unsupervised and supervised dimensionality reduction. Using the TREC question-classification dataset, we study the relationship between representation dimensionality, information retention, qubit count, and classification performance. Preliminary PCA experiments reveal a strong information bottleneck: reducing 768-dimensional embeddings to 3, 4, 5, and 8 dimensions retains about 8.2%, 10.2%, 11.9%, and 16.4% of the variance, with corresponding classification accuracies of 50.3%, 51.2%, 57.9%, and 63.4%. In contrast, supervised reduction is substantially more efficient. LDA reaches 85.3% accuracy and NCA reaches 83.1% using only 5 dimensions, under a leakage-free cross-validation protocol, compared with 85.1% for a full 384-dimensional classical baseline. These results indicate that supervised dimensionality reduction can preserve task-relevant information far more effectively than variance-based compression, making compact representations a promising route toward practical hybrid quantum-classical NLP models.
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