Anisotropy, quantified by dominant-dimension variance fraction, determines the best parameter-free similarity metric for text embeddings, with rank-based metrics gaining ~20% relative where cosine is weakest.
Riva Shalom, and Michal Cha- lamish
2 Pith papers cite this work. Polarity classification is still indexing.
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Quantum feature maps in k-means yield 88.6% accuracy on Iris and 91.0% on breast cancer data using shallow NISQ circuits, with improved stability over classical Euclidean distance.
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Anisotropy Decides Cosine vs. Rank Metrics for Text Embeddings
Anisotropy, quantified by dominant-dimension variance fraction, determines the best parameter-free similarity metric for text embeddings, with rank-based metrics gaining ~20% relative where cosine is weakest.
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Hybrid Quantum--Classical k-Means Clustering via Quantum Feature Maps
Quantum feature maps in k-means yield 88.6% accuracy on Iris and 91.0% on breast cancer data using shallow NISQ circuits, with improved stability over classical Euclidean distance.