Quantum neuromorphic kernels outperform parameterized quantum kernels on low-dimensional datasets like Iris but underperform on high-dimensional SDSS data in spectral clustering tasks.
K-means clustering on noisy intermediate scale quantum computers
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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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Quantum Spectral Clustering: Comparing Parameterized and Neuromorphic Quantum Kernels
Quantum neuromorphic kernels outperform parameterized quantum kernels on low-dimensional datasets like Iris but underperform on high-dimensional SDSS data in spectral clustering tasks.
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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.