Sequential machine learning on jet declustering history trees outperforms static models at identifying jet quenching in heavy-ion collision simulations.
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Quantum neuromorphic kernels outperform parameterized quantum kernels on low-dimensional datasets like Iris but underperform on high-dimensional SDSS data in spectral clustering tasks.
Angle encoding in hybrid quantum logistic regression yields the strongest performance among quantum variants, matching classical baselines in discrimination and achieving the lowest calibration error on pulsar candidate data.
citing papers explorer
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Jet Quenching Identification via Supervised Learning in Simulated Heavy-Ion Collisions
Sequential machine learning on jet declustering history trees outperforms static models at identifying jet quenching in heavy-ion collision simulations.
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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 Logistic Regression for Calibrated Classification of Pulsar Candidates
Angle encoding in hybrid quantum logistic regression yields the strongest performance among quantum variants, matching classical baselines in discrimination and achieving the lowest calibration error on pulsar candidate data.