Quantum computers may enable more natural manipulation of Fourier spectra in ML models via the Quantum Fourier Transform, potentially leading to resource-efficient spectral methods.
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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.
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Spectral methods: crucial for machine learning, natural for quantum computers?
Quantum computers may enable more natural manipulation of Fourier spectra in ML models via the Quantum Fourier Transform, potentially leading to resource-efficient spectral methods.
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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.