Neural-IC separates embedding inequalities from capacity bounds in query-separated computations, with one-bit RAC benchmarks and CHSH-layer stability selecting the Tsirelson threshold for quantum enhancements.
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2 Pith papers cite this work. Polarity classification is still indexing.
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PAPUS is a pair-adaptive quantum classification method in Pauli space that reaches over 90% accuracy on 9 datasets with lower measurement and gate costs and only 1.67% accuracy drop under noise compared to 9.44% for baselines.
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Neural Information Causality
Neural-IC separates embedding inequalities from capacity bounds in query-separated computations, with one-bit RAC benchmarks and CHSH-layer stability selecting the Tsirelson threshold for quantum enhancements.
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PAPUS: Pauli-Space-Based Multiclass Quantum Classification
PAPUS is a pair-adaptive quantum classification method in Pauli space that reaches over 90% accuracy on 9 datasets with lower measurement and gate costs and only 1.67% accuracy drop under noise compared to 9.44% for baselines.