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.
For one samplex, the up- loaded state is produced by a sequenceU= [g 1
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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.