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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2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
fields
quant-ph 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
Adaptive quantum ansatze outperform fixed UCCSD in ph-AFQMC projected energies for stretched H chains while using more compact circuits.
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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.
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Benchmarking quantum trial wavefunctions for phaseless auxiliary-field quantum Monte Carlo
Adaptive quantum ansatze outperform fixed UCCSD in ph-AFQMC projected energies for stretched H chains while using more compact circuits.