PAPUS: Pauli-Space-Based Multiclass Quantum Classification
Yuhang Tu, Shengmei Zhao, Le Wang, Anqi Zhang

TL;DR
PAPUS introduces a Pauli-space-based adaptive quantum classification framework that dynamically balances accuracy and resource use, demonstrating high performance and robustness across multiple datasets and noise conditions.
Contribution
The paper presents a novel pair-adaptive quantum classification method that adjusts circuit complexity based on class difficulty, improving efficiency and robustness.
Findings
Achieves over 90% accuracy in simulations and real quantum hardware.
Reduces measurement and circuit costs compared to traditional methods.
Maintains high robustness with minimal accuracy loss under noise.
Abstract
Quantum classification faces two key challenges. First, the difficulty of distinguishing between different classes varies: some class pairs are easy to separate, while others are more challenging. Second, practical execution is affected by noise, finite sampling, and measurement overhead. To address these issues, we propose PAPUS, a framework for pair-adaptive quantum classification in Pauli space. The method evaluates candidate upload circuits using low-weight Pauli features and formulates upload design as a structured model selection problem based on discriminative representations. By dynamically adjusting circuit complexity according to class-pair difficulty, the framework achieves a better balance between classification accuracy and resource efficiency. Experiments on 9 data sets with 474 tasks show that PAPUS achieves a favorable balance between predictive performance and execution…
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