Quantum Classification through Tournament Voting for Robust Single-Shot Inference

Abstract

Quantum machine learning (QML) promises powerful classification capabilities, but suffers from fragile output encodings and high sampling demands -- especially in multiclass settings. Traditional schemes such as one-hot and binary encoding either produce interpretable outputs too rarely or require many shots to achieve reliable predictions. We propose a decision aggregation framework for quantum multiclass classification based on round-robin tournament scoring. Each output qubit represents a binary comparison between class pairs, and the final prediction is determined by majority wins. This structure improves both the resolvability and accuracy of single-shot predictions, outperforming standard encodings under fixed shot-count conditions. Our method retains global entanglement while localizing decision tasks, enabling interpretable inference that remains reliable under intrinsic quantum randomness, without sacrificing expressivity. Empirical results show that this approach achieves high accuracy and interpretability under fixed shot-count measurements, suggesting a promising direction for future quantum classifiers.