Towards Ultimate Accuracy in Quantum Multi-Class Classification: A Trace-Distance Binary Tree AdaBoost Classifier

TL;DR

The paper introduces a quantum multi-class classifier called Trace-distance binary Tree AdaBoost (TTA) that improves accuracy and resource efficiency by hierarchical binary splitting and ensemble learning, suitable for near-term quantum devices.

Contribution

It presents a novel hierarchical quantum classifier combining trace-distance-based bipartitions with AdaBoost, enhancing trainability and scalability in quantum multi-class classification.

Findings

Achieves near 100% test accuracy on quantum and classical benchmarks.

Robust against common quantum errors and noise.

Utilizes many shallow circuits for resource efficiency.

Abstract

We propose a Trace-distance binary Tree AdaBoost (TTA) multi-class quantum classifier, a practical pipeline for quantum multi-class classification that combines quantum-aware reductions with ensemble learning to improve trainability and resource efficiency. TTA builds a hierarchical binary tree by choosing, at each internal node, the bipartition that maximizes the trace distance between average quantum states; each node trains a binary AdaBoost ensemble of shallow variational quantum base learners. By confining intrinsically hard, small trace distance distinctions to small node-specific datasets and combining weak shallow learners via AdaBoost, TTA distributes capacity across many small submodels rather than one deep circuit, mitigating barren-plateau and optimization failures without sacrificing generalization. Empirically TTA achieves top test accuracy ($\approx $100\%) among quantum and classical baselines, is robust to common quantum errors, and realizes aggregate systems with 10000 cumulative layers and 0.2M parameters, implemented as many shallow circuits. Our results are empirical and implementable on near-term platforms, providing a resource-efficient route to scalable multi-class quantum machine learning.