An efficient quantum algorithm for ensemble classification using bagging

IF 2.5 Q3 QUANTUM SCIENCE & TECHNOLOGY IET Quantum Communication Pub Date : 2024-02-02 DOI:10.1049/qtc2.12087
Antonio Macaluso, Luca Clissa, Stefano Lodi, Claudio Sartori
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Abstract

Ensemble methods aggregate predictions from multiple models, typically demonstrating improved accuracy and reduced variance compared to individual classifiers. However, they often come with significant memory usage and computational time requirements. A novel quantum algorithm that leverages quantum superposition, entanglement, and interference to construct an ensemble of classification models using bagging as an aggregation strategy is introduced. Through the generation of numerous quantum trajectories in superposition, the authors achieve B transformations of the training set with only log B $\mathit{log}\left(B\right)$ operations, allowing an exponential enlargement of the ensemble size while linearly increasing the depth of the corresponding circuit. Moreover, when assessing the algorithm's overall cost, the authors demonstrate that the training of a single weak classifier contributes additively to the overall time complexity, as opposed to the multiplicative impact commonly observed in classical ensemble methods. To illustrate the efficacy of the authors’ approach, experiments on reduced real-world datasets utilising the IBM qiskit environment to demonstrate the functionality and performance of the proposed algorithm are introduced.

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使用袋集技术进行集合分类的高效量子算法
集合方法汇总了多个模型的预测结果,与单个分类器相比,通常能提高准确性并减少方差。然而,这些方法往往需要占用大量内存和计算时间。本文介绍了一种新颖的量子算法,它利用量子叠加、纠缠和干涉来构建分类模型集合,并将袋聚作为一种聚合策略。通过在叠加中生成大量量子轨迹,作者仅用运算就实现了训练集的 B 变换,从而在线性增加相应电路深度的同时,以指数形式扩大了集合规模。此外,在评估算法的总体成本时,作者证明了单个弱分类器的训练对总体时间复杂度的影响是加法,而不是经典集合方法中常见的乘法。为了说明作者方法的有效性,介绍了利用 IBM qiskit 环境在缩小的真实世界数据集上进行的实验,以展示所提算法的功能和性能。
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