An efficient quantum algorithm for ensemble classification using bagging

Author:

Macaluso Antonio1ORCID,Clissa Luca23,Lodi Stefano4,Sartori Claudio4

Affiliation:

1. Agents and Simulated Reality Department German Research Center for Artificial Intelligence (DFKI) Saarbruecken Germany

2. Department of Physics and Astronomy University of Bologna Bologna Italy

3. Istituto Nazionale di Fisica Nucleare (INFN) Bologna Italy

4. Department of Computer Science and Engineering University of Bologna Bologna Italy

Abstract

AbstractEnsemble 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 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.

Publisher

Institution of Engineering and Technology (IET)

Cited by 2 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Quantum Supervised Learning;KI - Künstliche Intelligenz;2024-07-19

2. $$\mathrm{Q(AI)}^2$$: Quantum Artificial Intelligence for the Automotive Industry;KI - Künstliche Intelligenz;2024-07-17

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