Supervised Learning Guarantee for Quantum AdaBoost

• URL: http://arxiv.org/abs/2402.02376v2
• Date: Sat, 02 Nov 2024 06:02:37 GMT
• Title: Supervised Learning Guarantee for Quantum AdaBoost
• Authors: Yabo Wang, Xin Wang, Bo Qi, Daoyi Dong,
• Abstract summary: In the noisy intermediate-scale quantum (NISQ) era, variational quantum algorithms are constrained due to a limited number of qubits and the shallow depth of quantum circuits. In this paper, we theoretically establish and numerically verify a learning guarantee for quantum adaptive boosting (AdaBoost) Our work indicates that in the current NISQ era, introducing appropriate ensemble methods is particularly valuable in improving the performance of quantum machine learning algorithms.
• Score: 7.473180902609473
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• Abstract: In the noisy intermediate-scale quantum (NISQ) era, the capabilities of variational quantum algorithms are greatly constrained due to a limited number of qubits and the shallow depth of quantum circuits. We may view these variational quantum algorithms as weak learners in supervised learning. Ensemble methods are general approaches to combining weak learners to construct a strong one in machine learning. In this paper, by focusing on classification, we theoretically establish and numerically verify a learning guarantee for quantum adaptive boosting (AdaBoost). The supervised-learning risk bound describes how the prediction error of quantum AdaBoost on binary classification decreases as the number of boosting rounds and sample size increase. We further empirically demonstrate the advantages of quantum AdaBoost by focusing on a 4-class classification. The quantum AdaBoost not only outperforms several other ensemble methods, but in the presence of noise it can also surpass the ideally noiseless but unboosted primitive classifier after only a few boosting rounds. Our work indicates that in the current NISQ era, introducing appropriate ensemble methods is particularly valuable in improving the performance of quantum machine learning algorithms.

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