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International Conference on Electronic Information Engineering and Computer Science (EIECS)

Development of Weak Classifiers and Optimized Implementation of the QBoost Ensemble Model

Jiarui Liu, X.H. Wu, Yiwei Liao, Binlei Zhang

Published Sep 26, 2025• 0 citations
Publisher

Abstract

Ensemble learning algorithms, especially Boosting, have achieved remarkable success in classification tasks by combining multiple weak classifiers-you know, the sort that aren’t all that strong by themselves but work real well when you put them together-though traditional methods like AdaBoost? They’re prone to overfitting in noisy datasets, see? Like when a few labels are off or the data has random little glitches, they end up fitting that noise too hard instead of the real pattern, and methods like QBoost-the ones that use quantuminspired optimization, right?-they usually run into two problems: picking the right weak classifiers and just not being very fast computationally, so this study? We propose an optimized QBoost version plus a new way to build weak classifiers, just to fix those issues we mentioned, and the weak classifiers here? We made them based on decision stumps but added adaptive feature weighting, and that weighting part, by the way, helps make the weak classifiers more diverse and better at telling different classes apart, while as for the QBoost framework itself? We tweaked the objective function-threw in a regularization term, yeah?-to balance two things: getting solid classification accuracy and not letting the model get too complex, and we also used a gradient-based optimization algorithm to speed up convergence so the model doesn’t take forever to train; now, the experimental results?We tested on MNIST, CIFAR-10, and UCI Credit. The method shows 3.2% higher accuracy than AdaBoost.It cuts training time by 28%. In label-noise settings, the error rate falls by 5.7%.

DOI
10.1109/eiecs67708.2025.11283456

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Ensemble learning algorithms, especially Boosting, have achieved remarkable success in classification tasks by combining multiple weak classifiers-you know, the sort that aren’t all that strong by themselves but work real well when you put them together-though traditional methods like AdaBoost? They’re prone to overfitting in noisy datasets, see? Like when a few labels are off or the data has random little glitches, they end up fitting that noise too hard instead of the real pattern, and methods like QBoost-the ones that use quantuminspired optimization, right?-they usually run into two problems: picking the right weak classifiers and just not being very fast computationally, so this study? We propose an optimized QBoost version plus a new way to build weak classifiers, just to fix those issues we mentioned, and the weak classifiers here? We made them based on decision stumps but added adaptive feature weighting, and that weighting part, by the way, helps make the weak classifiers more diverse and better at telling different classes apart, while as for the QBoost framework itself? We tweaked the objective function-threw in a regularization term, yeah?-to balance two things: getting solid classification accuracy and not letting the model get too complex, and we also used a gradient-based optimization algorithm to speed up convergence so the model doesn’t take forever to train; now, the experimental results?We tested on MNIST, CIFAR-10, and UCI Credit. The method shows 3.2% higher accuracy than AdaBoost.It cuts training time by 28%. In label-noise settings, the error rate falls by 5.7%.
QBoost, a quantum-inspired ensemble learning algorithm, has performance that depends on the quality of weak classifiers and efficiency of the optimization process. Existing methods suffer from insufficient diversity among weak classifiers and low efficiency in handling nonlinear data patterns, leading to unsatisfactory generalization ability. This study proposes a new weak classifier construction method based on pruned random decision trees-combined with a multi-objective optimized QBoost model-to fix these problems. Weak classifiers are constructed using random subspaces with adaptive tree pruning; this enhances diversity while cutting overfitting. For QBoost, the study introduces a multi-objective optimization framework to minimize classification loss and maximize weak classifier diversity at the same time, and uses an improved AdamW algorithm to speed up convergence. Experiments on KDD Cup 99, IMDb, and Covertype datasets show the proposed method hits 96.8%96.8 \%96.8% classification accuracy on KDD Cup 99- 3.3%\mathbf{3. 3 \%}3.3% higher than Gradient Boosting (93.5%93.5 \%93.5%) and 2.6%2.6 \%2.6% higher than standard QBoost (94.2%94.2 \%94.2%). Compared with standard QBoost, its training time drops by 37%37 \%37%, and it keeps 89.7%89.7 \%89.7% accuracy under 15%15 \%15% feature noise-proving better robustness. These findings confirm the proposed method effectively improves classification performance, efficiency, and diversity.
Classification is one of the main applications of supervised learning. Recent advancements in developing quantum computers have opened a new possibility for machine learning on such machines. Due to the noisy performance of near-term quantum computers, error mitigation techniques are essential for extracting meaningful data from noisy raw experimental measurements. Here, we propose two ensemble-learning error mitigation methods, namely, bootstrap aggregating and adaptive boosting, which can significantly enhance the performance of variational quantum classifiers for both classical and quantum datasets. The idea is to combine several weak classifiers, each implemented on a shallow noisy quantum circuit, to make a strong one with high accuracy. While both of our protocols substantially outperform error-mitigated primitive classifiers, the adaptive boosting shows better performance than the bootstrap aggregating. The protocols have been exemplified for classical handwriting digits as well as quantum phase discrimination of a symmetry-protected topological Hamiltonian, in which we observe a significant improvement in accuracy. Our ensemble-learning methods provide a systematic way of utilizing shallow circuits to solve complex classification problems. Published by the American Physical Society 2024
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.
A powerful way to improve performance in machine learning is to construct an ensemble that combines the predictions of multiple models. Ensemble methods are often much more accurate and lower variance than the individual classifiers that make them up but have high requirements in terms of memory and computational time. In fact, a large number of alternative algorithms is usually adopted, each requiring to query all available data. We propose a new quantum algorithm that exploits quantum superposition, entanglement and interference to build an ensemble of classification models. Thanks to the generation of the several quantum trajectories in superposition, we obtain BBB transformations of the quantum state which encodes the training set in only log(B)log\left(B\right)log(B) operations. This implies exponential growth of the ensemble size while increasing linearly the depth of the correspondent circuit. Furthermore, when considering the overall cost of the algorithm, we show that the training of a single weak classifier impacts additively the overall time complexity rather than multiplicatively, as it usually happens in classical ensemble methods. We also present small-scale experiments on real-world datasets, defining a quantum version of the cosine classifier and using the IBM qiskit environment to show how the algorithms work.
We introduce a novel discrete optimization method for training in the context of a boosting framework for large scale binary classifiers. The motivation is to cast the training problem into the format required by existing adiabatic quantum hardware. First we provide theoretical arguments concerning the transformation of an originally continuous optimization problem into one with discrete variables of low bit depth. Next we propose QBoost as an iterative training algorithm in which a subset of weak classifiers is selected by solving a hard optimization problem in each iteration. A strong classifier is incrementally constructed by concatenating the subsets of weak classifiers. We supplement the findings with experiments on one synthetic and two natural data sets and compare against the performance of existing boosting algorithms. Finally, by conducting a quantum Monte Carlo simulation we gather evidence that adiabatic quantum optimization is able to handle the discrete optimization problems generated by QBoost.
Abstract A powerful way to improve performance in machine learning is to construct an ensemble that combines the predictions of multiple models. Ensemble methods are often much more accurate and have a lower variance than the individual classifiers that make them up, but have high requirements in terms of memory and computational time. In fact, a large number of alternative algorithms is usually adopted, each requiring to query all available data. We propose a new quantum algorithm that exploits quantum superposition, entanglement and interference to build an ensemble of classification models. Thanks to the generation of the several quantum trajectories in superposition, we obtain B transformations of the quantum state which encodes the training set in only log ( B ) operations. This implies an exponential growth of the ensemble size while increasing linearly the depth of the correspondent circuit. Furthermore, when considering the overall cost of the algorithm, we show that the training of a single weak classifier impacts additively to the overall time complexity rather than multiplicatively, as it usually happens in classical ensemble methods. We also present small-scale experiments on real-world datasets, defining a quantum version of the cosine classifier and using the IBM qiskit environment to show how the algorithms work.