用量子叠加态实现可加权的同质量子分类集成，提升预测精度。
A weighted quantum ensemble of homogeneous quantum classifiers
- 通过量子索引寄存器编码数据，利用叠加态实现不同数据子集的并行训练。
- 在测试时将权重编码进量子电路，实现量子并行加权集成推理。
- 适合对量子机器学习性能优化感兴趣的科研人员，兼具理论与实证价值。
集成学习通过组合多个模型提升预测准确性，关键在于确保预测器间的多样性。同质集成使用相同模型，通过不同的数据子集实现多样性；加权平均集成则通过权重学习过程赋予更准确模型更高影响力。本文提出一种基于量子分类器的加权同质量子集成方法，采用带索引寄存器的量子分类器进行数据编码。该方法利用基于实例的量子分类器，通过叠加态和受控酉操作实现特征与训练点的子采样，并可在叠加态中并行执行具有不同数据构成的内部分类器。该方法结合量子电路执行与经典权重优化的学习过程，使训练后的集成在测试时将权重编码于电路中实现量子并行执行。实证评估表明该方法有效，为性能表现提供了深入洞察。
原文摘要 · Abstract (English)
Ensemble methods in machine learning aim to improve prediction accuracy by combining multiple models. This is achieved by ensuring diversity among predictors to capture different data aspects. Homogeneous ensembles use identical models, achieving diversity through different data subsets, and weighted-average ensembles assign higher influence to more accurate models through a weight learning procedure. We propose a method to achieve a weighted homogeneous quantum ensemble using quantum classifiers with indexing registers for data encoding. This approach leverages instance-based quantum classifiers, enabling feature and training point subsampling through superposition and controlled unitaries, and allowing for a quantum-parallel execution of diverse internal classifiers with different data compositions in superposition. The method integrates a learning process involving circuit execution and classical weight optimization, for a trained ensemble execution with weights encoded in the circuit at test-time. Empirical evaluation demonstrate the effectiveness of the proposed method, offering insights into its performance.
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