论文标题
训练分类器的量子合奏
Quantum ensemble of trained classifiers
论文作者
论文摘要
通过叠加，量子计算机能够根据可用的量子数来表示一组巨大的状态。 Quantum机器学习是量子计算的子字段，它探讨了量子计算增强机器学习算法的潜力。一种名为量子分类器的量子机器学习方法的方法包括使用叠加来构建一个指数型的分类器集合，以通过无优化的学习算法训练。在这项工作中，我们研究了量子集合如何与添加优化方法一起工作。使用基准数据集的实验显示了通过添加优化步骤获得的改进。
Through superposition, a quantum computer is capable of representing an exponentially large set of states, according to the number of qubits available. Quantum machine learning is a subfield of quantum computing that explores the potential of quantum computing to enhance machine learning algorithms. An approach of quantum machine learning named quantum ensembles of quantum classifiers consists of using superposition to build an exponentially large ensemble of classifiers to be trained with an optimization-free learning algorithm. In this work, we investigate how the quantum ensemble works with the addition of an optimization method. Experiments using benchmark datasets show the improvements obtained with the addition of the optimization step.
