论文标题
使用各种量子分类器的粒子物理学量子机学习
Quantum Machine Learning for Particle Physics using a Variational Quantum Classifier
论文作者
论文摘要
Quantum机器学习旨在释放量子计算的能力以改善机器学习方法。通过将量子计算方法与经典神经网络技术相结合，我们旨在促进解决分类问题的性能提高。我们的算法专为现有和近期量子设备而设计。我们提出了一种新型的混合变分量子分类器，该量子分类器将量子梯度下降方法与最陡的梯度下降相结合，以优化网络的参数。通过将此算法应用于Di-Top最终状态的共振搜索，我们发现该方法比经典的神经网络或通过非量化优化方法训练的经典神经网络或量子机学习方法更好。对少量数据进行培训的分类器表明其在数据驱动的分类问题中的好处。
Quantum machine learning aims to release the prowess of quantum computing to improve machine learning methods. By combining quantum computing methods with classical neural network techniques we aim to foster an increase of performance in solving classification problems. Our algorithm is designed for existing and near-term quantum devices. We propose a novel hybrid variational quantum classifier that combines the quantum gradient descent method with steepest gradient descent to optimise the parameters of the network. By applying this algorithm to a resonance search in di-top final states, we find that this method has a better learning outcome than a classical neural network or a quantum machine learning method trained with a non-quantum optimisation method. The classifiers ability to be trained on small amounts of data indicates its benefits in data-driven classification problems.
