Quantum machine learning provides a fundamentally different approach to analyzing data. However, many interesting datasets are too complex for currently available quantum computers. Present quantum machine learning applications usually diminish this complexity by reducing the dimensionality of the data, e.g., via auto-encoders, before passing it through the quantum models. Here, we design a classical-quantum paradigm that unifies the dimensionality reduction task with a quantum classification model into a single architecture: the guided quantum compression model. We exemplify how this architecture outperforms conventional quantum machine learning approaches on a challenging binary classification problem: identifying the Higgs boson in proton-proton collisions at the LHC. Furthermore, the guided quantum compression model shows better performance compared to the deep learning benchmark when using solely the kinematic variables in our dataset.

翻译：量子机器学习为数据分析提供了一种根本不同的方法。然而，许多有趣的数据集对于当前可用的量子计算机而言过于复杂。现有的量子机器学习应用通常在将数据输入量子模型之前，通过降低数据维度（例如使用自编码器）来简化复杂度。本文设计了一种经典-量子混合范式，将降维任务与量子分类模型统一到单一架构中：引导式量子压缩模型。我们通过一个具有挑战性的二元分类问题——在LHC质子-质子对撞中识别希格斯玻色子——展示了该架构如何超越传统量子机器学习方法。此外，当仅使用数据集中的运动学变量时，引导式量子压缩模型相较于深度学习基准表现出更优的性能。