Light-Cone Feature Selection for Quantum Machine Learning

Abstract

Feature selection plays an essential role in improving the predictive performance and interpretability of trained models in classical machine learning. On the other hand, the usability of conventional feature selection can be limited for quantum machine learning (QML) tasks; the technique may not provide a clear interpretation on embedding quantum circuits for classical data tasks and, more importantly, is not applicable to quantum data tasks. In this work, a feature selection method is proposed with a specific focus on QML. This scheme treats the light-cones (i.e., subspace) of quantum models as features and then select relevant ones through training of the corresponding local quantum kernels. Its versatility is numerically demonstrated for four different applications using toy tasks: (1) feature selection of classical inputs, (2) circuit architecture search for data embedding, (3) compression of quantum machine learning models and (4) subspace selection for quantum data. The proposed framework paves the way toward applications of QML to practical tasks. Also, this technique could be used to practically test if the QML tasks really need quantumness, while it is beyond the scope of this work.