Study of Feature Importance for Quantum Machine Learning Models

Understand

Predictor importance is a crucial part of data preprocessing pipelines in classical and quantum machine learning (QML).

• This work presents the first study of its kind in which feature importance for QML models has been explored and contrasted against their classical machine learning (CML) equivalents.
• We developed a hybrid quantum-classical architecture where QML models are trained and feature importance values are calculated from classical algorithms on a real-world dataset.
• This architecture has been implemented on ESPN Fantasy Football data using Qiskit statevector simulators and IBM quantum hardware such as the IBMQ Mumbai and IBMQ Montreal systems.