A feature engineering method for machine learning inspired by quantum mechanics.

This work proposes a quantum-state-based feature engineering (QSFE) method for machine learning. QSFE uses wave functions that describe microscopic particle systems as mappings. By QSFE, original inputs or features extracted by neural networks are processed as quantum states to train wave function parameters. The experiments demonstrate that QSFE can improve the feature recalibration ability in deep neural networks. QSFE has a low computational cost, almost no additional parameters, and is easy to integrate with other modules. This work unfolds two effectiveness of QSFE: firstly, QSFE can enhance the expression ability of the model and make full use of the features extracted from the previous network; secondly, QSFE can extract complex spatial and temporal interactions, following the self-organization theory. The validations on various machine learning tasks, including a classical self-organization model, indicate that QSFE is valuable in interdisciplinary machine learning applications.