Quantum and complex-valued hybrid networks for multi-principal element alloys phase prediction

This study introduces a hybrid network model for phase classification, integrating quantum networks and complex-valued neural networks. This architecture uses elemental composition as its only input, eliminating complex feature engineering. Parameterized quantum networks handle sparse elemental data and convert data from real to complex domains, increasing information dimensionality. Complex-valued neural networks process data in the complex domain, significantly reducing information loss during transitions. The experimental results show that the hybrid model achieves a phase classification accuracy of 94.93%, outperforming the best machine learning model by 2.27% and the quantum model by 8.67%. Precision, recall, and F1-score are also excellent at 0.9494, 0.9493, and 0.9500, respectively. Additional tests on phase transitions in <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow><mml:msub><mml:mtext>Al</mml:mtext> <mml:mi>x</mml:mi></mml:msub> <mml:mtext>CoCrFeNi</mml:mtext></mml:mrow> </mml:math> alloys confirm the model's robust generalization, identifying transition thresholds at 0.46 and 0.88, closely matching the 0.45 and 0.88 reported in related studies.