The field of machine learning is experiencing a significant shift with the integration of quantum computing, leading to innovative solutions in aerospace and molecular understanding. Researchers are exploring the potential of hybrid quantum-classical models to improve the accuracy and efficiency of predictive maintenance, network attack detection, and molecular property prediction. These models leverage the strengths of both quantum and classical computing to capture complex patterns in data and enhance robustness to uncertainties. The results demonstrate superior performance compared to classical machine learning approaches, particularly in detecting unseen attacks and predicting remaining useful life. Notable papers include:

• Capturing Aerodynamic Characteristics of ATTAS Aircraft with Evolving Intelligent System, which presents a novel deployment of an Evolving Type-2 Quantum Fuzzy Neural Network for modeling aerodynamic coefficients.
• Network Attack Traffic Detection With Hybrid Quantum-Enhanced Convolution Neural Network, which explores the use of Quantum Convolutional Neural Networks for detecting malicious traffic.
• Evaluating Effects of Augmented SELFIES for Molecular Understanding Using QK-LSTM, which analyzes the impact of augmented Self-Referencing Embedded Strings on molecular property prediction.
• Hybrid Quantum Recurrent Neural Network For Remaining Useful Life Prediction, which introduces a framework combining Quantum Long Short-Term Memory layers with classical dense layers for Remaining Useful Life forecasting.