The field of quantum machine learning is moving towards exploring the potential of quantum computing in addressing complex problems in various domains. Researchers are investigating the application of quantum machine learning models in areas such as image classification, sentiment analysis, financial fraud detection, and reinforcement learning. Notably, quantum kernel methods are being used to enhance the prediction of CAR T-cell cytotoxicity, while density operator expectation maximization algorithms are being developed to learn latent variable models defined through density operator models. Quantum geometry is also being used to encode data, providing a rich geometric and topological structure. Noteworthy papers in this area include: Quantum-Cognitive Tunnelling Neural Networks for Military-Civilian Vehicle Classification and Sentiment Analysis, which employs novel QT-based neural networks to distinguish between military and civilian vehicles. Enhanced Prediction of CAR T-Cell Cytotoxicity with Quantum-Kernel Methods, which proposes a quantum approach using a Projected Quantum Kernel to address the challenge of identifying and experimentally testing new CAR constructs. DO-EM: Density Operator Expectation Maximization, which develops an Expectation-Maximization framework to learn latent variable models defined through density operator models on classical hardware. A Bit of Freedom Goes a Long Way: Classical and Quantum Algorithms for Reinforcement Learning under a Generative Model, which proposes novel classical and quantum online algorithms for learning finite-horizon and infinite-horizon average-reward Markov Decision Processes.