The field of optimization and quantum computing is rapidly advancing, with a focus on developing innovative methods and techniques to solve complex problems. Researchers are exploring new approaches, such as the use of graph neural networks, quantum graph attention networks, and federated learning, to improve the efficiency and effectiveness of optimization algorithms. Additionally, the integration of quantum computing and machine learning is showing promise in areas such as logistics and distribution, as well as in the monitoring of hazardous environments. Noteworthy papers in this area include: Random-Key Metaheuristic and Linearization for the Quadratic Multiple Constraints Variable-Sized Bin Packing Problem, which proposes a new method for solving a challenging combinatorial optimization problem. NFQ2.0: The CartPole Benchmark Revisited, which presents a modernized variant of a pioneering algorithm and demonstrates its improved performance and stability. Warm-starting active-set solvers using graph neural networks, which proposes a learning-to-optimize approach using graph neural networks to predict active sets in quadratic programming solvers. Empirical Quantum Advantage in Constrained Optimization from Encoded Unitary Designs, which introduces a new algorithm that achieves a significant reduction in shot complexity and demonstrates an exponential separation in the minimax sense. A Graph-Based, Distributed Memory, Modeling Abstraction for Optimization, which presents a general and flexible modeling abstraction for building and working with distributed optimization problems. Vehicle Routing Problems via Quantum Graph Attention Network Deep Reinforcement Learning, which proposes a hybrid model that maintains the expressive capacity of graph attention encoders while reducing trainable parameters. D2D Power Allocation via Quantum Graph Neural Network, which presents a fully quantum graph neural network that implements message passing via parameterized quantum circuits. Optimus-Q: Utilizing Federated Learning in Adaptive Robots for Intelligent Nuclear Power Plant Operations through Quantum Cryptography, which introduces a sophisticated system that leverages adaptive learning techniques and secure quantum communication to enhance safety and efficiency in nuclear power plants.