Quadratic Unconstrained Binary

Quadratic Unconstrained Binary Optimization (QUBO) focuses on minimizing a quadratic function of binary variables, a problem frequently encountered in various NP-hard combinatorial optimization tasks. Current research emphasizes efficient QUBO formulations for diverse applications, including machine learning model training, combinatorial problems (e.g., graph problems, bin packing), and mission planning, often employing algorithms like simulated annealing, quantum annealing, and graph neural networks. The ability to efficiently solve QUBOs has significant implications for advancing both theoretical understanding of optimization and practical applications across diverse fields, particularly where classical methods struggle with computational complexity.