Quantum Inspired

Quantum-inspired computing leverages principles from quantum mechanics to enhance classical algorithms, primarily focusing on optimization and machine learning tasks. Current research emphasizes the development and application of quantum-inspired evolutionary algorithms and metaheuristics, such as those incorporating chaos theory and techniques like QUBO (Quadratic Unconstrained Binary Optimization) solvers, for problems ranging from feature selection in high-dimensional data to probabilistic forecasting. These methods aim to improve efficiency and solution quality compared to purely classical approaches, with applications spanning diverse fields including medicine, finance, and engineering. The ongoing exploration of efficient multi-objective optimization within this framework is a key area of focus.