Quantum Decision

Quantum decision-making explores how principles of quantum mechanics, such as superposition and entanglement, can improve classical decision-making models, particularly in complex scenarios where human behavior deviates from classical probability. Current research focuses on developing quantum-inspired algorithms, including quantum-like evolutionary algorithms and quantum decision trees, to enhance forecasting accuracy and address limitations in classical machine learning approaches for tasks like probabilistic forecasting and collective decision-making. This field is significant for its potential to improve the performance of artificial intelligence systems, particularly in areas like finance and social network analysis, while also offering new insights into human decision-making under uncertainty.