After 30 months of fast-paced innovation in quantum algorithms, six research groups are hoping to hit paydirt. But there can ...

Abstract: Real-world constrained multiobjective optimization problems (CMOPs) are prevalent and often come with stringent time-sensitive requirements. However, most contemporary constrained ...

The annotation, recruitment, grounding, display, and won gates determine which content AI engines trust and recommend. Here’s ...

In large retail operations, category management teams spend significant time deciding which product goes onto which shelf and in which order. Shelf space is very expensive real estate in retail.

In most boardrooms, the final decision still comes down to a small circle of leaders weighing a narrow set of choices. Yet the problems they face now contain thousands, sometimes millions, of possible ...

Practical Application: The authors propose QFI-Informed Mutation (QIm), a heuristic that adapts mutation probabilities using diagonal QFI entries. QIm outperforms uniform and random-restart baselines, ...

Abstract: Solving constrained multi-objective optimization problems (CMOPs) is a challenging task due to the presence of multiple conflicting objectives and intricate constraints. In order to better ...

Overview: Quantum AI combines quantum computing with artificial intelligence to solve complex problems beyond the reach of ...