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, ...

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

Through the looking glass: In a field increasingly defined by quantum experiments and exotic materials, a physics team at Queen's University in Canada has shown that innovation can also come from the ...

Some users had accused the app of blocking them from posting videos about Immigration and Customs Enforcement. The app said it was a power outage issue. By David McCabe TikTok said on Tuesday that its ...

Abstract: Over the past decades, extensive research has been conducted on adversarial attacks and defense mechanisms in deep learning, particularly in real-world applications such as autonomous ...

The Traveling Salesman Problem (TSP), a quintessential challenge in computational theory, involves finding the shortest route that visits each city exactly once before returning to the starting point.