After 30 months of fast-paced innovation in quantum algorithms, six research groups are hoping to hit paydirt. But there can be only one big winner—if there is a winner at all.

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.

A new AI framework called THOR is transforming how scientists calculate the behavior of atoms inside materials. Instead of relying on slow simulations that take weeks of supercomputer time, the system ...

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, ...

Incorporating multiple constraints such as task completion time, UAV payload capacity, and flight range into path optimization algorithms allows for more efficient search patterns.

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 ...

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 ...