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 ...

Many engineering challenges come down to the same headache—too many knobs to turn and too few chances to test them. Whether tuning a power grid or designing a safer vehicle, each evaluation can be ...

Abstract: In scenarios where multiple decision-makers operate within a common decision space, each focusing on their own multi-objective optimization problem (e.g., bargaining games), the problem can ...

The CMO role is evolving, blending data science with AI's unpredictability. Niva Bupa's Nimish Agrawal highlights the shift ...