Solving complex optimization problems is central to many modern technologies, from logistics and financial modeling to chip ...

Tensor networks enable researchers to tackle quantum physics problems previously thought to be solvable only by quantum computers. Credit: Lucy Reading-Ikkanda/Simons Foundation By applying a 1980s ...

The technology uses predictive algorithms to identify frequently accessed data and move it between flash storage and high-speed memory in real time, reducing the amount of expensive DRAM a data center ...

Crystal Carter and Jen Cornwell tackled AI search from opposite angles at SMX Advanced. Together, they reveal why most ...

Abstract: Robust multiobjective optimization problems (RMOPs) widely exist in real-world applications, which introduce a variety of uncertainty in optimization models. While some evolutionary ...

Abstract: The Tardy/Lost (TL) penalties scheduling is a discrete optimization problem. TL scheduling problem is an NP-hard problem. As a result, proposing an optimization algorithm to handle this ...

The original version of this story appeared in Quanta Magazine. For computer scientists, solving problems is a bit like mountaineering. First they must choose a problem to solve—akin to identifying a ...

It’s been difficult to find important questions that quantum computers can answer faster than classical machines, but a new algorithm appears to do it for some critical optimization tasks. For ...

Conventional quantum algorithms are not feasible for solving combinatorial optimization problems (COPs) with constraints in the operation time of quantum computers. To address this issue, researchers ...