It's not necessarily about what programming language you learn or use. It's about how you approach problem solving.

By harnessing randomness, a new algorithm achieves a fundamentally novel — and faster — way of performing one of the most basic computations in math and computer science.

To come up with practical answers in the real world, computer scientists use approximation algorithms, methods that don’t solve these problems exactly but get close enough to be helpful.

A new algorithm that fast forwards simulations could bring greater use ability to current and near-term quantum computers, opening the way for applications to run past strict time limits that ...

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 ...

A discovery about how algorithms can learn and retain information more efficiently offers potential insight into the brain's ability to absorb new knowledge. The findings could aid in ...

In Algorithms Are Not Enough, Roitblat provides ideas on what to look for to advance AI systems that can actively seek and solve problems that they have not been designed for.

“When solving a very large computational problem, optimization solvers can require significant computational time to find a first feasible solution,” said Dr. Timo Berthold, director of Mixed ...

The MIT algorithm mimics this nonlinear phenomenon on a quantum computer, using Bose-Einstein math to connect nonlinearity and linearity. So by imagining a pseudo Bose-Einstein condensate tailor made ...