Algorithms

While devising a new quantum algorithm, four researchers accidentally established a hard limit on entanglement.

Neural networks and other forms of machine learning ultimately learn by trial and error, one improvement at a time.

Computational complexity theorists have discovered a surprising new way to understand what makes certain problems hard.

Computer scientist Lance Fortnow writes that by embracing the computations that surround us, we can begin to understand and tame our seemingly random world.

By making use of randomness, a team has created a simple algorithm for estimating large numbers of distinct objects in a stream of data.

After years of false starts, a team of computer scientists has found a way to efficiently deduce the Hamiltonian of a physical system at any constant temperature.

Researchers have shown how to find the simplest description of a data set faster than by simply checking every possibility.

The prolific researcher found deep connections between randomness and computation and spent a career influencing cryptographers, complexity researchers and more.

Researchers have shown that a problem relating to the energy of a quantum system is easy for quantum computers but hard for classical ones.