Quantum Annealing

Quantum annealing (QA) is a metaheuristic for finding the global minimum of a given objective function over a given set of candidate solutions (candidate states), by a process using quantum fluctuations Quantum annealing is used mainly for problems where the search space is discreate (combinatorial optimization problems) with manylocal minima; such as finding the ground state of a spin glass.


Quantum Monte Carlo

Quantum Monte Carlo encompasses a large family of computional methods whose common aim is the study of complex quantum systems. One of the major goals of these approaches is to provide a reliable solutions(or an accurate appoximation) of the quantum many-body problem. The diverse flavors of quantum Monte Carlo approaches all share the common use of the Monte Carlo method to handle the multi-dimentional integrals that arise in the different formulations of the many-body problem.

https://en.wikipedia.org/wiki/Quantum_Monte Carlo

Neural Network

In machine learning and congnitive science, artificial neural networks(ANNs) are a family of models inspired by biological neural networks ( the central nervous systems of animals, in particular the brain) and are used to estimate or approximate funtions that can depend on a large number of inputs and are generally unknown. Artificial neural networks are generally presented as systems of interconnected `neurons` which exchange messages between each other.

https://en.wikipedia.org/wiki/Artificial neural network