What are Quantum approximation optimization algorithms?

Highlights of Peter Shor’s presentation, indicated in this link ( https://www.youtube.com/watch?v=HHIWUi3GmdM).

Approximate quantum optimization algorithms. What is it?

Optimization problems are often very difficult, they are usually the most difficult among computationally resolvable problems.

Therefore, approximate algorithms , such as genetic algorithms for example, are important options for this type of problem. These are sub-optimal algorithms, in the sense that they deliver good solutions, but do not guarantee the optimal one. This study area is well established in classical computing.

The idea is to research approximate quantum algorithms .

Some advantages: they can be robust to errors, and therefore require less qubits.

The world is expected to take a long time to achieve a large number of controllable and robust error qubits.

QAOA algorithms can begin to add value to to the world even before quantum computers gain scale. Therefore, these can be of enormous use in the near future.

See also:

Meet the quantum computing study group:

Technical ideas with a bit of philosophy: