An adaptive quantum approximate optimization algorithm for solving combinatorial problems on a quantum computer
Abstract
The quantum approximate optimization algorithm (QAOA) is a hybrid variational quantumclassical algorithm that solves combinatorial optimization problems. While there is evidence suggesting that the fixed form of the original QAOA ansatz is not optimal, there is no systematic approach for finding better ansätze. We address this problem by developing an iterative version of QAOA that is problemtailored, and which can also be adapted to specific hardware constraints. We simulate the algorithm on a class of MaxCut graph problems and show that it converges much faster than the original QAOA, while simultaneously reducing the required number of CNOT gates and optimization parameters.
 Publication:

arXiv eprints
 Pub Date:
 May 2020
 arXiv:
 arXiv:2005.10258
 Bibcode:
 2020arXiv200510258Z
 Keywords:

 Quantum Physics
 EPrint:
 5 pages, 2 figures