Adaptive quantum approximate optimization algorithm for solving combinatorial problems on a quantum computer
Linghua Zhu
University of Washington, USA
: Res J Opt Photonics
Abstract
The quantum approximate optimization algorithm (QAOA) is a hybrid variational quantum-classical algorithm that solves combinatorial optimization problems. While there is evidence suggesting that the fixed form of the standard 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 problem tailored, and which can also be adapted to specific hardware constraints. We simulate the algorithm on a class of Max-Cut graph problems and show that it converges much faster than the standard QAOA, while simultaneously reducing the required number of CNOT gates and optimization parameters. We also provide evidence that this speedup is connected to the concept of shortcuts to adiabaticity. Besides, using dynamically generated, problem-tailored ansatze allows for arbitrarily accurate Gibbs state preparation using low-depth circuits.
Biography
Linghuan Zhu has completed her PhD from New Jersey Institute of Technology and postdoctoral studies from Virginia Tech. She is working at the University of Washington as postdoctoral associate. She has published several papers in reputed journals and has been serving as an editorial board member of repute.
