Work

Robust Control Optimization for Quantum Approximate Optimization Algorithms

Year: 2020

Type: article

Abstract: Quantum variational algorithms have garnered significant interest recently, due to their feasibility of being implemented and tested on noisy intermediate scale quantum (NISQ) devices. We examine the robustness of the quantum approximate optimization algorithm (QAOA), which can be used to solve certain quantum control problems, state preparation problems, and combinatorial optimization problems. We demonstrate that the error of QAOA simulation can be significantly reduced by robust control optimization techniques, specifically, by sequential convex programming (SCP), to ensure error suppression in situations where the source of the error is known but not necessarily its magnitude. We show that robust optimization improves both the objective landscape of QAOA as well as overall circuit fidelity in the presence of coherent errors and errors in initial state preparation.

Source: IFAC-PapersOnLine

DOI: 10.1016/j.ifacol.2020.12.130

Authors: Yulong Dong, Xiang Meng, Lin Lin, Robert Kosut, K. Birgitta Whaley

Institutions: University of California, Berkeley, Lawrence Berkeley National Laboratory, SC Solutions (United States)

Language: English

Topic: Quantum Computing Algorithms and Architecture

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: diamond

Funder: Peking University

OpenAlex ID: W3153383589

FWCI: 1.506

Cites: 26

Cited by: 30

Related to: 10