Work

Restricted global optimization for QAOA

Year: 2024

Type: article

Abstract: The Quantum Approximate Optimization Algorithm (QAOA) has emerged as a promising variational quantum algorithm for addressing NP-hard combinatorial optimization problems. However, a significant limitation lies in optimizing its classical parameters, which is in itself an NP-hard problem. To circumvent this obstacle, initialization heuristics, enhanced problem encodings and beneficial problem scalings have been proposed. While such strategies further improve QAOA’s performance, their remaining problem is the sole utilization of local optimizers. We show that local optimization methods are inherently inadequate within the complex cost landscape of QAOA. Instead, global optimization techniques greatly improve QAOA’s performance across diverse problem instances. While global optimization generally requires high numbers of function evaluations, we demonstrate how restricted global optimizers still show better performance without requiring an exceeding amount of function evaluations.

Source: APL Quantum

DOI: 10.1063/5.0189374

Authors: Peter Gleißner, Georg Kruse, Andreas Roßkopf

Institution: Fraunhofer Institute for Integrated Systems and Device Technology

Language: English

Topic: Quantum Computing Algorithms and Architecture

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: diamond

Funder: Bundesministerium für Wirtschaft und Klimaschutz

OpenAlex ID: W4396229276

FWCI: 0.442

Cites: 43

Cited by: 3

Related to: 10