Exploring the topological sector optimization on quantum computers

TL;DR

This paper investigates the topological sector optimization problem, revealing topological obstructions to traditional quantum annealing methods and proposing quantum imaginary time evolution as a promising solution.

Contribution

It identifies topological barriers in optimization problems and demonstrates QITE's potential to overcome these challenges on quantum computers.

Findings

Topological frustration obstructs quantum annealing in TSO problems.

QITE explores the full Hilbert space, effectively addressing topological optimization issues.

Different quantum algorithms show varied performance on TSO problems.

Abstract

Optimization problems are the core challenge in many fields of science and engineering, yet general and effective methods are scarce for searching optimal solutions. Quantum computing has been envisioned to help solve such problems, for example, the quantum annealing (QA) method based on adiabatic evolution has been extensively explored and successfully implemented on quantum simulators such as D-wave's annealers and some Rydberg arrays. In this work, we investigate topological sector optimization (TSO) problem, which attracts particular interests in the quantum many-body physics community. We reveal that the topology induced by frustration in the spin model is an intrinsic obstruction for QA and other traditional methods to approach the ground state. We demonstrate that the optimization difficulties of TSO problem are not restricted to the gaplessness, but are also due to the topological nature which are often ignored for the analysis of optimization problems before. To solve TSO problems, we utilize quantum imaginary time evolution (QITE) with a possible realization on quantum computers, which exploits the property of quantum superposition to explore the full Hilbert space and can thus address optimization problems of topological nature. We report the performance of different quantum optimization algorithms on TSO problems and demonstrate that their capability to address optimization problems are distinct even when considering the quantum computational resources required for practical QITE implementations.