Quantum-Enhanced Optimization by Warm Starts

TL;DR

This paper introduces a quantum-enhanced optimization approach that uses quantum sampling to provide warm starts for classical algorithms, improving runtime performance on complex combinatorial problems.

Contribution

It presents novel strategies for parameter setting, qubit mapping, and error mitigation in QAOA, enabling more efficient quantum-classical hybrid optimization.

Findings

Demonstrated runtime improvements on quantum hardware

Effective quantum sampling as warm starts accelerates classical heuristics

New techniques reduce quantum gate counts and errors

Abstract

We present an approach, which we term quantum-enhanced optimization, to accelerate classical optimization algorithms by leveraging quantum sampling. Our method uses quantum-generated samples as warm starts to classical heuristics for solving challenging combinatorial problems like Max-Cut and Maximum Independent Set (MIS). To implement the method efficiently, we introduce novel parameter-setting strategies for the Quantum Approximate Optimization Algorithm (QAOA), qubit mapping and routing techniques to reduce gate counts, and error-mitigation techniques. Experimental results, including on quantum hardware, showcase runtime improvements compared with the original classical algorithms.