Quantum-Enhanced Optimization by Warm Starts

• URL: http://arxiv.org/abs/2508.16309v1
• Date: Fri, 22 Aug 2025 11:36:19 GMT
• Title: Quantum-Enhanced Optimization by Warm Starts
• Authors: Ieva Čepaitė, Niam Vaishnav, Leo Zhou, Ashley Montanaro,
• Abstract summary: We present an approach, which we term quantum-enhanced optimization, to accelerate classical optimization algorithms by leveraging quantum samples.<n>Our method uses quantum-generated samples as warm starts to classical samplings for solving novel problems like Max-Cut and Maximum Independent Set (MIS)
• Score: 1.1666234644810893
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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