New method cuts QAOA circuit evaluations by 80% using graph neural networks

Researchers have developed a novel graph-conditioned trust-region method to reduce the number of objective evaluations required for the Quantum Approximate Optimization Algorithm (QAOA). This approach utilizes a graph neural network to predict QAOA angles, guiding a local optimizer within a defined trust region and adapting the evaluation budget based on predicted uncertainty. Experiments on MaxCut problems demonstrated a significant reduction in circuit evaluations, from hundreds to around 45, while maintaining comparable solution quality. AI

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IMPACT Introduces a query-efficient optimization technique for quantum algorithms, potentially reducing computational costs in specific quantum computing applications.

RANK_REASON This is a research paper detailing a new method for optimizing quantum algorithms.

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• QAOA
• MaxCut
• Erdos-Renyi
• Barabasi-Albert
• Watts-Strogatz

• paper
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