用量子神经网络优化量子算法，加速求解复杂组合问题。
Learning to Learn with Quantum Optimization via Quantum Neural Networks
- 用量子LSTM网络作为量子优化器，学习高效调参策略。
- 在最大割和SK模型上收敛更快，近似比高于经典方法。
- 适合研究量子算法优化与实用化部署的科研人员。
量子近似优化算法（QAOA）通过浅层量子电路有望高效求解经典难以处理的组合优化问题。然而，其性能和可扩展性常依赖于参数优化效果，而陡峭的能量景观和硬件噪声使这一过程极具挑战。本文提出一种结合量子长短期记忆（QLSTM）架构与QAOA的量子元学习框架。通过在较小图实例上训练QLSTM优化器，该方法可快速泛化至更大、更复杂的任务，显著减少收敛所需迭代次数。在最大割和Sherrington-Kirkpatrick模型实例上的全面基准测试表明，基于QLSTM的优化器收敛速度更快，且获得更高近似比，为当前含噪声中等规模量子（NISQ）时代提供了可靠的可扩展量子优化路径。
原文摘要 · Abstract (English)
Quantum Approximate Optimization Algorithms (QAOA) promise efficient solutions to classically intractable combinatorial optimization problems by harnessing shallow-depth quantum circuits. Yet, their performance and scalability often hinge on effective parameter optimization, which remains nontrivial due to rugged energy landscapes and hardware noise. In this work, we introduce a quantum meta-learning framework that combines quantum neural networks, specifically Quantum Long Short-Term Memory (QLSTM) architectures, with QAOA. By training the QLSTM optimizer on smaller graph instances, our approach rapidly generalizes to larger, more complex problems, substantially reducing the number of iterations required for convergence. Through comprehensive benchmarks on Max-Cut and Sherrington-Kirkpatrick model instances, we demonstrate that QLSTM-based optimizers converge faster and achieve higher approximation ratios compared to classical baselines, thereby offering a robust pathway toward scalable quantum optimization in the NISQ era.
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