量子电路用于工业优化中的离线上下文猜谜，表现优于经典模型。
Variational Quantum Circuits in Offline Contextual Bandit Problems
- 用变分量子电路拟合复杂奖励函数，结合粒子群优化找最优解。
- 在噪声大、数据稀疏的场景下仍能良好泛化，准确识别最优配置。
- 适合工业优化场景，为量子机器学习落地提供实证支持。
本文研究变分量子电路（VQCs）在工业优化任务中解决离线上下文猜谜问题的应用。基于工业基准（IB）环境，评估了量子回归模型与经典模型的性能。结果表明，量子模型能有效拟合复杂奖励函数，通过粒子群优化（PSO）识别最优配置，并在噪声大、数据稀疏的数据集上表现出良好泛化能力。这些发现为在离线上下文猜谜问题中使用VQCs提供了概念验证，凸显其在工业优化中的潜力。
原文摘要 · Abstract (English)
This paper explores the application of variational quantum circuits (VQCs) for solving offline contextual bandit problems in industrial optimization tasks. Using the Industrial Benchmark (IB) environment, we evaluate the performance of quantum regression models against classical models. Our findings demonstrate that quantum models can effectively fit complex reward functions, identify optimal configurations via particle swarm optimization (PSO), and generalize well in noisy and sparse datasets. These results provide a proof of concept for utilizing VQCs in offline contextual bandit problems and highlight their potential in industrial optimization tasks.
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