用量子退火初始化仓储优化，提升传统算法效率。
Exploring Utility in a Real-World Warehouse Optimization Problem: Formulation Based on Quantum Annealers and Preliminary Results
- 引入量子退火初始化机制，融合量子与经典计算
- 两阶段实验显示相较纯经典方法有初步性能提升
- 适合关注量子计算落地工业优化的从业者
在当前的NISQ时代，研究人员和实践者面临的核心挑战之一是如何以最高效、最具创新性的方式结合量子与经典计算。本文提出一种名为量子初始化（Quantum Initialization）的机制，利用D-Wave量子退火机解决真实世界中的仓储优化问题。该模块专为嵌入现有经典优化软件而设计，可直接用于工业级实际问题求解。我们通过两阶段实验，将该机制与经典版本软件进行对比，获得初步结果，验证了其在实际应用中的可行性与潜在优势。
原文摘要 · Abstract (English)
In the current NISQ-era, one of the major challenges faced by researchers and practitioners lies in figuring out how to combine quantum and classical computing in the most efficient and innovative way. In this paper, we present a mechanism coined as Quantum Initialization for Warehouse Optimization Problem that resorts to D-Wave's Quantum Annealer. The module has been specifically designed to be embedded into already existing classical software dedicated to the optimization of a real-world industrial problem. We preliminary tested the implemented mechanism through a two-phase experiment against the classical version of the software.
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