基于中性原子量子计算和网格划分求解 QUBO 问题的 MWIS 方法
Grid-Partitioned MWIS Solving with Neutral Atom Quantum Computing for QUBO Problems
AI总结：
提出一种混合量子-经典框架，通过网格划分将 QUBO 问题映射为单位圆盘图上的 MWIS 问题并利用中性原子量子计算求解，在投资组合优化任务中展现出可扩展性和实用潜力。
摘要：
二次无约束二值优化（QUBO）问题在投资组合优化等实际应用中很普遍，但大规模实例面临重大计算挑战。我们提出一种混合量子-经典框架，利用中性原子量子计算解决 QUBO 问题，方法是将它们映射为单位圆盘图上的最大加权独立集（MWIS）问题。该方法采用空间网格划分将问题分解为可管理的子图，使用模拟哈密顿模拟（AHS）求解每个子图，并贪婪地合并解以近似全局最优。我们使用历史标普 500 数据在 50 项资产的投资组合优化问题上评估该框架，以经典模拟退火作为基准。结果展示了有竞争力的性能，突出了我们的方法在含噪中等规模量子（NISQ）时代的可扩展性和实际潜力。随着中性原子量子硬件的进步，我们的框架为高效解决大规模优化问题提供了有前景的路径。
英文摘要：
Quadratic Unconstrained Binary Optimization (QUBO) problems are prevalent in real-world applications, such as portfolio optimization, but pose significant computational challenges for large-scale instances. We propose a hybrid quantum-classical framework that leverages neutral atom quantum computing to address QUBO problems by mapping them to the Maximum Weighted Independent Set (MWIS) problem on unit disk graphs. Our approach employs spatial grid partitioning to decompose the problem into manageable subgraphs, solves each subgraph using Analog Hamiltonian Simulation (AHS), and merges solutions greedily to approximate the global optimum. We evaluate the framework on a 50-asset portfolio optimization problem using historical S&P 500 data, benchmarking against classical simulated annealing. Results demonstrate competitive performance, highlighting the scalability and practical potential of our method in the Noisy Intermediate-Scale Quantum (NISQ) era. As neutral atom quantum hardware advances, our framework offers a promising path toward solving large-scale optimization problems efficiently.
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