论文标题
比较经典的量词投资组合优化与增强的约束
Comparing Classical-Quantum Portfolio Optimization with Enhanced Constraints
论文作者
论文摘要
经常提到的量子优势候选人的问题之一是选择金融资产投资组合以最大程度地提高回报，同时最大程度地降低风险。在本文中，我们为在量子退火器（QA）中使用了几种现实世界中的约束，并扩展了可以实现算法的方案。具体而言，我们展示了如何将基本分析添加到投资组合优化问题，并根据所选资产负债表指标添加资产特定和全局约束。我们还扩展了以前的工作，以改善限制领域的投资乐队，并限制投资的资产数量，从而为质量保证提供了强大而灵活的解决方案。 重要的是，我们分析了使用D-Wave的量子处理器来解决此类问题的当前最新算法，并比较获得的解决方案的质量与商业上可用的优化软件。我们探索了各种传统和新约束，这些限制使问题在计算上更难以解决，并表明，即使有了这些其他约束，经典算法也超过了静态投资组合优化模型中的当前混合解决方案。
One of the problems frequently mentioned as a candidate for quantum advantage is that of selecting a portfolio of financial assets to maximize returns while minimizing risk. In this paper we formulate several real-world constraints for use in a Quantum Annealer (QA), extending the scenarios in which the algorithm can be implemented. Specifically, we show how to add fundamental analysis to the portfolio optimization problem, adding in asset-specific and global constraints based on chosen balance sheet metrics. We also expand on previous work in improving the constraint to enforce investment bands in sectors and limiting the number of assets to invest in, creating a robust and flexible solution amenable to QA. Importantly, we analyze the current state-of-the-art algorithms for solving such a problem using D-Wave's Quantum Processor and compare the quality of the solutions obtained to commercially-available optimization software. We explore a variety of traditional and new constraints that make the problem computationally harder to solve and show that even with these additional constraints, classical algorithms outperform current hybrid solutions in the static portfolio optimization model.
