现实交易成本下动态投资组合优化的量子退火算法
Quantum Annealing for Dynamic Portfolio Optimization under Realistic Transaction Costs
AI总结：
研究现实交易与配置约束下量子和经典投资组合构建策略，将量子投资组合选择问题转化为QUBO并嵌入CQM求解，引入经典基准策略，从业务和计算性能评估效率，比较以明确量子退火作用及局限。
摘要：
本文研究并比较了在现实交易和配置约束下，用于投资组合构建的量子和经典投资策略。该研究考虑了一个多周期投资组合再平衡设置，其中资产权重受到上下限、类别级风险敞口限制和换手率限制。在量子方面，通过资产权重的有限二进制编码，将投资组合选择问题重新表述为二次无约束二进制优化（QUBO）模型，随后嵌入到一个由混合量子-经典优化工作流程求解的约束二次模型（CQM）中。在经典方面，引入了基准配置策略，为性能评估提供经济上有意义的参考。所提出的框架从两个互补的角度联合评估投资组合效率：业务绩效，通过回报、波动性以及它们之间的权衡度量夏普比率来衡量；计算性能，通过求解器结构、约束处理和硬件执行特性来评估。由此产生的比较为理解量子退火在约束投资组合优化中的实际作用提供了严格的基础，同时阐明了其相对于既定经典方法的建模优势和当前实现局限性。
英文摘要：
This paper investigates and compares quantum and classical investment strategies for portfolio construction under realistic trading and allocation constraints. The study considers a multi-period portfolio re-balancing setting in which asset weights are subject to lower and upper bounds, class-level exposure restrictions, and turnover limitations. On the quantum side, the portfolio selection problem is reformulated as a quadratic unconstrained binary optimization (QUBO) model through a finite binary encoding of asset weights, and subsequently embedded into a constrained quadratic model (CQM) solved by a hybrid quantum-classical optimization workflow. On the classical side, benchmark allocation strategies are introduced to provide an economically meaningful reference for performance assessment. The proposed framework jointly evaluates portfolio efficiency from two complementary perspectives: business performance, measured through return, volatility, and a measure of the trade-off between them, the Sharpe ratio; and computational performance, assessed through solver structure, constraint handling, and hardware execution characteristics. The resulting comparison provides a rigorous basis for understanding the practical role of quantum annealing in constrained portfolio optimization, while clarifying both its modeling advantages and its current implementation limitations relative to established classical approaches.
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