受限因子投资组合优化中的光子量子计算与经典求解器对比
Photonic Quantum Computing vs. Classical Solvers in Constrained Factor Portfolio Optimization
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中文总结 AI 辅助
该研究对比光子量子计算、Gurobi、SAC三种优化范式在因子投资组合优化中的表现，发现光子硬件在窄范围有优势，经典混合整数规划在严格尾部风险控制场景更优，还揭示了强化学习分配器的失效模式并给出应用指南。
中文摘要 AI 辅助
作者针对机构因子投资组合构建的三种不同优化范式开展了严谨的实证评估：基于熵的光子量子退火器（Dirac-3，Quantum Computing Inc.）、商用混合整数规划求解器（Gurobi）以及无模型深度强化学习智能体（SAC）。在Jensen-Kelly-Pedersen 13因子股票库上，于164个月的测试窗口内评估这些流程，我们实施了包含48种超参数配置的全因子惩罚扫描，这些配置控制收益、波动率和偏度之间的权衡。研究结果表明，尽管光子硬件能在狭窄的运行范围内找到更优的风险-收益拓扑结构，但对于需要严格尾部风险控制和跨种子稳定性的风险受限委托任务，经典混合整数规划仍更具优势。此外，我们记录了强化学习因子分配器在无锚定高阶矩 shaping 下的结构性失效模式。我们将这些实证结果转化为量化投资组合经理部署高级优化引擎时可操作的、针对具体委托任务的指南。
英文摘要
The authors present a rigorous empirical evaluation of three distinct optimization paradigms for institutional factor portfolio construction: an entropy-based photonic quantum annealer (Dirac-3, Quantum Computing Inc.), a commercial mixed-integer programming solver (Gurobi), and a model-free deep reinforcement learning agent (SAC). Evaluating these pipelines on the Jensen-Kelly-Pedersen 13-factor equity library across 164 months test window, we implement a full factorial penalty sweep comprising 48 hyperparameter configurations that govern return, volatility, and skewness trade-offs. Our findings demonstrate that while photonic hardware can locate superior risk-return topologies within a narrow operating range, classical mixed-integer programming remains superior for risk-constrained mandates requiring tight tail-risk control and cross-seed stability. Furthermore, we document structural failure modes in reinforcement learning factor allocators under unanchored higher-moment shaping. We translate these empirical results into actionable, mandate-specific guidelines for quantitative portfolio managers deploying advanced optimization engines.
