AI 中文总结
提出qReduMIS混合量子-经典算法，将投资组合多样化转化为最大独立集问题，在Quantinuum离子阱系统上对最多225个资产进行实验，成功概率和近似比均优于独立QAOA。
AI 中文摘要
投资组合多样化——现代投资管理的基石——可以表述为资产相关图上的最大独立集（MIS）问题。大规模求解该问题在计算上具有挑战性，这促使人们探索用于实际金融优化的量子算法。我们提出了一种端到端流水线，利用qReduMIS，一种递归的混合量子-经典算法。qReduMIS并非直接使用量子优化产生最终解，而是利用量子近似优化算法（QAOA）的独立集测量来识别冻结节点——即可能属于最优解的顶点——从而指导并解除后续（可证明最优的）经典约简对剩余图的阻塞。我们在来自四个主要市场指数的多达225个资产的真实金融数据上对qReduMIS进行了基准测试，实验在Quantinuum的98量子比特离子阱Helios系统上执行，QAOA电路作用于最多78个量子比特和1016个双量子比特门的核上。虽然独立的QAOA未能为两个最大指数（S&P 100和Nikkei 225）找到最优解，但qReduMIS的成功概率分别达到了0.40和0.95，所有四个指数的平均近似比≥0.96。我们在Quantinuum H2-1噪声模拟器上对73个不同规模的资产相关图进行了系统基准测试，结果表明，对于p=2层QAOA，qReduMIS的最优时间-解缩放指数比独立QAOA小3.2倍。
英文摘要
Portfolio diversification - a cornerstone of modern investment management - can be formulated as a Maximum Independent Set (MIS) problem on asset correlation graphs. Solving this problem at scale is computationally challenging, motivating the exploration of quantum algorithms for practical financial optimization. We propose an end-to-end pipeline leveraging qReduMIS, a recursive hybrid quantum-classical algorithm. Rather than using quantum optimization to directly produce a final solution, qReduMIS leverages independent set measurements from the Quantum Approximate Optimization Algorithm (QAOA) to identify frozen nodes - vertices likely to belong to optimal solutions - thereby guiding and unblocking subsequent (provably optimal) classical reductions on the remaining graph. We benchmark qReduMIS on real financial data from four major market indices with up to 225 assets, executing experiments on Quantinuum's 98-qubit trapped-ion Helios system, with QAOA circuits acting on kernels of up to 78 qubits and 1016 two-qubit gates. While standalone QAOA fails to find the optimal solution for two of the largest indices (S&P 100 and Nikkei 225), qReduMIS achieves success probabilities of $0.40$ and $0.95$, respectively, with average approximation ratios $\geq 0.96$ across all four indices. We perform a systematic benchmark on the Quantinuum H2-1 noisy emulator over 73 asset correlation graphs of varying size showing that, for $p=2$ QAOA layers, the optimal time-to-solution scaling exponent of qReduMIS is $3.2$ times smaller than that of standalone QAOA.
