arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 2602.23976quant-ph
收藏

在囚离子量子计算机上进行大规模投资组合优化

Large-scale portfolio optimization on a trapped-ion quantum computer

Kipu Quantum GmbH(基普量子有限公司);Department of Physical Chemistry, University of the Basque Country EHU(巴斯克大学物理化学系);TUM School of Management, Technische Universität München(慕尼黑工业大学管理学院);Instituto de Ciencia de Materiales de Madrid (CSIC)(马德里材料科学研究所(西班牙国家研究委员会));Departamento de Física Teórica de la Materia Condensada, Universidad Autónoma de Madrid(马德里自治大学凝聚态物质理论物理系);IDAL, Electronic Engineering Department, ETSE-UV, University of Valencia(瓦伦西亚大学电子工程学院IDAL);IonQ Inc.(IonQ公司);IonQ Italia(IonQ意大利分公司)
Alejandro Gomez Cadavid, Ananth Kaushik, Pranav Chandarana, Miguel Angel Lopez-Ruiz, Gaurav Dev, Willie Aboumrad, Qi Zhang, Claudio Girotto, Sebastián V. Romero… 展开作者

Alejandro Gomez Cadavid, Ananth Kaushik, Pranav Chandarana, Miguel Angel Lopez-Ruiz, Gaurav Dev, Willie Aboumrad, Qi Zhang, Claudio Girotto, Sebastián V. Romero, Martin Roetteler, Enrique Solano, Marco Pistoia, Narendra N. Hegade

AI总结:

本研究在囚离子量子计算机上实现了一种端到端的大规模投资组合优化流程,利用硬件感知分解和非变分量子优化方法,提高了投资组合的收益与风险权衡。

摘要:

我们提出了一条端到端的流程,用于具有基数约束的大规模投资组合选择,并在囚离子量子处理器上通过硬件感知的分解实验性地展示了它。基于RMT基于的相关矩阵去噪和社区检测,我们识别出相关资产组,并引入了一种基于相关性的贪心分割方案,该方案通过可执行量子位预算限制每个集群。每个集群定义了一个硬件可嵌入的QUBO子问题,我们使用偏场数字化反向量子优化(BF-DCQO)来解决,这是一种非变分方法,可以避免经典参数训练循环。我们重新组合低能候选者进入全球投资组合,并通过两阶段后处理流程来确保可行性:快速修复后接一个保持基数的交换局部搜索。我们对一个来自S&P 500的250资产宇宙进行了基准测试,并在64量子位的钡开发系统上执行子问题,该系统类似于即将推出的IonQ Tempo系列。我们发现,更大的可执行子问题规模会减少分解误差,并系统性地提高最终目标值和风险回报权衡,与在相同后处理下随机基线相比。总体而言,结果建立了一条经过硬件测试的路径,用于扩展金融优化问题,该路径由一个贸易空间定义,其中可执行问题规模和电路成本与最终的解决方案质量相平衡。

英文摘要:

We present an end-to-end pipeline for large-scale portfolio selection with cardinality constraints and experimentally demonstrate it on trapped-ion quantum processors using hardware-aware decomposition. Building on RMT-based correlation-matrix denoising and community detection, we identify correlated asset groups and introduce a correlation-guided greedy splitting scheme that caps each cluster by the executable qubit budget. Each cluster defines a hardware-embeddable QUBO subproblem that we solve using bias-field digitized counterdiabatic quantum optimization (BF-DCQO), a non-variational method that avoids classical parameter-training loops. We recombine low-energy candidates into global portfolios and enforce feasibility with a two-stage post-processing routine: fast repair followed by a cardinality-preserving swap local search. We benchmark the workflow on a 250-asset universe taken from the S&P 500 and execute subproblems on a 64-qubit Barium development system similar to the forthcoming IonQ Tempo line. We observe that larger executable subproblem sizes reduce decomposition error and systematically improve final objective values and risk-return trade-offs relative to randomized baselines under identical post-processing. Overall, the results establish a hardware-tested route for scaling financial optimization problems, defined by a trade space in which executable problem size and circuit cost are balanced against the resulting solution quality.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

↑