发表机构
Tata Consultency Services Limited（塔塔咨询服务有限公司）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究在IBM量子硬件上实现了货币颜色问题的概念验证，通过QUBO转换和变分量子算法，成功复现了经典最优解，验证了量子优化在资金管理中的可行性。
AI 中文摘要
货币颜色（CoM）问题是一个金融优化挑战，企业必须确定如何在多个账户、货币和时间段之间分配和路由资金，同时满足监管、运营和流动性约束。随着资产、债务和规划期数量的增加，决策空间迅速增长，使得大规模优化对经典方法而言日益具有挑战性。在这项工作中，我们提出了一个使用IBM量子硬件的CoM问题的概念验证量子实现。原始优化问题被表述为受约束的混合整数线性规划（MILP）问题，并转换为适合量子执行的二次无约束二元优化（QUBO）表示。我们采用带有泡利相关编码（PCE）的变分量子算法（VQA），这是一种量子比特高效编码技术，并应用误差缓解技术以提高嘈杂量子硬件上的解保真度。一个由3个资产、3个债务和2个时间段组成的原型实例在真实的IBM量子硬件上执行，并与经典精确求解器SCIP进行了基准比较。量子方法成功复现了经典获得的最优解，验证了所提出方法的正确性和可行性。虽然当前嘈杂的量子硬件在评估的小实例上并未超越经典优化，但结果表明在当前量子硬件上执行资金优化工作负载的可行性，并为未来研究提供了基础。
英文摘要
The Color-of-Money (CoM) problem is a financial optimization challenge in which enterprises must determine how to allocate and route funds across multiple accounts, currencies, and time periods while satisfying regulatory, operational, and liquidity constraints. As the number of assets, obligations, and planning periods increases, the decision space grows rapidly, making large-scale optimization increasingly challenging for classical approaches. In this work, we present a proof-of-concept quantum implementation of the CoM problem using IBM Quantum hardware. The original optimization problem is formulated as a constrained Mixed-Integer Linear Programming (MILP) problem and transformed into a Quadratic Unconstrained Binary Optimization (QUBO) representation suitable for quantum execution. We employ a Variational Quantum Algorithm (VQA) with Pauli Correlation Encoding (PCE), a qubit efficient encoding technique and apply error-mitigation technique to improve solution fidelity on noisy quantum hardware. A prototype instance consisting of 3 assets, 3 obligations, and 2 time periods was executed on real IBM quantum hardware and benchmarked against classical exact solver SCIP. The quantum approach successfully reproduced the same optimal solution obtained classically, validating the correctness and feasibility of the proposed methodology. While current noisy quantum hardware does not outperform classical optimization on the evaluated small instance, the results demonstrate the feasibility of executing treasury optimization workloads on current quantum hardware and provide a foundation for future investigations.
