Improving the Efficiency of Payments Systems Using Quantum Computing

• 成果类型: Article
• 署名作者: McMahon, Christopher; McGillivray, Donald; Desai, Ajit; Rivadeneyra, Francisco; Lam, Jean-Paul; Lo, Thomas; Marsden, Danica; Skavysh, Vladimir
• 署名单位: Bank of Canada; University of Waterloo
• 刊物名称: MANAGEMENT SCIENCE
• ISSN/ISSBN: 0025-1909
• DOI: 10.1287/mnsc.2023.00314
• 发表日期: 2024
• 页码: 7325-7341
• 关键词: quantum algorithm; Combinatorial Optimization; NP hard; high-value payments system

摘要

High-value payment systems (HVPSs) are typically liquidity intensive because payments are settled on a gross basis. State-of-the-art solutions to this problem include algorithms that seek netting sets and allow for ad hoc reordering of submitted payments. This paper introduces a new algorithm that explores the entire space of payments reordering to improve the liquidity efficiency of these systems without significantly increasing payment delays. Finding the optimal payment order among the entire space of reorderings is, however, an NP-hard combinatorial optimization problem. We solve this problem using a hybrid quantum annealing algorithm. Despite the limitations in size and speed of today's quantum computers, our algorithm provides quantifiable liquidity savings when applied to the Canadian HVPS using a 30-day sample of transaction data. By reordering batches of 70 payments, we achieve an average of Canadian (C) $240 million in daily liquidity savings, with a settlement delay of approximately 90 seconds. For a few days in the sample, the liquidity savings exceed C$1 billion. Compared with classical computing and with current algorithms in HVPS, our quantum algorithm offers larger liquidity savings, and it offers more reliable and consistent solutions, particularly under time constraints.