Improving the Efficiency of Payments Systems Using Quantum Computing

TL;DR

This paper presents a quantum computing-based algorithm that optimizes payment orderings in high-value payment systems, significantly reducing liquidity needs with minimal delays, demonstrated on Canadian transaction data.

Contribution

It introduces a quantum annealing algorithm for payment ordering that improves liquidity efficiency in high-value payment systems, a novel approach in this context.

Findings

Achieved an average of C$240 million daily liquidity savings

Liquidity savings exceeded C$1 billion on some days

Settlement delay was approximately 90 seconds

Abstract

High-value payment systems (HVPSs) are typically liquidity-intensive as the payment requests are indivisible and settled on a gross basis. Finding the right order in which payments should be processed to maximize the liquidity efficiency of these systems is an $NP$-hard combinatorial optimization problem, which quantum algorithms may be able to tackle at meaningful scales. We developed an algorithm and ran it on a hybrid quantum annealing solver to find an ordering of payments that reduced the amount of system liquidity necessary without substantially increasing payment delays. Despite the limitations in size and speed of today's quantum computers, our algorithm provided quantifiable efficiency improvements when applied to the Canadian HVPS using a 30-day sample of transaction data. By reordering each batch of 70 payments as they entered the queue, we achieved an average of 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 exceeded C\$1 billion. This algorithm could be incorporated as a centralized preprocessor into existing HVPS without entailing a fundamental change to their risk management models.