Improving the Efficiency of Payments Systems Using Quantum Computing
Bibliographic record
Abstract
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. This paper was accepted by Chung Piaw Teo, optimization. Supplemental Material: The data files are available at https://doi.org/10.1287/mnsc.2023.00314 .
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".