Toward Quantum Enabled Solutions for Real-Time Currency Arbitrage in Financial Markets

TL;DR

This work formulates an enhanced currency arbitrage optimization problem with simple-cycle preservation using a node-based binary quadratic programming model and a $K$-long cycle, ensuring feasible, non-redundant arbitrage cycles. It translates the problem into a QUBO and benchmarks quantum annealing (QA) and gate-based ACE against classical baselines like Gurobi and Tabu Search on real-world 14-currency data, highlighting execution time and profit performance. ACE leverages a qubit-efficient encoding and a classical local-search post-processing to boost solution quality, while QA demonstrates competitive speed and scalability. The findings indicate that quantum and hybrid approaches can substantially reduce latency in real-time arbitrage contexts, though practical deployment will require hardware validation and further hybridization, especially for ultra-low-latency industry use.

Abstract

Currency arbitrage leverages price discrepancies in currency exchange rates across different currency pairs to gain risk-free profits. It involves multiple trading, where short-lived price discrepancies require real-time, high-speed processing of vast solution space, posing challenges for classical computing. In this work, we formulate an enhanced mathematical model for the currency arbitrage problem by adding simple cycle preservation constraints, which guarantee trading cycle validity and eliminate redundant or infeasible substructures. To solve this model, we use and benchmark various solvers, including Quantum Annealing (QA), gate-based quantum approaches such as Variational Quantum Algorithm with Adaptive Cost Encoding (ACE), as well as classical solvers such as Gurobi and classical meta heuristics such as Tabu Search (TS). We propose a classical multi-bit swap post-processing to improve the solution generated by ACE. Using real-world currency exchange data, we compare these methods in terms of both arbitrage profit and execution time, the two key performance metrics. Our results give insight into the current capabilities and limitations of quantum methods for real-time financial use cases.

Figures (5)

• Figure 1: ACE-LS Workflow for Currency Arbitrage
• Figure 2: Different Ansatz used in ACE-LS
• Figure 3: Experiments with different circuit, layers and optimizers in ACE-LS approach. Circuit 2 with two layers, and by using DE optimizer, it gives lowest cost for all problem sizes.
• Figure 4: Execution Time for Different Solvers
• Figure 5: Optimal Arbitrage Cycle for Problem Size (14, 5)