Quantum Enabled Solutions Achieve Real-Time Currency Arbitrage

The pursuit of profitable currency arbitrage, exploiting fleeting price differences in global exchange rates, demands exceptionally fast computation, a challenge for even the most powerful conventional computers. Suman Kumar Roy, Rahul Rana, and M Girish Chandra, all from Tata Consultancy Services, along with their colleagues, investigate how quantum computing techniques might overcome these limitations. They present an improved mathematical model for identifying arbitrage opportunities, incorporating constraints that ensure the validity of trading cycles and eliminate inefficient calculations. The team then benchmarks several solution methods, including quantum annealing and gate-based approaches, against established classical algorithms, revealing both the potential and current constraints of applying quantum technologies to real-time financial markets and offering valuable insight into the feasibility of quantum-enabled financial solutions.

Currency Arbitrage Solved with Quantum Algorithms

This research investigates the application of quantum and hybrid quantum-classical algorithms to solve currency arbitrage, a process that exploits price discrepancies across different exchange markets to generate risk-free profit. The goal is to find profitable currency exchange cycles, and the research focuses on ensuring the validity of these trading cycles, critical in fast-moving financial markets. Scientists developed a mathematical model for currency arbitrage, incorporating constraints to guarantee valid trading cycles and eliminate infeasible solutions. The team compared Quantum Annealing, a quantum computing approach for optimization, and a hybrid quantum-classical algorithm called Variational Quantum Eigensolver with a custom circuit design, ACE-LS, against classical metaheuristic optimization algorithms, Tabu Search, and the state-of-the-art solver, Gurobi.

The problem was formulated for quantum annealers, and the ACE-LS algorithm involved designing a specific quantum circuit tailored to the problem, trained using classical optimizers. Results demonstrate that Quantum Annealing and ACE-LS generally outperformed Gurobi and Tabu Search in terms of execution time, especially as the problem size increased. These quantum and hybrid approaches scaled linearly with problem size, while Gurobi’s execution time grew exponentially. The solution quality, measured by profit, was comparable across all methods, particularly for smaller problems. The ACE-LS algorithm, using a specific circuit design and the Differential Evolution classical optimizer, yielded the best results, suggesting viability for real-time currency arbitrage applications.

The researchers plan to execute the ACE-LS algorithm on actual quantum hardware to validate its performance in a practical setting. Further research will focus on developing more sophisticated hybrid quantum-classical algorithms to reduce execution times and improve solution quality. They also intend to explore implementing the Tabu Search algorithm in the quantum domain and investigate neutral atom-based quantum computers, which offer scalability and long coherence times, for solving this problem. Recognizing the need for ultra-low latency infrastructure, they also plan to investigate using Field Programmable Gate Arrays to achieve true real-time performance. This research demonstrates the potential of quantum and hybrid quantum-classical algorithms for solving a practical financial optimization problem.

Simple Cycle Constraints Improve Currency Arbitrage Models

Scientists have developed an enhanced mathematical model for currency arbitrage and tested its performance using both quantum and classical computing techniques. The work focuses on identifying profitable trading cycles and introduces simple-cycle preservation constraints to guarantee the validity of identified arbitrage opportunities. This addition ensures that solutions represent feasible trading cycles, eliminating redundant or invalid results. The team implemented and evaluated this model using quantum annealing, a gate-based quantum approach called Adaptive Cost Encoding, or ACE, and classical solvers including Gurobi and Tabu Search.

To further refine the results obtained from ACE, they introduced a classical multi-bit swap post-processing technique, a local search method designed to improve solution quality. Experiments using real-world currency exchange data demonstrate the performance of each method in terms of both arbitrage profit and execution time, the two key metrics for evaluating success. Results show that the enhanced model, combined with these various solving techniques, delivers measurable arbitrage profit. The team meticulously benchmarked each solver, revealing critical insights into the trade-offs between solution quality and computation time, emphasizing execution latency as a crucial factor for practical, time-sensitive financial applications.

Quantum Advantage for Real-Time Currency Arbitrage

This research demonstrates the effectiveness of quantum and hybrid quantum-classical methods for solving real-time currency arbitrage problems, leveraging price discrepancies across fourteen currency pairs. The team developed an enhanced mathematical model incorporating cycle preservation constraints, which ensures the validity of trading cycles and eliminates infeasible solutions, creating a robust optimization framework. Comparative evaluations of Quantum Annealing, Adaptive Cost Encoding, Gurobi, and Tabu Search reveal that Quantum Annealing and Adaptive Cost Encoding outperform classical methods in terms of execution time, scaling linearly with problem size, unlike Gurobi’s exponential growth. The solution quality achieved by Quantum Annealing and Adaptive Cost Encoding is comparable to that of the classical benchmarks, affirming the potential of quantum approaches for this application.

While real-time currency arbitrage demands execution within microseconds to milliseconds, achievable with high-performance computing systems, these results represent an initial step towards a practical, industry-level quantum solution. The authors acknowledge that further research is needed to validate the performance of Adaptive Cost Encoding on actual quantum hardware and to explore advanced hybrid algorithms that reduce execution times and improve solution quality. Future work will also investigate the application of these techniques to neutral atom-based quantum computers, leveraging their scalability and long coherence times to enhance computational power.