Abstract:
This dissertation investigates the application of Quantum Annealing (QA) to the detection of optimal arbitrage opportunities, comparing its e cacy against classical approaches such as Simulated Annealing (SA). Arbitrage, a financial strategy exploit- ing price di↵erences of identical or similar assets across markets, presents complex optimization challenges, traditionally tackled by classical computational methods. With the advent of quantum computing, QA emerges as a promising alternative, leveraging quantum mechanical principles to explore solution spaces more e ciently.
We formulate the Arbitrage Detection Problem (ADP) as a Binary Quadratic Model (BQM), employing D-Wave Systems’ quantum processors for empirical eval- uation. The study meticulously assesses the performance of QA and SA in terms of execution time and accuracy, with the latter benchmarked against brute-force methods where feasible.
Our findings indicate that while SA excels in computational speed, QA demon- strates a significant potential in navigating complex solution spaces to identify not only the most profitable arbitrage opportunities but also near-optimal solutions. This study not only highlights the current capabilities and limitations of quantum annealing in financial optimization but also sets the stage for future explorations into the scalability and practical applications of quantum algorithms in solving NP-hard problems prevalent in finance and beyond.
