Classification of Financial Data Using Quantum Support Vector Machine
This work addresses classification challenges for financial analysts using quantum computing, but it is incremental as it applies existing quantum methods to a new dataset.
The authors tackled the problem of classifying financial data by applying quantum support vector machines to a self-curated Dhaka Stock Exchange Broad Index dataset, reporting empirical quantum advantage with specific quantum kernels and verifying a metric for future scalability.
Quantum Support Vector Machine is a kernel-based approach to classification problems. We study the applicability of quantum kernels to financial data, specifically our self-curated Dhaka Stock Exchange (DSEx) Broad Index dataset. To the best of our knowledge, this is the very first systematic research work on this dataset on the application of quantum kernel. We report empirical quantum advantage in our work, using several quantum kernels and proposing the best one for this dataset while verifying the Phase Space Terrain Ruggedness Index metric. We estimate the resources needed to carry out these investigations on a larger scale for future practitioners.