Seemanta Bhattacharjee

arXiv2024

Classification of Financial Data Using Quantum Support Vector Machine

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 first systematic study of quantum kernels applied to this dataset. Working within the empirical quantum advantage (EQA) framework of Krunic et al., we benchmark several quantum kernels against a classical RBF-kernel SVM baseline, propose the best-performing kernel for this dataset, and relate the observations to the Phase Space Terrain Ruggedness Index metric. We estimate the resources needed to carry out these investigations on a larger scale for future practitioners.

quant-ph↗

arXiv2024

Empirical Quantum Advantage Analysis of Quantum Kernel in Gene Expression Data

The incorporation of quantum ansatz with machine learning classification models demonstrates the ability to extract patterns from data for classification tasks. However, taking advantage of the enhanced computational power of quantum machine learning necessitates dealing with various constraints. In this paper, we focus on constraints like finding suitable datasets where quantum advantage is achievable and evaluating the relevance of features chosen by classical and quantum methods. Additionally, we compare quantum and classical approaches using benchmarks and estimate the computational complexity of quantum circuits to assess real-world usability. For our experimental validation, we selected the gene expression dataset, given the critical role of genetic variations in regulating physiological behavior and disease susceptibility. Through this study, we aim to contribute to the advancement of quantum machine learning methodologies, offering valuable insights into their potential for addressing complex classification challenges in various domains.

quant-ph↗