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Quantum Support Vector Machines applied to Dhaka Stock Exchange data

By PulseAugur Editorial · [1 sources] ·

Researchers have explored the application of Quantum Support Vector Machines (QSVM) for classifying financial data, specifically focusing on the Dhaka Stock Exchange (DSEx) Broad Index. This study, which appears to be the first of its kind for this dataset, benchmarks various quantum kernels against a classical RBF-kernel SVM. The findings propose an optimal kernel for the DSEx dataset and relate performance to the Phase Space Terrain Ruggedness Index, while also estimating resource requirements for future large-scale investigations. AI

IMPACT Explores novel quantum algorithms for financial data classification, potentially offering new analytical tools.

RANK_REASON Academic paper detailing a novel application of quantum computing to financial data analysis. [lever_c_demoted from research: ic=1 ai=0.7]

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Quantum Support Vector Machines applied to Dhaka Stock Exchange data

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Academic paper detailing a novel application of quantum computing to financial data analysis. [lever_c_demoted from research: ic=1 ai=0.7]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Seemanta Bhattacharjee, MD. Muhtasim Fuad, A. K. M. Fakhrul Hossain ·

    Classification of Financial Data Using Quantum Support Vector Machine

    arXiv:2412.10860v2 Announce Type: replace-cross Abstract: 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 d…

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