ntegrating Quantum-Inspired Principles into Financial Data Science: Optimizing Reservoir Computing Models
J Akshaya · INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT · 2024
This research explores the integration of quantum-inspired principles into financial data science with a focus on optimizing Reservoir Computing (RC) models. This approach aims to achieve a more nuanced representation of market states, improving accuracy in predicting stock price movements and capturing complex interdependencies among financial assets. Utilizing historical data from Yahoo Finance, implement and optimize RC models enhanced by Quantum State Model (QSM) and Entangled Asset Model (EAM) techniques. The results demonstrate the potential benefits and challenges of applying these quantum-inspired methods to financial data analysis. Keywords: Computational Intelligence, Financial Data Science, Quantum Computing, Quantum Machine Learning (QML), Reservoir Computing