CryptoQNet: Leveraging Quantum Computing for Enhanced Cryptocurrency Market Analysis

Abstract

Cryptocurrency markets are characterized by high volatility and nonlinear dynamics, making accurate analysis and forecasting challenging. This study proposes a novel quantum computing-based model, CryptoQNet, to address these challenges by leveraging Quantum Feature Maps, Variational Quantum Circuits, and Quantum Recurrent Neural Networks. CryptoQNet outperforms classical models such as LSTMs and GRUs, achieving a Mean Absolute Error (MAE) of 0.021 and Root Mean Squared Error (RMSE) of 0.028 for Bitcoin price predictions, compared to 0.038 and 0.045 by LSTMs. For Ethereum, the model achieved an MAE of 0.018 and RMSE of 0.025, demonstrating its robustness in modeling complex market trends. Additionally, CryptoQNet significantly reduced Mean Absolute Percentage Error (MAPE) for volatility prediction, achieving 3.2% for Bitcoin and 3.6% for Ethereum, compared to over 5% by classical models. The model also provides enhanced interpretability, identifying price and volume as key factors influencing market trends. While training time is higher due to quantum computations, inference efficiency and accuracy make CryptoQNet a promising tool for financial forecasting. This study highlights the transformative potential of quantum computing in financial market analysis and offers directions for future research in hybrid quantum-classical approaches.