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International Conference on Emerging Smart Computing and Informatics (ESCI)

CryptoQNet: Leveraging Quantum Computing for Enhanced Cryptocurrency Market Analysis

Rahat Pervez, Afsana Alam Nova, Mir Nafiul Nagib, Sheikh Razia Sultana, Jannatul Maua, Md Nahid Hasan

Published Mar 5, 2025• 2 citations
Publisher

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.

DOI
10.1109/esci63694.2025.10988262

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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.
Cryptocurrency markets are known for their high volatility and complex price dynamics, which make accurate prediction and analysis extremely challenging. Traditional financial forecasting models and classical machine learning algorithms often struggle to capture the nonlinear and rapidly changing patterns present in cryptocurrency datasets. In recent years, advancements in artificial intelligence and quantum computing have opened new possibilities for analyzing complex financial data and improving prediction accuracy.This study proposes a quantum computing–based framework for cryptocurrency market prediction by integrating quantum machine learning techniques with financial time-series analysis. The proposed model utilizes quantum computing concepts such as quantum feature mapping, variational quantum circuits, and quantum recurrent neural networks to analyze cryptocurrency market data. Historical datasets containing information about cryptocurrency prices, trading volume, and market capitalization are used to train and evaluate the model.The proposed system aims to identify hidden patterns in cryptocurrency market trends and generate accurate predictions for future price movements and market volatility. The performance of the quantum-based model is compared with classical deep learning models such as Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) networks. Experimental results indicate that the quantum machine learning approach achieves improved prediction accuracy and lower forecasting error compared to traditional deep learning models.By leveraging the computational advantages of quantum computing, the proposed framework provides a powerful approach for analyzing highly complex financial datasets. The results demonstrate that quantum machine learning techniques have the potential to significantly enhance cryptocurrency market analysis, enabling more accurate forecasting and better decision-making for investors and financial analysts.
Cryptocurrency price prediction remains a challenging problem due to the highly volatile nature of digital asset markets. This study introduces a Hybrid Quantum-Classical Neural Network (HQCNN) for cryptocurrency price forecasting, leveraging quantum feature encoding and extraction with deep learning architectures. The proposed model utilizes Quantum Convolutional Neural Networks (QCNN) to enhance feature representations, which are then processed by classical deep learning models such as Long Short-Term Memory (LSTM) and Transformer networks. Experimental results on historical cryptocurrency data demonstrate that HQCNN outperforms state-of-the-art models. The proposed model achieves a mean absolute error (MAE) of 0.023 and a root mean squared error (RMSE) of 0.036, surpassing traditional deep learning methods such as LSTM (MAE: 0.040, RMSE: 0.056) and Transformerbased models (MAE: 0.035, RMSE: 0.050). Ablation studies confirm the importance of quantum feature encoding, as removing it degrades performance by up to 38.2%\mathbf{3 8. 2 \%}38.2%. Additionally, HQCNN reduces inference time to 7 milliseconds, making it suitable for real-time trading applications. Despite its advantages, HQCNN faces challenges related to quantum hardware constraints, training overhead, and interpretability in financial decision-making. Future work will focus on deploying the model on real quantum processors, integrating reinforcement learning for market adaptability, and enhancing interpretability with quantum explainability techniques.
Financial markets undergo rapid oscillations driven by several volatile causes, necessitating expedited and precise models for risk evaluation and market trend forecasting. Classical models encounter computational constraints when examining high-dimensional, nonlinear datasets. This study seeks to use Quantum Machine Learning (QML) methodologies to expedite risk evaluation and improve volatility forecasting for various financial instruments in real-time settings. The unique contribution of this work is the integration of quantum machine learning with financial risk assessment, leveraging quantum parallelism and kernelized feature spaces to improve the accuracy of market volatility prediction. This framework not only enhances computational efficiency compared to classical models but also provides a scalable foundation for real-time financial decision-making. The system employs Kaggle-style datasets including CME futures, Microsoft (MSFT), Amazon (AMZN), HRB, and historical data from the Dow Jones (DJI). Essential metrics including Open, Close, High, Low, Volume, Volatility Index, and Price Returns were retrieved. A hybrid pipeline combining conventional preprocessing with quantum-enhanced classifiers, including variational quantum circuits, was developed using the Qiskit and Pennylane frameworks. The QML-based model had a prediction accuracy of 95.2% in risk categorization and market direction forecasting, exceeding traditional standards. Quantum models provide significant benefits in velocity and scalability for financial analytics, enabling real-time risk assessment and strategic investment analysis.
Financial risk management faces the essential problem of forecasting stock market volatility because traditional econometric models including GARCH fail to demonstrate the complex market behavior which characterizes emerging markets. The research investigates how quantum computing functions as an innovative forecasting method through its assessment of actual volatility patterns observed in four primary Indian stock market indexes between 2015 and 2025. Researchers tested two hybrid quantum-classical systems which included a Variational Quantum Regressor (VQR) and a Quantum Kernel Support Vector Regression (QK-SVR) against traditional GARCH family models and Long Short-Term Memory (LSTM) networks. The results demonstrate that quantum-assisted models achieve better performance than both traditional methods and deep learning benchmarks throughout all market scenarios while maintaining stability during financial emergencies. The VQR model which included India VIX data achieved maximum performance because it decreased Root Mean Square Error (RMSE) by 90% when compared to GARCH and by 65% to 76% when measured against LSTM. The current Noisy Intermediate-Scale Quantum (NISQ) era allows hybrid quantum models to function as efficient and accurate forecasting instruments for emerging market predictions.
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Accurate financial volatility forecasting is crucial but challenged by the non-linear, highly correlated nature of market data. Recently, quantum computing has emerged as a promising paradigm for solving complex high-dimensional sampling problems. To harness this, we propose a novel hybrid framework combining the temporal representation power of classical neural networks with the distribution-learning capabilities of quantum models. Specifically, we integrate a Long Short-Term Memory (LSTM) network with a Quantum Circuit Born Machine (QCBM). The LSTM extracts dynamic features, while the QCBM acts as a learnable generative prior modeling complex market distributions to guide forecasting. Evaluated on 5-minute high-frequency data from the SSE Composite and CSI 300 indices, our model significantly outperforms a classical LSTM baseline across MSE, RMSE, and QLIKE metrics. Furthermore, by introducing a stochastic ``Drop-Prior" mechanism during training, the LSTM implicitly distills structured information from the quantum prior. This establishes a pragmatic paradigm of ``quantum-assisted training with classical-efficient inference", whereby the model retains its quantum-enhanced accuracy even when the quantum module is entirely disabled during deployment. This demonstrates a practical pathway for leveraging quantum computing to enhance classical models without real-time quantum inference latency.