A Hybrid Quantum-Classical Framework for Financial Volatility Forecasting Based on Quantum Circuit Born Machines

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References (41)

[1]
Quantum vs. Classical Machine Learning: A Benchmark Study for Financial Prediction
2026Rehan Ahmad, Muhammad Kashif et al.
[2]
Quantum Temporal Convolutional Neural Networks for Cross-Sectional Equity Return Prediction: A Comparative Benchmark Study
2025Chi-Sheng Chen, Xinyu Zhang et al.
[3]
Hybrid Quantum-Classical Ensemble Learning for S\&P 500 Directional Prediction
2025A. Weinberg
[4]
BLS-QLSTM: a novel hybrid quantum neural network for stock index forecasting
2025Liyun Su, Dan Li et al.
[5]
HQNN-FSP: A Hybrid Classical-Quantum Neural Network for Regression-Based Financial Stock Market Prediction
2025Prashant Kumar Choudhary, Nouhaila Innan et al.
[6]
Contextual quantum neural networks for stock price prediction
2025Sharan Mourya, Hannes Leipold et al.
[7]
ADVANCING FINANCIAL PREDICTION THROUGH QUANTUM MACHINE LEARNING
2025Ananya (Ph.D. Candidate) Patel
[8]
A Hybrid Quantum-Classical Model for Stock Price Prediction Using Quantum-Enhanced Long Short-Term Memory
2024Kimleang Kea, Dongmin Kim et al.
[9]
QADQN: Quantum Attention Deep Q-Network for Financial Market Prediction
2024Siddhant Dutta, Nouhaila Innan et al.
[10]
Prediction of realized volatility and implied volatility indices using AI and machine learning: A review
2024Elias Søvik Gunnarsson, Håkon Isern et al.
[11]
From GARCH to Neural Network for Volatility Forecast
2024Pengfei Zhao, Haoren Zhu et al.
[12]
The Potential of Quantum Techniques for Stock Price Prediction
2023Naman Srivastava, Gaurang Belekar et al.
[13]
Neural Network–Based Financial Volatility Forecasting: A Systematic Review
2022Wenbo Ge, Pooia Lalbakhsh et al.
[14]
Multi-Transformer: A New Neural Network-Based Architecture for Forecasting S&P Volatility
2021Eduardo Ramos-Pérez, Pablo J. Alonso-González et al.
[15]
Encoding-dependent generalization bounds for parametrized quantum circuits
2021Matthias C. Caro, Elies Gil-Fuster et al.
[16]
Noisy intermediate-scale quantum (NISQ) algorithms
2021Kishor Bharti, Alba Cervera-Lierta et al.
[17]
Variational quantum algorithms
2020M. Cerezo, A. Arrasmith et al.
[18]
Foundations for Near-Term Quantum Natural Language Processing
2020B. Coecke, G. Felice et al.
[19]
Effect of data encoding on the expressive power of variational quantum-machine-learning models
2020M. Schuld, R. Sweke et al.
[20]
Quantum versus classical generative modelling in finance
2020Brian Coyle, Maxwell P. Henderson et al.

Showing 20 of 41 references

Founder's Pitch

"A hybrid quantum-classical framework for forecasting financial market volatility using LSTM and Quantum Circuit Born Machines."

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