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A Hybrid Quantum-Classical Model for Stock Price Prediction Using Quantum-Enhanced Long Short-Term Memory.

Kimleang Kea¹, Dongmin Kim¹, Chansreynich Huot¹

¹Department of AI Convergence, Pukyong National University, Nam-gu, Busan 48513, Republic of Korea.

Entropy (Basel, Switzerland)

|November 27, 2024

Summary

This summary is machine-generated.

This study introduces QLSTM, a hybrid quantum-classical machine learning model for stock price prediction. QLSTM significantly outperforms classical models, demonstrating improved accuracy and reduced error in financial market forecasting.

Keywords:

AI in finance long short-term memory quantum machine learning stock price prediction time-series analysis

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Area of Science:

Quantum Computing
Machine Learning
Financial Markets

Background:

Stock market prediction is a complex challenge in machine learning (ML).
Classical ML models for prediction are computationally intensive.
Quantum computing (QC) offers potential for exponential speedups over classical computers.

Purpose of the Study:

To develop and evaluate a hybrid quantum-classical ML model for stock price prediction.
To introduce a novel model, Quantum Long Short-Term Memory (QLSTM), by integrating classical Long Short-Term Memory (LSTM) with QC.
To compare QLSTM's performance against classical ML models.

Main Methods:

Developed a hybrid quantum-classical ML model (QLSTM).
Validated QLSTM using an IBM quantum simulator and a real IBM quantum computer.
Evaluated performance using Root Mean Square Error (RMSE) and prediction accuracy.
Conducted comparative analysis against classical models and explored hyperparameter impact.

Main Results:

QLSTM achieved a lower RMSE (0.0602) compared to classical LSTM (0.0693).
QLSTM demonstrated higher prediction accuracy (0.9736) than classical LSTM (0.8815).
QLSTM outperformed other classical models in both RMSE and accuracy metrics.

Conclusions:

The hybrid QLSTM model shows superior performance for stock price prediction.
Integrating QC with classical ML offers significant advantages in financial forecasting.
QLSTM represents a promising advancement in applying quantum computing to financial markets.