PRICE AND SALES VOLUME FORECASTING USING QUANTUM SUPPORT VECTOR REGRESSION

This article analyzes the issue of accurately predicting the price and sales volume of retail products based on classical and quantum approaches. The classical Support Vector Regression (SVR) model was evaluated in comparison with the Quantum Support Vector Regression (QSVR) model based on quantum computing. Latent representations of the data were obtained through dimensionality reduction using an autoencoder and utilized for forecasting. The experimental results demonstrated that the QSVR model outperforms the classical SVR model in terms of MAE, MSE, and R2 indicators. The study effectively employed quantum kernel functions, data re-uploading feature maps, and the COBYLA optimization algorithm. The findings confirm the practical potential of quantum machine learning methods.

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