Quantum Machine Learning For Business Forecasting And Risk Assessment

Authors: Dr. Pankaj Malik, Daksh Sethi, Akshat Sharma, Devansh Ramchandani, Harshit Soni

Abstract: Business forecasting and risk assessment are critical components of modern enterprise decision-making in finance, retail, and supply-chain management. Classical machine learning models such as LSTM, XGBoost, and SVM have delivered significant improvements in predictive accuracy but face limitations in modeling complex nonlinear patterns, especially under small datasets and high-dimensional feature interactions. Quantum Machine Learning (QML), leveraging quantum feature embeddings and variational quantum circuits (VQCs), offers a promising alternative with enhanced expressivity and improved generalization properties. This study proposes a hybrid quantum–classical framework integrating a VQC-based quantum feature encoder with classical regression and classification layers. The model is evaluated across three business tasks: financial time-series forecasting, retail demand prediction, and credit-risk classification. Experimental results demonstrate that the proposed QML approach achieves notable improvements in specific conditions. For forecasting tasks, the hybrid QML model yields 8.7% lower RMSE compared to LSTM and 12.4% lower RMSE than XGBoost in low-data regimes (20–30% of training data). For retail demand prediction, QML achieves a 9.3% reduction in MAPE and offers more stable predictions under noisy feature perturbations. In credit-risk assessment, the QML classifier attains an ROC-AUC of 0.79, performing comparably to classical models while exhibiting higher robustness, maintaining accuracy within ±2% under noise injection, where classical models degrade by up to 6%. Overall, results reveal that QML models do not universally outperform classical machine learning but offer clear advantages when training data is limited, features exhibit nonlinear entanglement, or robustness under uncertainty is required. These findings position QML as a promising direction for next-generation predictive analytics and enterprise risk intelligence. The study also highlights existing hardware limitations and proposes future pathways for scalable, real-world deployment of QML-based business forecasting systems.