Quantum-Enhanced Support Vector Machine for High-Dimensional Financial Market Prediction
Prajwal S S Reddy, Samyama Gunjal G H, Ramya R S +1 more
Financial datasets possess high dimensionality and non-linear structure that challenge classical models in providing necessary forecastings. This work propose a hybrid Quantum-Enhanced Support Vector Machine (QSVM) that encodes technical indicators via a fixed ZZFeatureMap and computes a quantum kernel utilising a trainable Real Amplitude circuit and a classical SVM. The study adopts rigorous statistical validation with 10 independent runs, stratified with 5-fold CV, mean with 95% confidence intervals and uses McNemar’s test for paired significance. Realistic resource analysis and optional NISQ noise settings are included with qubits, circuit depth, two-qubit gates and shots. On a combined financial dataset, including S&P 500 stocks, cryptocurrencies, forex pairs and FRED economic indicators, QSVM achieves modest but statistically significant gains over tuned classical baselines, with advantages widening at high dimensionality. Results clarify simulation overheads and expected noise-induced degradation, supporting a cautious and reproducible assessment of QSVM for financial forecasting.
International Conference on Next-Gen Quantum and Advanced Computing: Algorithms, Security, and Beyond (NQComp)•Mar 5, 2026•0 citations