Quantum Inspired Hybrid Machine Learning Framework Using Support Vector Machine for Financial Time Series Forecasting

Abstract

This article proposes a quantum inspired Support Vector Machine (QISVM) framework that employs simulated quantum circuit features to address these challenges. To encapsulate such features, we use a four-qubit encoding protocol using X-axis rotation and entanglement created from Controlled-NOT (CNOT). Quantum inspired elements from simulated circuit output include entropy and probability of the most likely measurement state. These features, including time-based indicators and quantum inspired representations, are scaled using MinMax normalization to ensure consistent feature ranges and numerical stability during SVM training. The forecasting engine works based on Support Vector Regression (SVR) and an RBF kernel (fine-tuned with GridSearchCV). Moreover, we propose a dual learning architecture implementing SVR and SVC for regression-based forecasting and movement direction judgment. Quantum circuit simulations on batch level done using Qiskit AerSimulator and joblib-based caching which are scalable and reproducible but without quantum hardware. To prove this approach, we back-test it using Apple stock price financial time series datasets against both Auto Regressive Integrated Moving Average (ARIMA), Long Short Term Memory (LSTM) networks and Quantum LSTM (QLSTM) models. QISVM has superior forecasting performance as well as directional forecast capability with less training time and computational resources than hybrid, deep learning and others. Herein, the proposed framework provides feature-level diagnostic interpretability through its explicitly defined quantum-inspired features and permutation-based feature analysis.