Fig. 5
From: A quantum inspired machine learning approach for multimodal Parkinson’s disease screening
Quantum-Inspired Support Vector Machine (qSVM) Framework. The diagram outlines a qSVM architecture where classical data is processed through a hybrid quantum-classical pipeline: Data Preparation Raw data is cleaned, normalized, and features are extracted for quantum encoding. Quantum Feature Mapping: Classical data is embedded into a quantum feature space using statevector simulation. Quantum Processing: Quantum kernels, measuring data similarity in the quantum space, are computed. Quantum Circuit: Quantum operations on qubits are executed, including the decomposition of the RY gate into RZ and √x gates. SVM Training: A classical optimizer employs the quantum kernel matrix to determine the optimal separating hyperplane. Prediction & Evaluation: New data is classified and performance is evaluated using metrics such as accuracy, precision, and recall. The pipeline follows the sequence: Input Data → Feature Encoding → Quantum Circuit → Kernel Calculation → SVM Optimization → Classification. This approach integrates quantum-enhanced feature representation with traditional SVM for improved classification.
