Fig. 4
From: A quantum inspired machine learning approach for multimodal Parkinson’s disease screening
Example qubit encoding of one feature of the data. This qubit represents one of 15 qubits in the qSVM kernel circuit, with one qubit corresponding to each feature of the dataset. SVM kernels are designed to output a measure of similarity between two inputs x1 and x2. This figure shows the overlap computation between two inputs from the training set, using the gates RZ(\(\:-\frac{\pi\:}{2}\)), \(\:\sqrt{x}\), RZ(x1 - x2), \(\:\sqrt{x}\) and RZ(\(\:-\frac{\pi\:}{2}\)). These gates represent a quantum mechanical decomposition of RY(x1) followed by RY(-x2). Finally, the measurement gate at the end provides an output corresponding to the similarity between the inputs x1 and x2. Figure was generated using version 1.2.4 of the Qiskit Python library (https://pypi.org/project/qiskit/).
