Variational Quantum Approximate Support Vector Machine With Inference Transfer

A kernel-based quantum classifier is the most interesting and powerful quantum machine learning technique for hyperlinear classification of complex data, which can be easily realized in shallow-depth quantum circuits such as a SWAP test classifier. Surprisingly, a support vector machine can be realized inherently and explicitly on these circuits by introduction of a variational scheme to map the quadratic optimization problem of the SVM theory to a quantum-classical variational optimization problem. This scheme is realized with parameterized quantum circuits (PQC) to create a nonuniform weight vector to index qubits that can evaluate training loss and classification score in a linear time. We train the classical parameters of this Variational Quantum Approximate Support Vector Machine (VQASVM), which can be transferred to many copies of other VQASVM decision inference circuits for classification of new query data. Our VQASVM algorithm is experimented with toy example data sets on cloud-based quantum machines for feasibility evaluation, and numerically investigated to evaluate its performance on a standard iris flower data set. The accuracy of iris data classification reached 98.8%.