  Quantum machine learning (QML) has emerged as an important area for Quantum
applications, although useful QML applications would require many qubits.
Therefore our paper is aimed at exploring the successful application of the
Quantum Support Vector Machine (QSVM) algorithm while balancing several
practical and technical considerations under the Noisy Intermediate-Scale
Quantum (NISQ) assumption. For the quantum SVM under NISQ, we use quantum
feature maps to translate data into quantum states and build the SVM kernel out
of these quantum states, and further compare with classical SVM with radial
basis function (RBF) kernels. As data sets are more complex or abstracted in
some sense, classical SVM with classical kernels leads to less accuracy
compared to QSVM, as classical SVM with typical classical kernels cannot easily
separate different class data. Similarly, QSVM should be able to provide
competitive performance over a broader range of data sets including ``simpler''
data cases in which smoother decision boundaries are required to avoid any
model variance issues (i.e., overfitting). To bridge the gap between
``classical-looking'' decision boundaries and complex quantum decision
boundaries, we propose to utilize general shallow unitary transformations to
create feature maps with rotation factors to define a tunable quantum kernel,
and added regularization to smooth the separating hyperplane model. We show in
experiments that this allows QSVM to perform equally to SVM regardless of the
complexity of the data sets and outperform in some commonly used reference data
sets.
