  Quantum machine learning is at the crossroads of two of the most exciting
current areas of research; quantum computing and classical machine learning. It
explores the interaction between quantum computing and machine learning,
investigating how results and techniques from one field can be used to solve
the problems of the other. Here, we investigate quantum support vector machine
(QSVM) algorithm and its circuit version on present quantum computers. We
propose a general encoding procedure extending QSVM algorithm, which would
allow one to feed vectors with higher dimension in the training-data oracle of
QSVM. We compute the efficiency of the QSVM circuit implementation method by
encoding training and testing data sample in quantum circuits and running them
on quantum simulator and real chip for two datasets; 6/9 and banknote. We
highlight the technical difficulties one would face while applying the QSVM
algorithm on current NISQ era devices. Then we propose a new method to classify
these datasets with enhanced efficiencies for the above datasets both on
simulator and real chips.
