  In recent years, interest in expressing the success of neural networks to the
quantum computing has increased significantly. Tensor network theory has become
increasingly popular and widely used to simulate strongly entangled correlated
systems. Matrix product state (MPS) is the well-designed class of tensor
network states, which plays an important role in processing of quantum
information. In this paper, we have shown that matrix product state as
one-dimensional array of tensors can be used to classify classical and quantum
data. We have performed binary classification of classical machine learning
dataset Iris encoded in a quantum state. Further, we have investigated the
performance by considering different parameters on the ibmqx4 quantum computer
and proved that MPS circuits can be used to attain better accuracy. Further,
the learning ability of MPS quantum classifier is tested to classify
evapotranspiration ($ET_{o}$) for Patiala meteorological station located in
Northern Punjab (India), using three years of historical dataset (Agri).
Furthermore, we have used different performance metrics of classification to
measure its capability. Finally, the results are plotted and degree of
correspondence among values of each sample is shown.
