  We devise new quantum algorithms that exponentially speeds up the training
and prediction procedures of twin support vector machines (TSVM). To train
TSVMs using quantum methods, we demonstrate how to prepare the desired input
states according to classical data, and these states are used in the quantum
algorithm for the system of linear equations. In the prediction process, we
employ a quantum circuit to estimate the distances from a new sample to the
hyperplanes and then make a decision. The proposed quantum algorithms can learn
two non-parallel hyperplanes and classify a new sample by comparing the
distances from the sample to the two hyperplanes in $O(\log mn)$ time, where
$m$ is the sample size and $n$ is the dimension of each data point. In
contrast, the corresponding classical algorithm requires polynomial time for
both the training and prediction procedures.
