  Quantum computers have the unique ability to operate relatively quickly in
high-dimensional spaces -- this is sought to give them a competitive advantage
over classical computers. In this work, we propose a novel quantum machine
learning model called the Quantum Discriminator, which leverages the ability of
quantum computers to operate in the high-dimensional spaces. The quantum
discriminator is trained using a quantum-classical hybrid algorithm in O(N
logN) time, and inferencing is performed on a universal quantum computer in
linear time. The quantum discriminator takes as input the binary features
extracted from a given datum along with a prediction qubit initialized to the
zero state and outputs the predicted label. We analyze its performance on the
Iris data set and show that the quantum discriminator can attain 99% accuracy
in simulation.
