  Lately, much attention has been given to quantum algorithms that solve
pattern recognition tasks in machine learning. Many of these quantum machine
learning algorithms try to implement classical models on large-scale universal
quantum computers that have access to non-trivial subroutines such as
Hamiltonian simulation, amplitude amplification and phase estimation. We
approach the problem from the opposite direction and analyse a distance-based
classifier that is realised by a simple quantum interference circuit. After
state preparation, the circuit only consists of a Hadamard gate as well as two
single-qubit measurements, and computes the distance between data points in
quantum parallel. We demonstrate the proof-of-principle using the IBM Quantum
Experience and analyse the performance of the classifier with numerical
simulations, showing that it classifies surprisingly well for simple benchmark
tasks.
