  Although entanglement is a basic resource for reaching quantum advantange in
many computation and information protocols, we lack a universal recipe for
detecting it, with analytical results obtained for low dimensional systems and
few special cases of higher dimensional systems. In this work, we use a machine
learning algorithm, the support vector machine with polynomial kernel, to
classify separable and entangled states. We apply it to two-qubit and
three-qubit systems, and we show that, after training, the support vector
machine is able to recognize if a random state is entangled with an accuracy up
to 92% for the two-qubit system and up to 98% for the three-qubit system. We
also describe why and in what regime the support vector machine algorithm is
able to implement the evaluation of an entanglement witness operator applied to
many copies of the state, and we describe how we can translate this procedure
into a quantum circuit.
