Experimental demonstration of quantum-enhanced machine learning in NV center system

We demonstrate the quantum-enhanced supervised classication of vectors in an NV center system
using the algorithm of Lloyd, Mohseni and Rebentrost [arXiv:1307.0411 (2013)]. A ¹³C nuclear spin
is employed to encode the vectors with the spin-triplet state of the NV center as an ancilla. We design
efficient methods to prepare the initial electron-nuclear entangled state within the coherence time T_2^*
of the electron spin, and then compute the distance between the test vector and the center of each
class by measuring the level population through maximum likelihood estimation. Our experiment
allows more than one reference vectors in each class, thus forms an important enabling step toward
quantum-enhanced machine learning.