  We construct a hybrid quantum-classical approach for the $K$-Nearest
Neighbour algorithm, where the information is embedded in a phase-distributed
multimode coherent state with the assistance of a single photon. The task of
finding the closeness between the data points is delivered by the quantum
optical computer, while the sorting and class assignment are performed by a
classical computer. We provide the quantum optical architecture corresponding
to our algorithm. The subordinate optical network is validated by numerical
simulation. We also optimize the computational resources of the algorithm in
the context of space, energy requirements and gate complexity. Applications are
presented for diverse and well-known public benchmarks and synthesized data
sets.
