  Encoding classical data on quantum spin Hamiltonians yields ordered spin
ground states which are used to discriminate data types for binary
classification. The Ising Hamiltonian is a typical spin model to encode
classical data onto qubits, known as the ZZ feature map. We assess the ground
states of the Ising Hamiltonian encoded with three separate data sets
containing two classes of data. A new methodology is proposed to predict a
certain data class using the ground state of the encoded Ising Hamiltonian.
Ground state observables are obtained through quantum simulation on a quantum
computer, and the expectation values are used to construct a classical
probability distribution on the state space. Our approach is a low dimensional
representation of the exponentially large feature space. The antiferromagnetic
ground state is the stable ground state for the one dimensional chain lattice
and the 2D square lattice. Frustration induces unique ordered states on the
triangle lattice encoded with data, hinting at the possibility for an
underlying phase diagram for the model. We examine order stability with data
scaling and data noise.
