  In this work, we present a quantum circuit for a binary classification
prediction algorithm using a random forest model. The quantum prediction
algorithm is presented in our previous works. We construct a circuit and
implement it using qiskit tools (python module for quantum programming). One of
our goals is reducing the number of basic quantum gates (elementary gates). The
set of basic quantum gates which we use in this work consists of single-qubit
gates and a controlled NOT gate. The number of CNOT gates in our circuit is
estimated by $O(2^{n+2h+1})$ , when trivial circuit decomposition techniques
give $O(4^{|X|+n+h+2})$ CNOT gates, where $n$ is the number of trees in a
random forest model, $h$ is a tree height and $|X|$ is the length of attributes
of an input object $X$. The prediction process returns an index of the
corresponding class for the input $X$.
