  There is heightened interest in quantum algorithms for Topological Data
Analysis (TDA) as it is a powerful tool for data analysis, but it can get
highly computationally expensive. Even though there are different propositions
and observations for Quantum Topological Data Analysis (QTDA), the necessary
details to implement them on software platforms are lacking. Towards closing
this gap, the present paper presents an implementation of one such algorithm
for calculating Betti numbers. The step-by-step instructions for the chosen
quantum algorithm and the aspects of how it can be used for machine learning
tasks are provided. We provide encouraging results on using Betti numbers for
classification and give a preliminary analysis of the effect of the number of
shots and precision qubits on the outcome of the quantum algorithm.
