A Geometric view of Quantum Machine Learning

Linear Regression is being used ubiquitously in Machine Learning (ML) based solutions to predict the output. In this paper, we propose a geometric approach to this ML approach of linear regression. We use the Diophantus II.VIII generalized equations to build a sphere around the data set, use the slope equation and finetune it to arrive at the best fit for the ML prediction. With the help of Quantum computing, the estimation brings out to perform 2D and 3D representations of this approach towards Heisenberg uncertainty principle.