Quantum machine learning
QxBranch, a quantum computing firm based out of Washington DC, has come up with a quantum machine learning approach to model the American elections. They used the 2016 American elections to create their machine learning model. A fully connected graphical model was identified as the best fit for correlations between the American states. The following diagram shows an example of what a graphical model could look like.
One of the key challenges associated with connected graphical models in modeling correlations across variables is in implementing them using classical computation. The models were powerful; however, they could not be generated using existing computing infrastructure. Recent developments in quantum computing have addressed the computational power needs to train these models. Graphical networks are now a realistic option when dealing with correlated variables.
Figure 1: Illustration of a graphical network Source: https://medium...
