Rebbelith explores the viability of quantum machine learning as a tool for coping with Radical Uncertainty by focussing on two key points:

1. Why finance and investing will not benefit from quantum machine learning as expected and how, because of non-stationarity, its application may embed and magnify risks.

2. Why some uncertainties, like those faced in pharmaceuticals development, make a viable application for quantum machine learning.

We then discuss why systems that help us cope with uncertainty must accommodate the way the world actually is, and the way humans actually think, so that we can make better decisions.

Using Quantum Computing uncertainty to run stochastic methods (pharmaceutical) => Ok

Using Quantum Computing uncertainty to replay another uncertainty (finance) => Not Ok