A new paper entitled "An efficient geometric approach to quantum-inspired classifications" has been published in Nature Scientific Reports by our DISI researcher Davide Pastorello together with prof. Roberto Leporini from the University of Bergamo.
In the paper, the authors define and test some quantum-inspired algorithms for classification showing that the geometric structures of quantum theories allow to obtain a new kind of classifiers that can be run efficiently also on classical machines without the need for an underlying physical quantum system.
These quantum-inspired machine learning techniques will be even more efficient once implemented on a quantum computer, however it is clear that the quantum formalism enables also the development of machine learning schemes for conventional computers.