Quantum-inspired machine learning: The solution for explainable AI?
Tensor networks (TN) are a well-established tool for simulating quantum systems due to their ability to transparently and efficiently process a high amount of information. The group of Tensor Solutions now adapted this method to solve machine learning (ML) problems and thereby were able to efficiently learn and interpret big data problems. With this development, they are aiming to create a new fair, transparent and efficient technology for artificial intelligence.
The research behind was executed as a cooperation work between the research group of theoretical quantum physics at the University of Padova, the INFN section in Padova and the spin-off project Tensor Solutions. Tensor Solutions is a BMWi-funded project that aims to make the field of Artificial Intelligence (AI) more transparent, comprehensible and efficient. The team behind consists of researchers from quantum physics and data science developing Machine Learning prototypes with novel quantum-inspired methods to enter the future market of AI.