EPFL scientists have made significant progress in teaching a quantum computer how to comprehend and predict the behavior of quantum systems. Their study, led by Professor Zoe Holmes and her team at EPFL, in collaboration with researchers from Caltech, the Free University of Berlin, and the Los Alamos National Laboratory, has paved the way for the development of quantum neural networks (QNNs). These machine-learning models leverage principles inspired by quantum mechanics to simulate the behavior of quantum systems.
QNNs, like traditional neural networks, consist of interconnected nodes or “neurons” that perform calculations. However, QNNs differ in that their neurons operate on the principles of quantum mechanics, allowing them to manipulate and process quantum information. In this study, the researchers demonstrate that even a few simple examples, known as “product states,” provide enough information for a quantum machine-learning model to learn how a quantum system behaves, even when dealing with more complex and entangled states.
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