Quantum AI can overcome the threat of “Barren Plateaus”
Novel theorem demonstrates convolutional neural networks can always be trained on quantum computers, overcoming threat of ‘barren plateaus’ in optimization problems. Convolutional neural networks running on quantum computers have sparked a lot of interest because of their ability to analyse quantum data better than traditional computers. While the applicability of artificial neural networks for huge data sets has been limited due to a basic solvability problem known as “barren plateaus,” new research solves that Achilles heel with a rigorous argument that guarantees scalability. “The way you construct a quantum neural…