Technical finding regarding CNN and the ability to train. Applicability to discovering new materials.
Breakthrough Proof Clears Path for Quantum AI
Los Alamos National Laboratory News, October 15, 2021
Scientists at the U.S. Department of Energy's Los Alamos National Laboratory (LANL) have devised a proof that convolutional neural networks can always be trained on quantum computers, avoiding the threat of "barren plateaus" in optimization problems. LANL's Marco Cerezo said while a barren plateau eliminates any possibility of quantum speedup or advantage, "We proved the absence of barren plateaus for a special type of quantum neural network. Our work provides trainability guarantees for this architecture, meaning that one can generically train its parameters." LANL's Patrick Coles said, "With this guarantee in hand, researchers will now be able to sift through quantum-computer data about quantum systems and use that information for studying material properties or discovering new materials, among other applications." ... '
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