There is considerable interest in running convolutional neural networks on quantum computers, thanks to their potential ability to run quantum simulations far more effectively than classical computers can. However, the fundamental solvability problem of 'barren plateaus' has so far limited the application of these neural nets for large data sets.
“The way you construct a quantum neural network can lead to a barren plateau, or not,” explained Dr Marco Cerezo, quantum computing expert at Los Alamos and co-author of the study. “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.”
Quantum convolutional neural networks are inspired by the visual cortex. They involve a series of 'filters', or convolutional layers, interleaved with pooling layers that reduce the dimension of the data while retaining...