QuCNN : A Quantum Convolutional Neural Network with Entanglement Based Backpropagation
This work addresses the challenge of implementing efficient backpropagation in quantum machine learning for researchers in quantum computing, though it is incremental as it adapts classical methods to quantum settings.
The authors tackled the problem of adapting classical convolutional neural networks to quantum systems by introducing QuCNN, a quantum convolutional neural network with entanglement-based backpropagation, and demonstrated its feasibility by training a filter state against an ideal target state on a small MNIST subset.
Quantum Machine Learning continues to be a highly active area of interest within Quantum Computing. Many of these approaches have adapted classical approaches to the quantum settings, such as QuantumFlow, etc. We push forward this trend and demonstrate an adaption of the Classical Convolutional Neural Networks to quantum systems - namely QuCNN. QuCNN is a parameterised multi-quantum-state based neural network layer computing similarities between each quantum filter state and each quantum data state. With QuCNN, back propagation can be achieved through a single-ancilla qubit quantum routine. QuCNN is validated by applying a convolutional layer with a data state and a filter state over a small subset of MNIST images, comparing the back propagated gradients, and training a filter state against an ideal target state.