QuCNN : A Quantum Convolutional Neural Network with Entanglement Based Backpropagation

• URL: http://arxiv.org/abs/2210.05443v1
• Date: Tue, 11 Oct 2022 13:36:15 GMT
• Title: QuCNN : A Quantum Convolutional Neural Network with Entanglement Based Backpropagation
• Authors: Samuel A. Stein, Ying Mao, James Ang, and Ang Li
• Abstract summary: QuCNN is a parameterised multi-quantum-state based neural network layer computing similarities between each quantum filter state and each quantum data state. Back propagation can be achieved through a single-ancillabit quantum routine. validated by applying a convolutional layer with a data state and a filter state over a small subset of MNIST images.
• Score: 9.760266670459446
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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