QuCNN: A Quantum Convolutional Neural Network with Entanglement Based Backpropagation

On 12 Oct, 2022 By admin 0 Comments

October, 2022

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.

Attachment: 
PDF icon QuCNN A Quantum Convolutional Neural Network with Entanglement Based Backpropagation.pdf
Resource Type: 
Academic Paper
Tags: 
Quantum Computing
Machine Learning
Quantum Machines
Quantum Information Processing
Quantum Algorithms
Quantum Processor
Quantum Machine Learning
Convolutional Neural Networks