Defining Quantum Neural Networks via Quantum Time Evolution

This work presents a novel fundamental algorithm for for defining and training Neural Networks in Quantum Information based on time evolution and the Hamiltonian. Classical Neural Network algorithms (ANN) are computationally expensive. For example, in image classification, representing an image pixel by pixel using classical information requires an enormous amount of computational memory resources. Hence, exploring methods to represent images in a different paradigm of information is important. Quantum Neural Networks (QNNs) have been explored for over 20 years. The current forefront work based on Variational Quantum Circuits is specifically defined for the Continuous Variable (CV) Model of quantum computers. In this work, a model is proposed which is defined at a more fundamental level and hence can be inherited by any variants of quantum computing models. This work also presents a quantum backpropagation algorithm to train our QNN model and validate this algorithm on the MNIST dataset on a quantum computer simulation.

Image Processing in Quantum Computers

Quantum Image Processing (QIP) is an exiting new field showing a lot of promise. Expressing an image using quantum information would mean all of the unique quantum properties like superposition or entanglement can apply to the image. In this work, we explore and compare a number of quantum image models and then implement simulate a quantum image using a classical computer.