Quantum Convolutional Neural Networks

Convolutional neural networks provide a successful machine learning architecture for classification tasks such as image recognition 1,24,25.A CNN generally consists of a sequence of different

Quantum Convolutional Neural Networks. Tue, 09102019. Machine learning techniques have so far proved to be very promising for the analysis of data in several fields, with many potential applications. However, researchers have found that applying these methods to quantum physics problems is far more challenging due to the exponential

A Quantum Convolutional Neural Network QCNN designed specifically to enhance the classification of classical data using quantum machine learning techniques. The architecture leverages

Quantum convolutional neural networks can greatly improve classification accuracy in image recognition tasks by leveraging quantum filter optimization methodologies to refine feature extraction processes. To further boost accuracy, the utilization of data augmentation techniques and transfer learning benefits can be instrumental.

Simple model of a feed forward neural network. For a deep learning network, increase the number of hidden layers. Quantum neural networks are computational neural network models which are based on the principles of quantum mechanics.The first ideas on quantum neural computation were published independently in 1995 by Subhash Kak and Ron Chrisley, 1 2 engaging with the theory of quantum

Here, we realize a quantum convolutional neural network QCNN on a 7-qubit superconducting quantum processor to identify symmetry-protected topological SPT phases of a spin model characterized

This tutorial implements a simplified Quantum Convolutional Neural Network QCNN, a proposed quantum analogue to a classical convolutional neural network that is also translationally invariant. This example demonstrates how to detect certain properties of a quantum data source, such as a quantum sensor or a complex simulation from a device.

CNN is composed of several layers of filters to get feature maps of input data, yet foremost and crucial one is convolutional layer, hence the name Convolutional neural networks. However, the growth of quantum computing and quantum neural network in deep learning is limited. Three main obstacles that limit the growth of these are, first is due

A novel quantum machine learning model inspired by convolutional neural networks, with efficient training and implementation on near-term quantum devices. The paper shows two applications recognizing topological phases and optimizing quantum error correction codes.