The rapid development of quantum computer hardware has laid the hardware foundation for
the realization of QNN. Due to quantum properties, QNN shows higher storage capacity and …

We develop computationally affordable and encoding independent gradient evaluation
procedures for unitary coupled-cluster type operators, applicable on quantum computers …

Abstract Deep Convolutional Neural Network (CNN)-based image classification systems are
often susceptible to noise interruption, ie, minor image noise may significantly impact the …

In the past few years, quantum computing and machine learning fostered rapid
developments in their respective areas of application, introducing new perspectives on how …

Active quantum error correction is a central ingredient to achieve robust quantum
processors. In this paper we investigate the potential of quantum machine learning for …

Quantum machine learning is in a period of rapid development and discovery, however it
still lacks the resources and diversity of computational models of its classical complement …

The third type of neural network called spiking is developed due to a more accurate
representation of neuronal activity in living organisms. Spiking neural networks have many …

Quantum computing is expected to fundamentally change computer systems in the future.
Recently, a new research topic of quantum computing is the hybrid quantum–classical …

Different from the concept of universal computation, the universality of a quantum neural
network focuses on the ability to approximate arbitrary functions and is an important …

Recently, quantum machine learning has been ap-plied to stochastic-based modelling,
promising that the inherent uncertainty in quantum computing will be a significant advan …