Abstract Machine Learning (ML) is one of the most exciting and dynamic areas of modern
research and application. The purpose of this review is to provide an introduction to the core …

Quantum neural networks (QNNs) are promised to be powerful computing devices that
integrate the advantages of artificial neural networks (ANNs) and quantum computing. Due …

In dieser Open-Access-Publikation liefert Thomas Gehlert eine konsistente, auf aktuellen
Experimenten und Theorien basierte, fundierte natur-und neurowissenschaftliche Theorie …

Binary neural networks, ie, neural networks whose parameters and activations are
constrained to only two possible values, offer a compelling avenue for the deployment of …

Quantum neural networks have been explored in a number of tasks including image
recognition. Most of the approaches involve using quantum gates in the neurons. Hybrid …

In this paper, we go through the theoretical steps of the spectral clustering on quantum
computers by employing the phase estimation and the amplitude amplification algorithms …

Most existing cortico-hippocampal computational models use different artificial neural
network topologies. These conventional approaches, which simulate various biological …

Quantum computing is a technology that takes advantage of quantum effects, which
operates using quantum computing, resulting in increased algorithm speed compared to the …

This research is motivated by previous research with a quantum circuit architecture on the
quantum perceptron algorithm that is not yet optimal, characterized by its probability value …

This research is motivated by the incomplete use of quantum in existing learning algorithms,
so that the proposed learning algorithm is not optimal. Research (Fahri & Neven, 2018) …