Machine learning algorithms learn a desired input-output relation from examples in order to
interpret new inputs. This is important for tasks such as image and speech recognition or …

With the overwhelming success in the field of quantum information in the last decades, the
'quest'for a Quantum Neural Network (QNN) model began in order to combine quantum …

Machine Learning (ML) aims at designing models that learn from previous experience,
without being explicitly formulated. Applications of machine learning are inexhaustible …

Here we discuss advances in the field of quantum machine learning. The following
document offers a hybrid discussion; both reviewing the field as it is currently, and …

In this work, we propose a quantum neural network named quantum perceptron over a field
(QPF). Quantum computers are not yet a reality and the models and algorithms proposed in …

This paper makes an exhaustive survey of various applications of Quantum inspired
computational intelligence (QCI) techniques proposed till date. Definition, categorization and …

摘要机器学习在过去十几年里不断发展, 并对其他领域产生了深远的影响. 近几年,
研究人员发现结合量子计算特性的新型机器学习算法可实现对传统算法的加速 …

The practice of deep neural framework specific to convolutional neural networks
(ConNeuNets) in domain of object detection is substantial. The existing deep ConNeuNets …

A supervised learning algorithm for quantum neural networks (QNN) based on a novel
quantum neuron node implemented as a very simple quantum circuit is proposed and …

Training artificial neural networks requires a tedious empirical evaluation to determine a
suitable neural network architecture. To avoid this empirical process several techniques …