Feedforward Neural Networks for Quantum Frustrated Magnets

Kenny Choo, Antonio Mezzacapo, Titus Neupert +1 more

Kenny Choo, Titus Neupert, Giuseppe Carleo

Inés Corte, Santiago Acevedo, Marcelo Arlego +1 more

Gopathy Purushothaman, Nicolaos B. Karayiannis

This paper introduces quantum neural networks (QNNs), a class of feedforward neural networks which are inherently capable of estimating the structure of a feature space in the form of fuzzy membership information. The hidden units of these networks develop quantized representations of the crisp sample information provided by the training set in various graded levels of certainty. Experimental results show that QNNs have an inherent ability for recognizing structures in the feature space that conventional feedforward neural networks with sigmoidal hidden units lack.

R. Kretzschmar, R. Bueler, N.B. Karayiannis +1 more

This study investigates the capacity of quantum neural networks (QNNs) to function as fuzzy classifiers. For this purpose, QNNs are compared with multilayer feedforward neural networks (FFNNs). The experiments are performed on two-dimensional speech data and investigate a variety of issues involved in the training of QNNs. This experimental study verifies that QNNs are capable of representing and quantifying the uncertainty inherent in the training data. It is also shown that simple post-processing of the QNN outputs makes QNNs an attractive alternative to conventional FFNNs for pattern classification applications.

Gopathy Purushothaman, Nicolaos B. Karayiannis

This paper introduces quantum neural networks (QNNs), a class of feedforward neural networks (FFNNs) inherently capable of estimating the structure of a feature space in the form of fuzzy sets. The hidden units of these networks develop quantized representations of the sample information provided by the training data set in various graded levels of certainty. Unlike other approaches attempting to merge fuzzy logic and neural networks, QNNs can be used in pattern classification problems without any restricting assumptions such as the availability of a priori knowledge or desired membership profile, convexity of classes, a limited number of classes, etc. Experimental results presented here show that QNNs are capable of recognizing structures in data, a property that conventional FFNNs with sigmoidal hidden units lack.