Challenges
Datasets
Workspaces
Papers
Discussions
Leaderboard
Log inSign up
Challenges
Datasets
Workspaces
Papers
Discussions
Leaderboard
Blog
Job Board
Q3AS

© 2026 Aqora Quantum S.A.S.

TermsPrivacyLegal Notice

IEEE Transactions on Neural Networks

Quantum neural networks (QNNs): inherently fuzzy feedforward neural networks

Gopathy Purushothaman, Nicolaos B. Karayiannis

Published May 1, 1997• 163 citations
Publisher

Abstract

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.

DOI
10.1109/72.572106

Related papers

Quantum neural networks (QNNs): inherently fuzzy feedforward neural networks

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.

IEEE International Conference on Neural Networks/IEEE ... International Conference on Neural Networks•Dec 23, 2002•148 citations

Data Classification using Quantum Neural Network

Ghassan H. Al- Majeed, Zainab T. Alisa, Hassan Saadallah Naji

In this paper, integrated quantum neural network (QNN), which is a class of feedforward neural networks (FFNN’s), is performed through emerging quantum computing (QC) with artificial neural network(ANN) classifier. It is used in data classification technique, and here iris flower data is used as a classification signals. For this purpose independent component analysis (ICA) is used as a feature extraction technique after normalization of these signals, the architecture of (QNN’s) has inherently built in fuzzy, hidden units of these networks (QNN’s) to develop quantized representations of sample information provided by the training data set in various graded levels of certainty. Experimental results presented here show that (QNN’s) are capable of recognizing structures in data, a property that conventional (FFNN’s) with sigmoidal hidden units lack. In addition, (QNN) gave a kind of fast and realistic results compared with the (FFNN). Simulation results indicate that QNN is superior (with total accuracy of 97.778%) than ANN (with total accuracy of 93.334%).

Journal of Engineering•Jun 19, 2023•0 citations

Quantum neural networks versus conventional feedforward neural networks: an experimental study

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.

Machine learning for signal processing ...•Nov 7, 2002•17 citations

Quantum-Assisted Hierarchical Fuzzy Neural Network for Image Classification

Shengyao Wu, Runze Li, Yanqi Song +3 more

Deep learning is a powerful technique for data-driven learning in the era of Big Data. However, most deep learning models are deterministic models that ignore the uncertainty of data. Fuzzy neural networks are proposed to tackle this type of problem. In this article, we proposed a novel quantum assisted hierarchical fuzzy neural network (QA-HFNN). Different from classical fuzzy neural networks, QA-HFNN uses quantum neural networks (QNNs) to learn fuzzy membership functions. The model is a multifeature fusion learning algorithm with a parallel structural design that integrates quantum and classical neural networks. The classical network is used to capture high-dimensional neural features, the QNNs are designed to capture fuzzy logic features of the data, then, the two features are fused to form the final features to be classified. The experiment is performed on a classical computer, and the quantum circuit is built through a simulated quantum environment. The results indicate that the accuracy of QA-HFNN can equal to or even surpass classical methods in image classification tasks. The quantum circuit utilizes only a single qubit which is easy to implement. In addition, the fidelity of quantum circuit in a quantum noise environment is assessed, demonstrating that QA-HFNN has strong robustness. The time and computational complexity of QNNs was analyzed, further proving the effectiveness of the model.

IEEE Transactions on Fuzzy Systems•Aug 19, 2024•31 citations

Deep Quantum Networks for Classification

Shusen Zhou, Qingcai Chen, Xiaolong Wang

This paper introduces a new type of deep learning method named Deep Quantum Network (DQN) for classification. DQN inherits the capability of modeling the structure of a feature space by fuzzy sets. At first, we propose the architecture of DQN, which consists of quantum neuron and sigmoid neuron and can guide the embedding of samples divisible in new Euclidean space. The parameter of DQN is initialized through greedy layer-wise unsupervised learning. Then, the parameter space of the deep architecture and quantum representation are refined by supervised learning based on the global gradient-descent procedure. An exponential loss function is introduced in this paper to guide the supervised learning procedure. Experiments conducted on standard datasets show that DQN outperforms other feed forward neural networks and neuro-fuzzy classifiers.

Proceedings - International Conference on Pattern Recognition/Proceedings/International Conference on Pattern Recognition•Aug 1, 2010•14 citations

Quantum NN vs. NN in Signal Recognition

Xin-Yi Tsai, Yu‐Ju Chen, Huang‐Chu Huang +2 more

In this paper, the signal recognition by using quantum neural network (QNN) is studied and simulated. The signals with fuzziness distributed in the boundary of two different types of signals could be effectively recognized due to the structure of QNN's hidden units. To demonstrate the capability of QNN in recognition, the signals in a two-dimension (NC2) non-convex system is simulated. All the experiments are also performed by using the traditional neural network (NN) for a comparison.

International Conference on Information Technology and Applications•Aug 3, 2005•13 citations
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.
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.
In this paper, integrated quantum neural network (QNN), which is a class of feedforward neural networks (FFNN’s), is performed through emerging quantum computing (QC) with artificial neural network(ANN) classifier. It is used in data classification technique, and here iris flower data is used as a classification signals. For this purpose independent component analysis (ICA) is used as a feature extraction technique after normalization of these signals, the architecture of (QNN’s) has inherently built in fuzzy, hidden units of these networks (QNN’s) to develop quantized representations of sample information provided by the training data set in various graded levels of certainty. Experimental results presented here show that (QNN’s) are capable of recognizing structures in data, a property that conventional (FFNN’s) with sigmoidal hidden units lack. In addition, (QNN) gave a kind of fast and realistic results compared with the (FFNN). Simulation results indicate that QNN is superior (with total accuracy of 97.778%) than ANN (with total accuracy of 93.334%).
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.
Deep learning is a powerful technique for data-driven learning in the era of Big Data. However, most deep learning models are deterministic models that ignore the uncertainty of data. Fuzzy neural networks are proposed to tackle this type of problem. In this article, we proposed a novel quantum assisted hierarchical fuzzy neural network (QA-HFNN). Different from classical fuzzy neural networks, QA-HFNN uses quantum neural networks (QNNs) to learn fuzzy membership functions. The model is a multifeature fusion learning algorithm with a parallel structural design that integrates quantum and classical neural networks. The classical network is used to capture high-dimensional neural features, the QNNs are designed to capture fuzzy logic features of the data, then, the two features are fused to form the final features to be classified. The experiment is performed on a classical computer, and the quantum circuit is built through a simulated quantum environment. The results indicate that the accuracy of QA-HFNN can equal to or even surpass classical methods in image classification tasks. The quantum circuit utilizes only a single qubit which is easy to implement. In addition, the fidelity of quantum circuit in a quantum noise environment is assessed, demonstrating that QA-HFNN has strong robustness. The time and computational complexity of QNNs was analyzed, further proving the effectiveness of the model.
This paper introduces a new type of deep learning method named Deep Quantum Network (DQN) for classification. DQN inherits the capability of modeling the structure of a feature space by fuzzy sets. At first, we propose the architecture of DQN, which consists of quantum neuron and sigmoid neuron and can guide the embedding of samples divisible in new Euclidean space. The parameter of DQN is initialized through greedy layer-wise unsupervised learning. Then, the parameter space of the deep architecture and quantum representation are refined by supervised learning based on the global gradient-descent procedure. An exponential loss function is introduced in this paper to guide the supervised learning procedure. Experiments conducted on standard datasets show that DQN outperforms other feed forward neural networks and neuro-fuzzy classifiers.