Adaptive Neuro Fuzzy Networks based on Quantum Subtractive Clustering

• URL: http://arxiv.org/abs/2102.00820v1
• Date: Tue, 26 Jan 2021 20:59:48 GMT
• Title: Adaptive Neuro Fuzzy Networks based on Quantum Subtractive Clustering
• Authors: Ali Mousavi, Mehrdad Jalali and Mahdi Yaghoubi
• Abstract summary: In this paper, an adaptive Neuro fuzzy network with TSK fuzzy type and an improved quantum subtractive clustering has been developed. The experimental results revealed that proposed Anfis based on quantum subtractive clustering yielded good approximation and generalization capabilities.
• Score: 5.957580737396458
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Data mining techniques can be used to discover useful patterns by exploring and analyzing data and it's feasible to synergitically combine machine learning tools to discover fuzzy classification rules.In this paper, an adaptive Neuro fuzzy network with TSK fuzzy type and an improved quantum subtractive clustering has been developed. Quantum clustering (QC) is an intuition from quantum mechanics which uses Schrodinger potential and time-consuming gradient descent method. The principle advantage and shortcoming of QC is analyzed and based on its shortcomings, an improved algorithm through a subtractive clustering method is proposed. Cluster centers represent a general model with essential characteristics of data which can be use as premise part of fuzzy rules.The experimental results revealed that proposed Anfis based on quantum subtractive clustering yielded good approximation and generalization capabilities and impressive decrease in the number of fuzzy rules and network output accuracy in comparison with traditional methods.

Related papers

• Measurement-based quantum convolutional neural network for deep learning [7.689125776844024]
  We propose an alternate approach to implementing quantum convolutional neural networks (QCNNs) by utilizing cluster states. The whole system is easier to stabilize by avoiding the complex controls. We provide numerical evidence that both quantum and classical data can be learned by measuring cluster states.
  arXiv  Detail & Related papers  (2024-12-11T08:55:07Z)
• A clustering aggregation algorithm on neutral-atoms and annealing quantum processors [0.44531072184246007]
  This work presents a hybrid quantum-classical algorithm to perform clustering aggregation. It is designed for neutral-atoms quantum computers and quantum annealers. Findings suggest promising potential for future advancements in hybrid quantum-classical pipelines.
  arXiv  Detail & Related papers  (2024-12-10T14:48:44Z)
• The role of data embedding in equivariant quantum convolutional neural networks [2.255961793913651]
  We investigate the role of classical-to-quantum embedding on the performance of equivariant quantum neural networks (EQNNs) We numerically compare the classification accuracy of EQCNNs with three different basis-permuted amplitude embeddings to the one obtained from a non-equivariant quantum convolutional neural network (QCNN)
  arXiv  Detail & Related papers  (2023-12-20T18:25:15Z)
• ShadowNet for Data-Centric Quantum System Learning [188.683909185536]
  We propose a data-centric learning paradigm combining the strength of neural-network protocols and classical shadows. Capitalizing on the generalization power of neural networks, this paradigm can be trained offline and excel at predicting previously unseen systems. We present the instantiation of our paradigm in quantum state tomography and direct fidelity estimation tasks and conduct numerical analysis up to 60 qubits.
  arXiv  Detail & Related papers  (2023-08-22T09:11:53Z)
• Learning Neural Eigenfunctions for Unsupervised Semantic Segmentation [12.91586050451152]
  Spectral clustering is a theoretically grounded solution to it where the spectral embeddings for pixels are computed to construct distinct clusters. Current approaches still suffer from inefficiencies in spectral decomposition and inflexibility in applying them to the test data. This work addresses these issues by casting spectral clustering as a parametric approach that employs neural network-based eigenfunctions to produce spectral embeddings. In practice, the neural eigenfunctions are lightweight and take the features from pre-trained models as inputs, improving training efficiency and unleashing the potential of pre-trained models for dense prediction.
  arXiv  Detail & Related papers  (2023-04-06T03:14:15Z)
• Problem-Dependent Power of Quantum Neural Networks on Multi-Class Classification [83.20479832949069]
  Quantum neural networks (QNNs) have become an important tool for understanding the physical world, but their advantages and limitations are not fully understood. Here we investigate the problem-dependent power of QCs on multi-class classification tasks. Our work sheds light on the problem-dependent power of QNNs and offers a practical tool for evaluating their potential merit.
  arXiv  Detail & Related papers  (2022-12-29T10:46:40Z)
• A didactic approach to quantum machine learning with a single qubit [68.8204255655161]
  We focus on the case of learning with a single qubit, using data re-uploading techniques. We implement the different proposed formulations in toy and real-world datasets using the qiskit quantum computing SDK.
  arXiv  Detail & Related papers  (2022-11-23T18:25:32Z)
• ClusterQ: Semantic Feature Distribution Alignment for Data-Free Quantization [111.12063632743013]
  We propose a new and effective data-free quantization method termed ClusterQ. To obtain high inter-class separability of semantic features, we cluster and align the feature distribution statistics. We also incorporate the intra-class variance to solve class-wise mode collapse.
  arXiv  Detail & Related papers  (2022-04-30T06:58:56Z)
• On Circuit-based Hybrid Quantum Neural Networks for Remote Sensing Imagery Classification [88.31717434938338]
  The hybrid QCNNs enrich the classical architecture of CNNs by introducing a quantum layer within a standard neural network. The novel QCNN proposed in this work is applied to the Land Use and Land Cover (LULC) classification, chosen as an Earth Observation (EO) use case. The results of the multiclass classification prove the effectiveness of the presented approach, by demonstrating that the QCNN performances are higher than the classical counterparts.
  arXiv  Detail & Related papers  (2021-09-20T12:41:50Z)
• Quantum Machine Learning with SQUID [64.53556573827525]
  We present the Scaled QUantum IDentifier (SQUID), an open-source framework for exploring hybrid Quantum-Classical algorithms for classification problems. We provide examples of using SQUID in a standard binary classification problem from the popular MNIST dataset.
  arXiv  Detail & Related papers  (2021-04-30T21:34:11Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.