Hybrid quantum-classical convolutional neural networks

Liu, Junhua; Lim, Kwan Hui; Wood, Kristin L.; Huang, Wei; Guo, Chu; Huang, He-Liang

doi:10.1007/s11433-021-1734-3

“…Lithology interpretation from well logs is discussed in [61], and quantum variational autoencoder presented in [62]. Quantum Neural Networks (QNNs) are often presented as hybrid algorithms that leverage quantum nodes throughout the networks [63], [64], [65]. QNNs develop a network of both quantum and classical nodes with some given activation functions, convolutional connections, and weighted edges.…”

Section: Quantum Machine Learningmentioning

confidence: 99%

On Circuit-Based Hybrid Quantum Neural Networks for Remote Sensing Imagery Classification

Sebastianelli

¹

,

Zaidenberg

²

,

Spiller

³

et al. 2022

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

View full text Add to dashboard Cite

This article aims to investigate how circuit-based hybrid Quantum Convolutional Neural Networks (QCNNs) can be successfully employed as image classifiers in the context of remote sensing. 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, and tested on the EuroSAT dataset used as reference benchmark. 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. Moreover, investigation of various quantum circuits shows that the ones exploiting quantum entanglement achieve the best classification scores. This study underlines the potentialities of applying quantum computing to an EO case study and provides the theoretical and experimental background for futures investigations.

show abstract

“…Lithology interpretation from well logs is discussed in [40], and quantum variational autoencoder presented in [41]. Quantum Neural Networks (QNNs) are often presented as hybrid algorithms that leverage quantum nodes throughout the networks [42], [43], [44]. QNNs develop a network of both quantum and classical nodes with some given activation functions, convolutional connections, and weighted edges.…”

Section: Quantum Machine Learningmentioning

confidence: 99%

On Circuit-based Hybrid Quantum Neural Networks for Remote Sensing Imagery Classification

Sebastianelli¹,

Zaidenberg²,

Spiller³

et al. 2021

Preprint

View full text Add to dashboard Cite

This article aims to investigate how circuit-based hybrid Quantum Convolutional Neural Networks (QCNNs) can be successfully employed as image classifiers in the context of remote sensing. 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, and tested on the EuroSAT dataset used as reference benchmark. 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. Moreover, investigation of various quantum circuits shows that the ones exploiting quantum entanglement achieve the best classification scores. This study underlines the potentialities of applying quantum computing to an EO case study and provides the theoretical and experimental background for futures investigations.

show abstract

“…The author in [11] improve the feature mapping process, introduce a hybrid quantum-classical convolutional neural network (QCCNN), which is based on convolutional neural networks (CNNs) but is optimized for quantum computing. In terms of both the number of qubits and the depths of the circuits, QCCNN is favorable to existing noisy intermediatescale quantum computers, while keeping crucial aspects of classical CNN, such as nonlinearity and scalability.…”

Section: Related Workmentioning

confidence: 99%

Modified Deep Residual Quantum Computing Optimization Technique for IoT Platform

El-Aziz¹,

Rayan²,

Shahin³

et al. 2021

IJACSA

2

0

View full text Add to dashboard Cite

Internet of Things (IoT) is defined as millions of interconnections between wireless devices to obtain data globally. The multiple data are targeting to observe the data through a common platform, and then it becomes essential to investigate accuracy for realizing the best IoT platform. To address the growing demand for time-sensitive data analysis and real-time decision-making, accuracy in IoT data collecting has become critical. The Res-HQCNN is a hybrid quantum-classical neural network with deep residual learning. The model is qualified in an end-to-end analog method in a traditional neural network, backpropagation is used. To discover the Res-HQCNN efficiency to perform on the classical computer, there has been a lot of investigation into quantum data with or without noise. Then focus on the application of the artificial neural network to analyze the dangers to these IoT networks. For data recording purposes, to undertake in-depth analysis on the threat severity, kind, and source, a model is trained using recurrent and convolutional neural networks. The intrusion detection system (IDS) explored in this study has a success rate of 99% based on the empirical data supplied to the model. Due to irregularly distributed robust execution, larger affectability for the introduction of authority dimension, steadiness, and the extremely large crucial area, a quantum hash function work has been proposed as an amazing method for secure communication between the IoT and cloud.

show abstract

Hybrid quantum-classical convolutional neural networks

Cited by 140 publications

References 46 publications

On Circuit-Based Hybrid Quantum Neural Networks for Remote Sensing Imagery Classification

On Circuit-Based Hybrid Quantum Neural Networks for Remote Sensing Imagery Classification

On Circuit-based Hybrid Quantum Neural Networks for Remote Sensing Imagery Classification

Modified Deep Residual Quantum Computing Optimization Technique for IoT Platform

Contact Info

Product

Resources

About