Quantum-Classical Hybrid Machine Learning for Image Classification (ICCAD Special Session Paper)

Alam, Mahabubul; Kundu, Satwik; Topaloglu, Rasit Onur; Ghosh, Swaroop

doi:10.1109/iccad51958.2021.9643516

Cited by 20 publications

(8 citation statements)

References 31 publications

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“…Amplitude encoding conserves qubits but relies on complex quantum circuits [47,48]. Conversely, angle encoding and its variants maintain consistent circuit depth but may be less efficient for high-dimensional data [32,49,50]. A hybrid encoding approach strikes a balance between qubit usage and circuit depth [46], while threshold-based encoding simplifies quantum convolution but may have limitations on real quantum devices [28].…”

Section: Related Workmentioning

confidence: 99%

“…Liu et al (2019) pioneered the development of the first QCNN model for image identification, drawing inspiration from regular CNNs [26]. This groundbreaking work has since sparked further investigation and research in the field, as evidenced by following publications [27][28][29][30][31][32], motivating the application of QCNN with improvement in its basic architecture for road extraction from HRSI.…”

Section: Introductionmentioning

confidence: 99%

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Extraction of Roads Using the Archimedes Tuning Process with the Quantum Dilated Convolutional Neural Network

Khan,

Singh,

Pradhan

et al. 2023

Sensors

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Road network extraction is a significant challenge in remote sensing (RS). Automated techniques for interpreting RS imagery offer a cost-effective solution for obtaining road network data quickly, surpassing traditional visual interpretation methods. However, the diverse characteristics of road networks, such as varying lengths, widths, materials, and geometries across different regions, pose a formidable obstacle for road extraction from RS imagery. The issue of road extraction can be defined as a task that involves capturing contextual and complex elements while also preserving boundary information and producing high-resolution road segmentation maps for RS data. The objective of the proposed Archimedes tuning process quantum dilated convolutional neural network for road Extraction (ATP-QDCNNRE) technology is to tackle the aforementioned issues by enhancing the efficacy of image segmentation outcomes that exploit remote sensing imagery, coupled with Archimedes optimization algorithm methods (AOA). The findings of this study demonstrate the enhanced road-extraction capabilities achieved by the ATP-QDCNNRE method when used with remote sensing imagery. The ATP-QDCNNRE method employs DL and a hyperparameter tuning process to generate high-resolution road segmentation maps. The basis of this approach lies in the QDCNN model, which incorporates quantum computing (QC) concepts and dilated convolutions to enhance the network’s ability to capture both local and global contextual information. Dilated convolutions also enhance the receptive field while maintaining spatial resolution, allowing fine road features to be extracted. ATP-based hyperparameter modifications improve QDCNNRE road extraction. To evaluate the effectiveness of the ATP-QDCNNRE system, benchmark databases are used to assess its simulation results. The experimental results show that ATP-QDCNNRE performed with an intersection over union (IoU) of 75.28%, mean intersection over union (MIoU) of 95.19%, F1 of 90.85%, precision of 87.54%, and recall of 94.41% in the Massachusetts road dataset. These findings demonstrate the superior efficiency of this technique compared to more recent methods.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Extraction of Roads Using the Archimedes Tuning Process with the Quantum Dilated Convolutional Neural Network

Khan,

Singh,

Pradhan

et al. 2023

Sensors

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“…As mentioned in the introduction, a QNN encodes the input as a set of qubits and replaces traditional neurons based on weights and biases with a quantum circuit consisting of a set of parameterized quantum gates. As shown in the Figure 2 (Variational Quantum Circuit), it consists of an encoding layer, a trainable quantum circuit layer, and a measurement layer [7]. The encoding layer encodes the classical input data into a quantum state using a series of parameterized rotation gates.…”

Section: Quantum Neural Networkmentioning

confidence: 99%

Multivariate time series prediction based on quantum enhanced LSTM models

2023

Second International Conference on Electronic Information Technology (EIT 2023)

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Long short-term memory (LSTM) is a widely used artificial neural network that is well suited for time series prediction. Quantum machine learning as a new research topic combines the advantages of quantum data processing and classical machine learning. In this paper, based on a hybrid quantum classical scheme, we design a quantum enhanced LSTM model and several variants such as QGRU. We also performed experiments with a multivariate time series prediction problem to verify the feasibility of these models. Through this research, we expect to explore the benefits and implementation of quantum-based machine learning.

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“…We attempt to demonstrate a potential security application of QML in classifying various PCB defects using a hybrid quantum-classical model. For this task, we employ the framework proposed by [3], in which we first use a convolutional autoencoder to reduce dimensionality before training a QNN with the crucial extracted features for our classification problem.…”

Section: Pcb Defect Classificationmentioning

confidence: 99%

“…In terms of encoding methods, parametric circuits, and measurement operations, a QNN/quantum filter has a plethora of design options. The Python framework developed by [3] supports a wide range of these options, which will impact the learnability of the QNN [27]. However, we use the single feature/qubit encoding method like in (Fig.…”

Section: Pcb Defect Classificationmentioning

confidence: 99%

Security Aspects of Quantum Machine Learning: Opportunities, Threats and Defenses

Kundu,

Ghosh

2022

Preprint

Self Cite

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In the last few years, quantum computing has experienced a growth spurt. One exciting avenue of quantum computing is quantum machine learning (QML) which can exploit the high dimensional Hilbert space to learn richer representations from limited data and thus can efficiently solve complex learning tasks. Despite the increased interest in QML, there have not been many studies that discuss the security aspects of QML. In this work, we explored the possible future applications of QML in the hardware security domain. We also expose the security vulnerabilities of QML and emerging attack models, and corresponding countermeasures. CCS CONCEPTS• Computing methodologies → Neural networks; • Computer systems organization → Quantum computing; • Security and privacy → Hardware attacks and countermeasures.

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Quantum-Classical Hybrid Machine Learning for Image Classification (ICCAD Special Session Paper)

Cited by 20 publications

References 31 publications

Extraction of Roads Using the Archimedes Tuning Process with the Quantum Dilated Convolutional Neural Network

Extraction of Roads Using the Archimedes Tuning Process with the Quantum Dilated Convolutional Neural Network

Multivariate time series prediction based on quantum enhanced LSTM models

Security Aspects of Quantum Machine Learning: Opportunities, Threats and Defenses

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