Efficient Quantum Image Classification Using Single Qubit Encoding

Easom-McCaldin, Philip; Bouridane, Ahmed; Belatreche, Ammar; Jiang, Richard; Al-Maadeed, Somaya

doi:10.1109/tnnls.2022.3179354

Cited by 17 publications

(6 citation statements)

References 43 publications

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“…In this field, quantum neural networks (QNN) have emerged as a promising research area in quantum machine learning [27]- [29]. Due to the limited quantum resources available, most of the existing works focused on numerical analysis or datasets with lower dimensionalities [17], [30], [31], such as MNIST [32]. Farhi et al [33] introduced a QNN for binary classification, which utilizes quantum entanglement to enhance the model's computational power.…”

Section: Related Workmentioning

confidence: 99%

A Quantum-Classical Collaborative Training Architecture Based on Quantum State Fidelity

L'Abbate,

D'Onofrio,

Stein

et al. 2024

IEEE Trans. Quantum Eng.

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Recent advancements have highlighted the limitations of current quantum systems, particularly the restricted number of qubits available on near-term quantum devices. This constraint greatly inhibits the range of applications that can leverage quantum computers. Moreover, as the available qubits increase, the computational complexity grows exponentially, posing additional challenges. Consequently, there is an urgent need to use qubits efficiently and mitigate both present limitations and future complexities. To address this, existing quantum applications attempt to integrate classical and quantum systems in a hybrid framework. In this study, we concentrate on quantum deep learning and introduce a collaborative classical-quantum architecture called co-TenQu. The classical component employs a tensor network for compression and feature extraction, enabling higher-dimensional data to be encoded onto logical quantum circuits with limited qubits. On the quantum side, we propose a quantum-state-fidelity-based evaluation function to iteratively train the network through a feedback loop between the two sides. co-TenQu has been implemented and evaluated with both simulators and the IBM-Q platform. Compared to state-ofthe-art approaches, co-TenQu enhances a classical deep neural network by up to 41.72% in a fair setting. Additionally, it outperforms other quantum-based methods by up to 1.9 times and achieves similar accuracy while utilizing 70.59% fewer qubits.

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Section: Related Workmentioning

confidence: 99%

A Quantum-Classical Collaborative Training Architecture Based on Quantum State Fidelity

L'Abbate,

D'Onofrio,

Stein

et al. 2024

IEEE Trans. Quantum Eng.

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“…Third step, each counterbalance energy (corresponds to lose contacts) is computed in superposition and last step, the counterbalance with lowest energy (high number of loose contacts) is selected and given as input to the Grover's algorithm. [17] The following steps are described in detail.…”

Section: B Prediction Using Quantum Algorithmmentioning

confidence: 99%

Computational Analysis: Unveiling the Quantum Algorithms for Protein Analysis and Predictions

Bhuvaneswari,

Deepakraj,

Urooj

et al. 2023

IEEE Access

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The study of protein-protein interactions (PPIs) and predicting the protein structure plays a critical role in understanding cellular processes and designing therapeutic interventions. In this research, we explore the application of quantum algorithms, specifically Grover's algorithm, in improving the accuracy and efficiency of PPI prediction. By harnessing the inherent parallelism and quantum search capabilities of Grover's algorithm, we aim to enhance the identification of interacting protein pairs from large-scale datasets. We demonstrate the effectiveness of using this algorithm through an extensive approach, comparing the performance of Grover's algorithm with classical machine learning algorithms. Our results reveal that the quantum algorithm offers significant improvements in prediction accuracy, enabling the identification of previously undetected PPIs. Moreover, we discuss the advantages and limitations of using Grover's algorithm in PPI prediction and provide insights into its potential for accelerating research in protein interaction networks. This research highlights the potential of quantum algorithms in advancing the field of bioinformatics and protein interaction analysis.

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“…Due to its inherent parallelism and high speed of execution, QC would be able to ratify the errors in classification seen in traditional ML [32]. The research on quantum machine learning [33] for image categorization has revealed some positive results and shown that there is a huge potential for advancement. It is quite challenging to train a deep learning model for image classification, particularly underwater images, because of the complex environment of the ocean and the ever-present issue of image blurring.…”

Section: Introductionmentioning

confidence: 99%

Underwater Animal Identification and Classification Using a Hybrid Classical-Quantum Algorithm

Pravin,

Rohith,

Kiruthika

et al. 2023

IEEE Access

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Underwater Animal Identification and Classification is gaining significance in recent times as a result of the increased need for biodiversity monitoring and ecological surveillance. Although classical deep learning techniques have been widely used for these tasks, the effectiveness of identification and classification remains a bottleneck for several researchers due to the live capture of animals in complex environments, a limited sea-animal image dataset, and the complex topography of the seafloor, particularly in shallow waters, sediments, reefs, submarine ridges, and ship radiation. In this paper, three hybrid Classical-Quantum neural networks ResNet50-QCNN, ResNet18-QCNN and InceptionV3-QCNN have been proposed for underwater quantum-classical Animal Identification and Classification. By using quantum devices to reduce dimension and denoise datasets, it dramatically reduces the complexity of data processing on classical computers. The outcomes of the numerical simulation show that the quantum algorithm may effectively reduce dimensionality and increase classification accuracy. Even when quantum data is read out classically, the hybrid approach provides polynomial acceleration in dimension reduction beyond classical procedures. The three hybrid models, viz., ResNet50-QCNN, ResNet18-QCNN, and InceptionV3-QCNN, displayed classification test accuracy of 88%, 80.29%, and 70%, respectively, revealing that ResNet50-QCNN performed best in identifying and classifying underwater animals.INDEX TERMS Hybrid quantum circuit, InceptionV3-QCNN, Resnet50-QCNN, ResNet18-QCNN, and Sea-animal image dataset.

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Efficient Quantum Image Classification Using Single Qubit Encoding

Cited by 17 publications

References 43 publications

A Quantum-Classical Collaborative Training Architecture Based on Quantum State Fidelity

A Quantum-Classical Collaborative Training Architecture Based on Quantum State Fidelity

Computational Analysis: Unveiling the Quantum Algorithms for Protein Analysis and Predictions

Underwater Animal Identification and Classification Using a Hybrid Classical-Quantum Algorithm

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