Incremental Data-Uploading for Full-Quantum Classification

Periyasamy, Maniraman; Meyer, Nico; Ufrecht, Christian; Scherer, Daniel D.; Plinge, Axel; Mutschler, Christopher

doi:10.1109/qce53715.2022.00021

Cited by 6 publications

(1 citation statement)

References 17 publications

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“…However, when I > n (where n is the number of qubits and I is the length of the feature sequence), the current approach typically reduces the input dimensionality to match the number of qubits through a linear layer, which may result in the loss of semantic information to some extent. We draw inspiration from a quantum algorithm for image classification on the MNIST handwritten dataset [33], which differs from the previous data re-uploading approach [34].…”

Section: B Qrnnmentioning

confidence: 99%

Application of Quantum Recurrent Neural Network in Low-Resource Language Text Classification

Yu,

Yin,

Zhang

et al. 2024

IEEE Trans. Quantum Eng.

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Text sentiment analysis is an important task in natural language processing and has always been a hot research topic. However, in low-resource regions such as South Asia, where languages like Bengali are widely used, the research interest is relatively low compared to high-resource regions due to limited computational resources, flexible word order, and high inflectional nature of the language. With the development of quantum technology, quantum machine learning models leverage the superposition property of qubits to enhance model expressiveness and achieve faster computation compared to classical systems. To promote the development of quantum machine learning in low-resource language domains, we propose a quantum-classical hybrid architecture. This architecture utilizes a pre-trained multilingual BERT model to obtain vector representations of words and combines the proposed Batched Upload Quantum Recurrent Neural Network (BUQRNN) and Parameter Non-shared Batched Upload Quantum Recurrent Neural Network (PN-BUQRNN) as feature extraction models for sentiment analysis in Bengali. Our numerical results demonstrate that the proposed BUQRNN structure achieves a maximum accuracy improvement of 0.993% in Bengali text classification tasks while reducing average model complexity by 12%. The PN-BUQRNN structure surpasses the BUQRNN structure once again and outperforms classical architectures in certain tasks.INDEX TERMS Natural language processing,Quantum machine learning,Quantum recurrent neural network.

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Section: B Qrnnmentioning

confidence: 99%