A hybrid quantum-classical graph transformer for sentiment classification reports higher accuracy and better sample efficiency than a classical graph transformer on five small benchmark datasets.
Challenges and Opportunities in Quantum Machine Learning
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abstract
At the intersection of machine learning and quantum computing, Quantum Machine Learning (QML) has the potential of accelerating data analysis, especially for quantum data, with applications for quantum materials, biochemistry, and high-energy physics. Nevertheless, challenges remain regarding the trainability of QML models. Here we review current methods and applications for QML. We highlight differences between quantum and classical machine learning, with a focus on quantum neural networks and quantum deep learning. Finally, we discuss opportunities for quantum advantage with QML.
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cs.CL 1years
2025 1verdicts
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Quantum Graph Transformer for NLP Sentiment Classification
A hybrid quantum-classical graph transformer for sentiment classification reports higher accuracy and better sample efficiency than a classical graph transformer on five small benchmark datasets.