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Hybrid Quantum-Classical Selective State Space Artificial Intelligence

arXiv — cs.CL•Wednesday, November 12, 2025 at 5:00:00 AM
PositiveArtificial Intelligence
The recent publication on Hybrid Quantum Classical (HQC) algorithms highlights their potential to revolutionize machine learning by utilizing quantum circuits for enhanced computational efficiency. Traditional NLP models often struggle with time complexity due to extensive matrix multiplications, but the proposed mechanism, specifically designed for temporal sequence classification within the Mamba architecture, aims to mitigate these issues. By employing Variational Quantum Circuits (VQCs) as quantum gating modules, the research promises to improve feature extraction and suppress irrelevant information, directly addressing deep learning's computational bottlenecks. The integration of quantum resources not only enhances the performance of these models but also impacts their generalization capability, expressivity, and parameter efficiency, paving the way for more scalable and resource-efficient NLP applications.
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