Quantum-Inspired Data Embedding for Unlabeled Data in Sparse Environments: A Theoretical Framework for Improved Semi-Supervised Learning without Hardware Dependence
This paper introduces an innovative theoretical framework for quantum-inspired data embeddings, grounded in foundational concepts of quantum mechanics such as superposition and entanglement. This approach aims to advance semi-supervised learning in contexts characterized by limited labeled data by e...
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| Main Author: | |
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| Format: | Article |
| Language: | English |
| Published: |
Sakarya University
2024-12-01
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| Series: | Sakarya University Journal of Computer and Information Sciences |
| Subjects: | |
| Online Access: | https://dergipark.org.tr/en/download/article-file/4276847 |
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