PreprintArticleVersion 1This version is not peer-reviewed
Quantum-Inspired Data Embedding for Unlabeled Data in Sparse Environments: A Theoretical Framework for Improved Semi-Supervised Learning without Hardware Dependence
Version 1
: Received: 8 October 2024 / Approved: 8 October 2024 / Online: 9 October 2024 (08:38:32 CEST)
How to cite:
Ray, S. Quantum-Inspired Data Embedding for Unlabeled Data in Sparse Environments: A Theoretical Framework for Improved Semi-Supervised Learning without Hardware Dependence. Preprints2024, 2024100663. https://doi.org/10.20944/preprints202410.0663.v1
Ray, S. Quantum-Inspired Data Embedding for Unlabeled Data in Sparse Environments: A Theoretical Framework for Improved Semi-Supervised Learning without Hardware Dependence. Preprints 2024, 2024100663. https://doi.org/10.20944/preprints202410.0663.v1
Ray, S. Quantum-Inspired Data Embedding for Unlabeled Data in Sparse Environments: A Theoretical Framework for Improved Semi-Supervised Learning without Hardware Dependence. Preprints2024, 2024100663. https://doi.org/10.20944/preprints202410.0663.v1
APA Style
Ray, S. (2024). Quantum-Inspired Data Embedding for Unlabeled Data in Sparse Environments: A Theoretical Framework for Improved Semi-Supervised Learning without Hardware Dependence. Preprints. https://doi.org/10.20944/preprints202410.0663.v1
Chicago/Turabian Style
Ray, S. 2024 "Quantum-Inspired Data Embedding for Unlabeled Data in Sparse Environments: A Theoretical Framework for Improved Semi-Supervised Learning without Hardware Dependence" Preprints. https://doi.org/10.20944/preprints202410.0663.v1
Abstract
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 enabling more intricate and expressive embeddings that capture the underlying structure of the data effectively. Grounded in foundational quantum mechanics concepts such as superposition and entanglement, this approach redefines data representation by enabling more intricate and expressive embeddings. Emulating quantum superposition encodes each data point as a probabilistic amalgamation of multiple feature states, facilitating a richer, multidimensional representation of underlying structures and patterns. Additionally, quantum-inspired entanglement mechanisms are harnessed to model intricate dependencies between labeled and unlabeled data, promoting enhanced knowledge transfer and structural inference within the learning paradigm. In contrast to conventional quantum machine learning methodologies that often rely on quantum hardware, this framework is fully realizable within classical computational architectures, thus bypassing the practical limitations of quantum hardware. The versatility of this model is illustrated through its application to critical domains such as medical diagnosis, resource-constrained natural language processing, and financial forecasting—areas where data scarcity impedes the efficacy of traditional models. Experimental evaluations reveal that quantum-inspired embeddings substantially outperform standard approaches, enhancing model resilience and generalization in high-dimensional, low-sample scenarios. This research marks a significant stride in integrating quantum theoretical principles with classical machine learning, broadening the scope of data representation and semi-supervised learning while circumventing the technological barriers of quantum computing infrastructure.
Keywords
Quantum-Inspired Embeddings; Semi-Supervised Learning; Data Scarcity; Medical Diagnosis; Natural Language Processing; Financial Forecasting; High-Dimensional Data; Superposition; Entanglement; Model Robustness; Classification Accuracy; Knowledge Transfer; Graph-Based Approach; Experimental Evaluation; Predictive Accuracy; Probabilistic Representation; Data Representation; Quantum Principles; Machine Learning; Overfitting; Generalization
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
