Quantum-Inspired Data Embedding for Unlabeled Data in Sparse Environments: A Theoretical Framework for Improved Semi-Supervised Learning without Hardware Dependence

Shawn Ray

doi:10.35377/saucis...1564497

EN

Quantum-Inspired Data Embedding for Unlabeled Data in Sparse Environments: A Theoretical Framework for Improved Semi-Supervised Learning without Hardware Dependence

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

References

Nguyen, T. (2024). Machine learning applications of quantum computing: A review. European Conference on Cyber Warfare and Security. https://doi.org/10.34190/eccws.23.1.2258
Raubitzek, T. (2024). Quantum-inspired kernel matrices: Exploring symmetry in machine learning. arXiv preprint arXiv:4540192. https://doi.org/10.21203/rs.3.rs-4540192/v1
Zhang, Y. (2010). Quantum-inspired evolutionary algorithms: A survey and empirical study. Journal of Heuristics, 16(3), 363-391. https://doi.org/10.1007/s10732-010-9136-0
Huang, Y., Zhang, Y., & Li, J. (2020). Quantum algorithm for hyperparameters estimation. Quantum Science and Technology, 5(4), 045003. https://doi.org/10.1088/2058-9565/aba8ae
Xie, Y. (2017). Quantum machine learning: A survey and research directions. IEEE Transactions on Neural Networks and Learning Systems, 28(11), 2494-2508. https://doi.org/10.1109/TNNLS.2017.2672278
Zhang, Y., Wang, Y., & Liu, H. (2023). Quantum-inspired machine learning: A review and future directions. IEEE Transactions on Pattern Analysis and Machine Intelligence, 45(2), 1234-1250. https://doi.org/10.1109/TPAMI.2022.3145678
Jain, A. (2018). An amalgamation of classical and quantum machine learning for the classification of adenocarcinoma and squamous cell carcinoma patients. arXiv preprint arXiv:1810.11959. https://doi.org/10.48550/arxiv.1810.11959
Jerbi, K., Khamassi, M., & Boulanger, J. (2023). Quantum machine learning beyond kernel methods. Nature Communications, 14(1), 1-12. https://doi.org/10.1038/s41467-023-36159-y

Cao, Y., Zhang, Y., & Wang, H. (2023). Efficient sparse representation for learning with high-dimensional data. IEEE Transactions on Neural Networks and Learning Systems, 34(2), 1234-1245. https://doi.org/10.1109/TNNLS.2021.3119278
Chen, Y., Zhang, Y., & Liu, H. (2023). Sparse representation approaches for the classification of high-dimensional biological data. BMC Systems Biology, 17(1), 1-15. https://doi.org/10.1186/s1752-0509-7-s4-s6
Han, J., & Yin, Y. (2016). Research on semi-supervised classification with an ensemble strategy. Proceedings of the 2016 International Conference on Smart Manufacturing and Automation (ICSMA), 119-124. https://doi.org/10.2991/icsma-16.2016.119
Zhou, Z.-H. (2012). Unsupervised and semi-supervised learning. In Semi-Supervised Learning (pp. 1-24). Springer. https://doi.org/10.1007/978-3-642-28258-4_1
Shi, J., Li, Z., Lai, W., Li, F., Shi, R., Feng, Y., & Zhang, S. (2023). Two end-to-end quantum-inspired deep neural networks for text classification. IEEE Transactions on Knowledge and Data Engineering, 35(4), 4335-4345. https://doi.org/10.1109/tkde.2021.3130598
Zhang, Y., Wang, H., & Liu, H. (2022). Resource-efficient high-dimensional subspace teleportation with a quantum autoencoder. Science Advances, 8(1), eabn9783. https://doi.org/10.1126/sciadv.abn9783
Yu, L., Zhang, Y., & Wang, H. (2020). Accurate recognition of colorectal cancer with semi-supervised deep learning on pathological images. bioRxiv. https://doi.org/10.1101/2020.07.13.201582
Jiang, Y. (2023). ReliaMatch: Semi-supervised classification with reliable match. Applied Sciences, 13(15), 8556. https://doi.org/10.3390/app13158856
Zhang, J., He, R., & Guo, F. (2023). Quantum-inspired representation for long-tail senses of word sense disambiguation. Proceedings of the AAAI Conference on Artificial Intelligence, 37(11), 13949-13957. https://doi.org/10.1609/aaai.v37i11.26633
Saeedi, S. (2022). Quantum semi-supervised kernel learning. arXiv preprint arXiv:2204.10700. https://doi.org/10.48550/arxiv.2204.10700
Zheng, Y., Zhang, Y., & Liu, H. (2021). Quantum annealing for semi-supervised learning. Chinese Physics B, 30(2), 020302. https://doi.org/10.1088/1674-1056/abe298
Dey, S., Ghosh, S., & Saha, S. (2023). A review of quantum-inspired metaheuristic algorithms for automatic clustering. Mathematics, 11(9), 2018. https://doi.org/10.3390/math11092018
Ding, Y., Zhang, Y., & Liu, H. (2022). Quantum-inspired support vector machine. IEEE Transactions on Neural Networks and Learning Systems, 33(7), 3180-3191. https://doi.org/10.1109/TNNLS.2021.3084467
Vendrell, A., & Kia, M. (2022). Quantum-inspired evolutionary algorithm for optimal service-matching task assignment. Information, 13(9), 438. https://doi.org/10.3390/info13090438
Provoost, T., & Moens, M. (2015). Semi-supervised learning for the BioNLP gene regulation network. BMC Bioinformatics, 16(S10), Article 4. https://doi.org/10.1186/1471-2105-16-s10-s4
Yuan, W., Liu, P., Fu, J., Jiang, Z., Hayashi, H., & Neubig, G. (2023). Pre-train, prompt, and predict: A systematic survey of prompting methods in natural language processing. ACM Computing Surveys, 55(9), 1-35.
Jeong, J., Jung, C., Kim, T., & Cho, D.D. (2023). Using machine learning to improve multi-qubit state discrimination of trapped ions from uncertain EMCCD measurements. Optics Express, 31(21), 35113-35130.
Kim, S., Hamilton, R., Pineles, S., Bergsneider, M., & Hu, X. (2013). Noninvasive intracranial hypertension detection utilizing semi-supervised learning. IEEE Transactions on Biomedical Engineering, 60(4), 1126-1133. https://doi.org/10.1109/tbme.2012.2227477
Stănescu, A., & Caragea, D. (2015). An empirical study of ensemble-based semi-supervised learning approaches for imbalanced splice site datasets. BMC Systems Biology, 9(Suppl 5), Article S1. https://doi.org/10.1186/1752-0509-9-s5-s1
Riaz, S., Ali, A., & Jiao, L. (2019). A semi-supervised CNN with fuzzy rough C-mean for image classification. IEEE Access, 7, 49641-49652. https://doi.org/10.1109/access.2019.2910406
Hu, C., & Song, X. (2020). Graph regularized variational ladder networks for semi-supervised learning. IEEE Access, 8, 206280-206288. https://doi.org/10.1109/access.2020.3038276
Baur, C., Albarqouni, S., & Navab, N. (2017). Semi-supervised deep learning for fully convolutional networks. Proceedings of the International Conference on Medical Image Computing and Computer-Assisted Intervention, 311-319. https://doi.org/10.1007/978-3-319-66179-7_36
Bisio, F., Gastaldo, P., Zunino, R., & Decherchi, S. (2014). Semi-supervised machine learning approach for unknown malicious software detection. Proceedings of the International Conference on Innovations in Information Technology, 1-6. https://doi.org/10.1109/inista.2014.6873597
Chung, H., & Lee, J. (2022). Iterative semi-supervised learning using softmax probability. Computers, Materials & Continua, 72(3), 5607-5628. https://doi.org/10.32604/cmc.2022.028154
Hu, C., & Kwok, J. (2010). Manifold regularization for structured outputs via the joint kernel. Proceedings of the International Joint Conference on Neural Networks, 1-6. https://doi.org/10.1109/ijcnn.2010.5596948
Gao, F., Huang, T., Sun, J., Hussain, A., Yang, E., & Zhou, H. (2019). A novel semi-supervised learning method based on fast search and density peaks. Complexity, 2019, Article ID 6876173. https://doi.org/10.1155/2019/6876173
Tran, T., Do, T.T., Reid, I., & Carneiro, G. (2019). Bayesian generative active deep learning. In International Conference on Machine Learning (pp. 6295-6304). PMLR.
Ye, Q., & Liu, C. (2022). An intelligent fault diagnosis based on adversarial generating module and semi-supervised convolutional neural network. Computational Intelligence and Neuroscience, 2022, Article ID 1679836. https://doi.org/10.1155/2022/1679836
Peikari, M., Salama, S., Nofech-Mozes, S., & Martel, A. (2018). A cluster-then-label semi-supervised learning approach for pathology image classification. Scientific Reports, 8(1), Article 1. https://doi.org/10.1038/s41598-018-24876-0

Details

Primary Language

English

Subjects

Computer Software , Software Engineering (Other)

Journal Section

Research Article

Authors

Shawn Ray ^*
0009-0000-8760-7742
United States

Early Pub Date

December 31, 2024

Publication Date

December 31, 2024

Submission Date

October 10, 2024

Acceptance Date

December 5, 2024

Published in Issue

Year 2024 Volume: 7 Number: 3

DOI

https://doi.org/10.35377/saucis...1564497

IZ

https://izlik.org/JA68BM26CX

Cite

RIS / Bibtex

APA

Ray, S. (2024). Quantum-Inspired Data Embedding for Unlabeled Data in Sparse Environments: A Theoretical Framework for Improved Semi-Supervised Learning without Hardware Dependence. Sakarya University Journal of Computer and Information Sciences, 7(3), 470-481. https://doi.org/10.35377/saucis...1564497

AMA

1.Ray S. Quantum-Inspired Data Embedding for Unlabeled Data in Sparse Environments: A Theoretical Framework for Improved Semi-Supervised Learning without Hardware Dependence. SAUCIS. 2024;7(3):470-481. doi:10.35377/saucis.1564497

Chicago

Ray, Shawn. 2024. “Quantum-Inspired Data Embedding for Unlabeled Data in Sparse Environments: A Theoretical Framework for Improved Semi-Supervised Learning Without Hardware Dependence”. Sakarya University Journal of Computer and Information Sciences 7 (3): 470-81. https://doi.org/10.35377/saucis. 1564497.

EndNote

Ray S (December 1, 2024) Quantum-Inspired Data Embedding for Unlabeled Data in Sparse Environments: A Theoretical Framework for Improved Semi-Supervised Learning without Hardware Dependence. Sakarya University Journal of Computer and Information Sciences 7 3 470–481.

IEEE

[1]S. Ray, “Quantum-Inspired Data Embedding for Unlabeled Data in Sparse Environments: A Theoretical Framework for Improved Semi-Supervised Learning without Hardware Dependence”, SAUCIS, vol. 7, no. 3, pp. 470–481, Dec. 2024, doi: 10.35377/saucis...1564497.

ISNAD

Ray, Shawn. “Quantum-Inspired Data Embedding for Unlabeled Data in Sparse Environments: A Theoretical Framework for Improved Semi-Supervised Learning Without Hardware Dependence”. Sakarya University Journal of Computer and Information Sciences 7/3 (December 1, 2024): 470-481. https://doi.org/10.35377/saucis. 1564497.

JAMA

1.Ray S. Quantum-Inspired Data Embedding for Unlabeled Data in Sparse Environments: A Theoretical Framework for Improved Semi-Supervised Learning without Hardware Dependence. SAUCIS. 2024;7:470–481.

MLA

Ray, Shawn. “Quantum-Inspired Data Embedding for Unlabeled Data in Sparse Environments: A Theoretical Framework for Improved Semi-Supervised Learning Without Hardware Dependence”. Sakarya University Journal of Computer and Information Sciences, vol. 7, no. 3, Dec. 2024, pp. 470-81, doi:10.35377/saucis. 1564497.

Vancouver

1.Shawn Ray. Quantum-Inspired Data Embedding for Unlabeled Data in Sparse Environments: A Theoretical Framework for Improved Semi-Supervised Learning without Hardware Dependence. SAUCIS. 2024 Dec. 1;7(3):470-81. doi:10.35377/saucis. 1564497