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Yıl 2024, Cilt: 7 Sayı: 3, 470 - 481, 31.12.2024

Shawn Ray

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

Öz

Kaynakça

  • 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

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

Yıl 2024, Cilt: 7 Sayı: 3, 470 - 481, 31.12.2024

Shawn Ray

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

Öz

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.

Anahtar Kelimeler

Quantum-Inspired Data Embedding High-Dimensional Data Entangled Representations

Kaynakça

  • 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
Toplam 37 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Bilgisayar Yazılımı, Yazılım Mühendisliği (Diğer)
Bölüm Research Article
Yazarlar

Shawn Ray 0009-0000-8760-7742

Erken Görünüm Tarihi 31 Aralık 2024
Yayımlanma Tarihi 31 Aralık 2024
Gönderilme Tarihi 10 Ekim 2024
Kabul Tarihi 5 Aralık 2024
Yayımlandığı Sayı Yıl 2024 Cilt: 7 Sayı: 3

Kaynak Göster

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 Ray S. Quantum-Inspired Data Embedding for Unlabeled Data in Sparse Environments: A Theoretical Framework for Improved Semi-Supervised Learning without Hardware Dependence. SAUCIS. Aralık 2024;7(3):470-481. doi:10.35377/saucis.1564497
Chicago 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, sy. 3 (Aralık 2024): 470-81. https://doi.org/10.35377/saucis. 1564497.
EndNote Ray S (01 Aralık 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 S. Ray, “Quantum-Inspired Data Embedding for Unlabeled Data in Sparse Environments: A Theoretical Framework for Improved Semi-Supervised Learning without Hardware Dependence”, SAUCIS, c. 7, sy. 3, ss. 470–481, 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 (Aralık 2024), 470-481. https://doi.org/10.35377/saucis. 1564497.
JAMA 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, c. 7, sy. 3, 2024, ss. 470-81, doi:10.35377/saucis. 1564497.
Vancouver 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):470-81.
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