Difference between revisions of "Quantum Machine Learning models for predicting disease"
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|References=1. Tiwari, P., Dehdashti, S., Obeid, A. K., Marttinen, P., & Bruza, P. (2022). Kernel method based on non-linear coherent states in quantum feature space. Journal of Physics A: Mathematical and Theoretical, 55(35), 355301. | |References=1. Tiwari, P., Dehdashti, S., Obeid, A. K., Marttinen, P., & Bruza, P. (2022). Kernel method based on non-linear coherent states in quantum feature space. Journal of Physics A: Mathematical and Theoretical, 55(35), 355301. | ||
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2. Laxminarayana, N., Mishra, N., Tiwari, P., Garg, S., Behera, B. K., & Farouk, A. (2022). Quantum-Assisted Activation for Supervised Learning in Healthcare-based Intrusion Detection Systems. IEEE Transactions on Artificial Intelligence. | 2. Laxminarayana, N., Mishra, N., Tiwari, P., Garg, S., Behera, B. K., & Farouk, A. (2022). Quantum-Assisted Activation for Supervised Learning in Healthcare-based Intrusion Detection Systems. IEEE Transactions on Artificial Intelligence. | ||
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3. Tiwari, P., & Melucci, M. (2018, October). Towards a quantum-inspired framework for binary classification. In Proceedings of the 27th ACM international conference on information and knowledge management. | 3. Tiwari, P., & Melucci, M. (2018, October). Towards a quantum-inspired framework for binary classification. In Proceedings of the 27th ACM international conference on information and knowledge management. | ||
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4. Zhang, Y., Liu, Y., Li, Q., Tiwari, P., Wang, B., Li, Y., ... & Song, D. (2021). CFN: a complex-valued fuzzy network for sarcasm detection in conversations. IEEE Transactions on Fuzzy Systems, 29(12), 3696-3710. | 4. Zhang, Y., Liu, Y., Li, Q., Tiwari, P., Wang, B., Li, Y., ... & Song, D. (2021). CFN: a complex-valued fuzzy network for sarcasm detection in conversations. IEEE Transactions on Fuzzy Systems, 29(12), 3696-3710. | ||
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| + | 5. Moreira, C., Tiwari, P., Pandey, H. M., Bruza, P., & Wichert, A. (2020). Quantum-like influence diagrams for decision-making. Neural Networks, 132, 190-210. | ||
|Supervisor=Prayag Tiwari | |Supervisor=Prayag Tiwari | ||
|Level=Master | |Level=Master | ||
Latest revision as of 22:58, 2 October 2022
| Title | Quantum Machine Learning models for predicting disease |
|---|---|
| Summary | explore quantum models, including hybrid (classical-quantum), and apply them to different disease prediction tasks |
| Keywords | Quantum Machine Learning, Kernel Methods, Quantum Classifier |
| TimeFrame | ASAP |
| References | 1. Tiwari, P., Dehdashti, S., Obeid, A. K., Marttinen, P., & Bruza, P. (2022). Kernel method based on non-linear coherent states in quantum feature space. Journal of Physics A: Mathematical and Theoretical, 55(35), 355301.
2. Laxminarayana, N., Mishra, N., Tiwari, P., Garg, S., Behera, B. K., & Farouk, A. (2022). Quantum-Assisted Activation for Supervised Learning in Healthcare-based Intrusion Detection Systems. IEEE Transactions on Artificial Intelligence. 3. Tiwari, P., & Melucci, M. (2018, October). Towards a quantum-inspired framework for binary classification. In Proceedings of the 27th ACM international conference on information and knowledge management. 4. Zhang, Y., Liu, Y., Li, Q., Tiwari, P., Wang, B., Li, Y., ... & Song, D. (2021). CFN: a complex-valued fuzzy network for sarcasm detection in conversations. IEEE Transactions on Fuzzy Systems, 29(12), 3696-3710. 5. Moreira, C., Tiwari, P., Pandey, H. M., Bruza, P., & Wichert, A. (2020). Quantum-like influence diagrams for decision-making. Neural Networks, 132, 190-210. |
| Prerequisites | |
| Author | |
| Supervisor | Prayag Tiwari |
| Level | Master |
| Status | Open |
Quantum machine learning is an emerging area that aims to bridge the gap between machine learning and quantum physics. Several quantum algorithms have been proposed and implemented on the quantum circuit in recent years. For example, it is possible to explore quantum kernel methods in deep neural networks. The main goal of this project is to explore quantum models, including hybrid (classical-quantum), and apply them to different disease prediction tasks.
