Systematic Review: Integrating Neural Networks and Quantum Computing using Qiskit
Abstract
This study proposes a systematic review that analyzes the applications and future prospects of integrating neural networks with quantum computing, focusing on one of the most utilized frameworks in the literature, Qiskit. By exploring how Qiskit is used to enhance these integrations, we aim to provide a more detailed understanding of the emerging capabilities and the challenges faced by researchers in the field. The study emphasizes significant results to identify current and future advancements in these areas.
Keywords:
Quantum computing, Neural networks, Qiskit framework, Machine learning, Quantum neural networks, Quantum machine learning
References
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Chen, H.-Y., Chang, Y.-J., Liao, S.-W., and Chang, C.-R. (2024). Deep-q learning with hybrid quantum neural network on solving maze problems. Quantum Machine Intelligence, 6(2):1–8.
IBM (2023). Qiskit. Disponível em: [link]. Acesso em: 03 de julho 2023.
Jiang, W., Xiong, J., and Shi, Y. (2021). When machine learning meets quantum computers: A case study. In Proceedings of the 26th Asia and South Pacific Design Automation Conference, ASPDAC ’21. ACM.
Kumar, S., Dangwal, S., Adhikary, S., and Bhowmik, D. (2021). A quantum activation function for neural networks: Proposal and implementation. In Proceedings of the 2021 International Joint Conference on Neural Networks (IJCNN), pages 1–8. IEEE.
Maurya, S., Mude, C. N., Oliver, W. D., Lienhard, B., and Tannu, S. (2023). Scaling qubit readout with hardware efficient machine learning architectures. In Proceedings of the 50th Annual International Symposium on Computer Architecture, ISCA ’23, pages 1–13. ACM.
Nielsen, M. A. and Chuang, I. L. (2000). Quantum Computation and Quantum Information. Cambridge University Press.
Patel, H., Kamthekar, S., Prajapati, D., and Agarwal, R. (2024). Quantum inspired image classification: A hybrid svm framework. In Proceedings of the 2024 International Conference on Emerging Smart Computing and Informatics (ESCI), pages 1–7. IEEE.
Rahman, M. A., Shahriar, H., Clincy, V., Hossain, M. F., and Rahman, M. (2023). A quantum generative adversarial network-based intrusion detection system. In 2023 IEEE 47th Annual Computers, Software, and Applications Conference (COMPSAC), pages 1810–1815. IEEE.
Shahwar, T., Zafar, J., Almogren, A., Zafar, H., Rehman, A. U., Shafiq, M., and Hamam, H. (2022). Automated detection of alzheimer’s via hybrid classical quantum neural networks. Electronics, 11(5).
Published
2024-11-17
How to Cite
SCHONHOFEN, Larissa; BOTELHO, Cecília; LUCCA, Giancarlo; YAMIN, Adenauer; SANTOS, Helida; REISER, Renata.
Systematic Review: Integrating Neural Networks and Quantum Computing using Qiskit. In: NATIONAL MEETING ON ARTIFICIAL AND COMPUTATIONAL INTELLIGENCE (ENIAC), 21. , 2024, Belém/PA.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2024
.
p. 870-881.
ISSN 2763-9061.
DOI: https://doi.org/10.5753/eniac.2024.245111.
