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Quantum Federated Learning Experiments in the Cloud with Data Encoding
Published 1 May 2024 in cs.LG, cs.ET, and quant-ph | (2405.00909v1)
Abstract: Quantum Federated Learning (QFL) is an emerging concept that aims to unfold federated learning (FL) over quantum networks, enabling collaborative quantum model training along with local data privacy. We explore the challenges of deploying QFL on cloud platforms, emphasizing quantum intricacies and platform limitations. The proposed data-encoding-driven QFL, with a proof of concept (GitHub Open Source) using genomic data sets on quantum simulators, shows promising results.
- S. S. Gill, A. Kumar, H. Singh, M. Singh, K. Kaur, M. Usman, and R. Buyya, “Quantum computing: A taxonomy, systematic review and future directions,” Software: Practice and Experience, vol. 52, no. 1, pp. 66–114, 2022.
- H. T. Larasati, M. Firdaus, and H. Kim, “Quantum federated learning: Remarks and challenges,” in 2022 IEEE 9th International Conference on Cyber Security and Cloud Computing (CSCloud)/2022 IEEE 8th International Conference on Edge Computing and Scalable Cloud (EdgeCom), pp. 1–5, 2022.
- M. Chehimi and W. Saad, “Quantum federated learning with quantum data,” in ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 8617–8621, 2022.
- M. C. Caro, H.-Y. Huang, M. Cerezo, K. Sharma, A. Sornborger, L. Cincio, and P. J. Coles, “Generalization in quantum machine learning from few training data,” Nature communications, vol. 13, no. 1, p. 4919, 2022.
- S. R. Pokhrel and J. Choi, “Federated learning with blockchain for autonomous vehicles: Analysis and design challenges,” IEEE Transactions on Communications, vol. 68, no. 8, pp. 4734–4746, 2020.
- P. W. Shor, “Algorithms for quantum computation: Discrete logarithms and factoring,” in Proceedings 35th Annual Symposium on Foundations of Computer Science, pp. 124–134, 1994.
- S. L. et al., “Pricing plans - azure quantum.” https://learn.microsoft.com/en-us/azure/quantum/pricing.
- Amazon, “Amazon braket pricing.” https://aws.amazon.com/braket/pricing/.
- IonQ Staff, “The value of classical quantum simulators,” March 2023.
- M. Kashif and S. Al-Kuwari, “Qiskit as a simulation platform for measurement-based quantum computation,” in 2022 IEEE 19th International Conference on Software Architecture Companion (ICSA-C), pp. 152–159, 2022.
- I. Cong, S. Choi, and M. D. Lukin, “Quantum convolutional neural networks,” Nature Physics, vol. 15, no. 12, pp. 1273–1278, 2019.
- H. Yano, Y. Suzuki, K. M. Itoh, R. Raymond, and N. Yamamoto, “Efficient discrete feature encoding for variational quantum classifier,” IEEE Transactions on Quantum Engineering, vol. 2, pp. 1–14, 2021.
- B. McMahan, E. Moore, D. Ramage, S. Hampson, and B. A. y Arcas, “Communication-efficient learning of deep networks from decentralized data,” in Artificial intelligence and statistics, pp. 1273–1282, PMLR, 2017.
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