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Paper 1
Q-Pandora Unboxed: Characterizing Noise Resilience of Quantum Error Correction Codes
Chatterjee A, Das S, Ghosh S.
- Year
- 2023
- Journal
- Europe PMC
- DOI
- 10.21203/rs.3.rs-3663236/v1
- arXiv
- -
No abstract.
Open paperPaper 2
Communication-efficient Quantum Algorithm for Distributed Machine Learning
Hao Tang, Boning Li, Guoqing Wang, Haowei Xu, Changhao Li, Ariel Barr, Paola Cappellaro, Ju Li
- Year
- 2022
- Journal
- arXiv preprint
- DOI
- arXiv:2209.04888
- arXiv
- 2209.04888
The growing demands of remote detection and increasing amount of training data make distributed machine learning under communication constraints a critical issue. This work provides a communication-efficient quantum algorithm that tackles two traditional machine learning problems, the least-square fitting and softmax regression problem, in the scenario where the data set is distributed across two parties. Our quantum algorithm finds the model parameters with a communication complexity of $O(\frac{\log_2(N)}ε)$, where $N$ is the number of data points and $ε$ is the bound on parameter errors. Compared to classical algorithms and other quantum algorithms that achieve the same output task, our algorithm provides a communication advantage in the scaling with the data volume. The building block of our algorithm, the quantum-accelerated estimation of distributed inner product and Hamming distance, could be further applied to various tasks in distributed machine learning to accelerate communication.
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