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Quantum Circuit Learning with Error Backpropagation Algorithm and Experimental Implementation

1
Engineering Department, The University of Electro-Communications, Tokyo 182-8585, Japan
2
Grid, Inc., Tokyo 107-0061, Japan
3
i-Powered Energy Research Center (i-PERC), The University of Electro-Communications, Tokyo 182-8585, Japan
*
Author to whom correspondence should be addressed.
Quantum Rep. 2021, 3(2), 333-349; https://doi.org/10.3390/quantum3020021
Received: 23 April 2021 / Revised: 18 May 2021 / Accepted: 24 May 2021 / Published: 28 May 2021
(This article belongs to the Special Issue Exclusive Feature Papers of Quantum Reports)
Quantum computing has the potential to outperform classical computers and is expected to play an active role in various fields. In quantum machine learning, a quantum computer has been found useful for enhanced feature representation and high-dimensional state or function approximation. Quantum–classical hybrid algorithms have been proposed in recent years for this purpose under the noisy intermediate-scale quantum computer (NISQ) environment. Under this scheme, the role played by the classical computer is the parameter tuning, parameter optimization, and parameter update for the quantum circuit. In this paper, we propose a gradient descent-based backpropagation algorithm that can efficiently calculate the gradient in parameter optimization and update the parameter for quantum circuit learning, which outperforms the current parameter search algorithms in terms of computing speed while presenting the same or even higher test accuracy. Meanwhile, the proposed theoretical scheme was successfully implemented on the 20-qubit quantum computer of IBM Q, ibmq_johannesburg. The experimental results reveal that the gate error, especially the CNOT gate error, strongly affects the derived gradient accuracy. The regression accuracy performed on the IBM Q becomes lower with the increase in the number of measurement shot times due to the accumulated gate noise error. View Full-Text
Keywords: quantum computing; machine learning; backpropagation; IBM Q quantum computing; machine learning; backpropagation; IBM Q
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MDPI and ACS Style

Watabe, M.; Shiba, K.; Chen, C.-C.; Sogabe, M.; Sakamoto, K.; Sogabe, T. Quantum Circuit Learning with Error Backpropagation Algorithm and Experimental Implementation. Quantum Rep. 2021, 3, 333-349. https://doi.org/10.3390/quantum3020021

AMA Style

Watabe M, Shiba K, Chen C-C, Sogabe M, Sakamoto K, Sogabe T. Quantum Circuit Learning with Error Backpropagation Algorithm and Experimental Implementation. Quantum Reports. 2021; 3(2):333-349. https://doi.org/10.3390/quantum3020021

Chicago/Turabian Style

Watabe, Masaya, Kodai Shiba, Chih-Chieh Chen, Masaru Sogabe, Katsuyoshi Sakamoto, and Tomah Sogabe. 2021. "Quantum Circuit Learning with Error Backpropagation Algorithm and Experimental Implementation" Quantum Reports 3, no. 2: 333-349. https://doi.org/10.3390/quantum3020021

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