Machine learning distributions of quantum ansatz with hierarchical structure
2020 ◽
Vol 34
(20)
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pp. 2050196
Keyword(s):
Machine learning techniques can help to represent and solve quantum systems. Learning measurement outcome distribution of quantum ansatz is useful for characterization of near-term quantum computing devices. In this work, we use the popular unsupervised machine learning model, variational autoencoder (VAE), to reconstruct the measurement outcome distribution of quantum ansatz. The number of parameters in the VAE are compared with the number of measurement outcomes. The numerical results show that VAE can efficiently learn the measurement outcome distribution with few parameters. The influence of entanglement on the task is also revealed.
2018 ◽
Vol 34
(10)
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pp. e3121
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2020 ◽
Vol 9
(6)
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pp. 379
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2021 ◽
Vol 2021
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pp. 1-10
2020 ◽
Vol 9
(3)
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pp. 1223-1225
2020 ◽
Vol 9
(4)
◽
pp. 2412-2417
2019 ◽
Vol 6
(4)
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pp. 739-747
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