21 citations to https://www.mathnet.ru/eng/entr1
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Markus Schmitt, Zala Lenarčič, “From observations to complexity of quantum states via unsupervised learning”, Phys. Rev. B, 106:4 (2022)
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Shichen Cao, Jingjing Li, Kenric P. Nelson, Mark A. Kon, “Coupled VAE: Improved Accuracy and Robustness of a Variational Autoencoder”, Entropy, 24:3 (2022), 423
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Atithi Acharya, Siddhartha Saha, Anirvan M. Sengupta, “Shadow tomography based on informationally complete positive operator-valued measure”, Phys. Rev. A, 104:5 (2021)
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Juan Carrasquilla, Di Luo, Felipe Pérez, Ashley Milsted, Bryan K. Clark, Maksims Volkovs, Leandro Aolita, “Probabilistic simulation of quantum circuits using a deep-learning architecture”, Phys. Rev. A, 104:3 (2021)
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Japneet Singh, Mathias Scheurer, Vipul Arora, “Conditional generative models for sampling and phase transition indication in spin systems”, SciPost Phys., 11:2 (2021)
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Ilya A. Luchnikov, Mikhail E. Krechetov, Sergey N. Filippov, “Riemannian geometry and automatic differentiation for optimization problems of quantum physics and quantum technologies”, New J. Phys., 23 (2021), 73006–25
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Hee Young Kwon, Han Gyu Yoon, Sung Min Park, Doo Bong Lee, Jun Woo Choi, Changyeon Won, “Magnetic State Generation using Hamiltonian Guided Variational Autoencoder with Spin Structure Stabilization”, Advanced Science, 8:11 (2021)
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Ilia A. Luchnikov, Alexander Ryzhov, Sergey N. Filippov, Henni Ouerdane, “QGOpt: Riemannian optimization for quantum technologies”, SciPost Phys., 10:3 (2021), 79–26
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Huikang Huang, Haozhen Situ, Shenggen Zheng, “Bidirectional Information Flow Quantum State Tomography”, Chinese Phys. Lett., 38:4 (2021), 040303
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B. McNaughton, M. V. Milošević, A. Perali, S. Pilati, “Boosting Monte Carlo simulations of spin glasses using autoregressive neural networks”, Phys. Rev. E, 101:5 (2020)