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Differentiable programming is a fresh programming paradigm which composes parameterized algorithmic components and trains them using automatic differentiation (AD).
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Philippe Corboz, Steven R. White, Guifré Vidal, and Matthias Troyer, “Stripes in the two-dimensional t-J model with infinite projected entangled-pair states,” Phys. Rev. B 84
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2012
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2012
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Wei Li, Jan von Delft, and Tao Xiang, “Efficient simulation of infinite tree tensor network states on the Bethe lattice,” Phys. Rev. B 86
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2013
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2014
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Andrew J Ferris and David Poulin, “Tensor networks and quantum error correction,” Phys. Rev. Lett. 113
2014
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Z. Y. Xie, J. Chen, J. F. Yu, X. Kong, B. Normand, and T. Xiang, “Tensor Renormalization of Quantum Many-Body Systems Using Projected Entangled Simplex States,” Phys. Rev. X 4
2014
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2017
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Shuo Yang, Zheng-Cheng Gu, and Xiao-Gang Wen, “Loop Optimization for Tensor Network Renormalization,” Phys. Rev. Lett. 118
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2017
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Jing Chen, Hai-Jun Liao, Hai-Dong Xie, Xing-Jie Han, Rui-Zhen Huang, Song Cheng, Zhong-Chao Wei, Zhi-Yuan Xie, and Tao Xiang, “Phase Transition of the q-State Clock Model: Duality and Tensor Renormalization,” Chinese Physics Letters 34
2017
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Z. Y. Xie, H. J. Liao, R. Z. Huang, H. D. Xie, J. Chen, Z. Y. Liu, and T. Xiang, “Optimized contraction scheme for tensor-network states,” Phys. Rev. B 96
2017
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H. J. Liao, Z. Y. Xie, J. Chen, Z. Y. Liu, H. D. Xie, R. Z. Huang, B. Normand, and T. Xiang, “Gapless Spin-Liquid Ground State in the S=1 /2 Kagome Antiferromagnet,” Phys. Rev. Lett. 118
2017
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Román Orús, “Tensor networks for complex quantum systems,” arXiv (2018) , arXiv:1812.04011
2018
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E Miles Stoudenmire, “Learning relevant features of data with multi-scale tensor networks,” Quantum Sci. Technol. 3
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Zhao-Yu Han, Jun Wang, Heng Fan, Lei Wang, and Pan Zhang, “Unsupervised Generative Modeling Using Matrix Product States,” Phys. Rev. X 8
2018
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2018
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Teresa Tamayo-Mendoza, Christoph Kreisbeck, Roland Lindh, and Alán Aspuru-Guzik, “Automatic Differentiation in Quantum Chemistry with Applications to Fully Variational Hartree-Fock,” ACS Cent. Sci. 4
2018
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Matthew Johnson, Roy Frostig, and Chris Leary, “Compiling machine learning programs via high-level tracing,” in SysML (2018)
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2018
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Hiroyuki Fujita, Yuya O Nakagawa, Sho Sugiura, and Masaki Oshikawa, “Construction of Hamiltonians by supervised learning of energy and entanglement spectra,” Phys. Rev. B 97
2018
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M. T. Fishman, L. Vanderstraeten, V. Zauner-Stauber, J. Haegeman, and F. Verstraete, “Faster methods for contracting infinite two-dimensional tensor networks,” Phys. Rev. B 98
2018
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2018
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Soeren Laue, Matthias Mitterreiter, and Joachim Giesen, “Computing higher order derivatives of matrix and tensor expressions,” in Advances in Neural Information Processing Systems 31 (2018) pp. 2750–2759
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2019
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Song Cheng, Lei Wang, Tao Xiang, and Pan Zhang, “Tree tensor networks for generative modeling,” Phys. Rev. B 99
2019
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Antoine Tilloy and J. Ignacio Cirac, “Continuous tensor network states for quantum fields,” Phys. Rev. X 9
2019
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