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Tensor network (TN), a young mathematical tool of high vitality and great potential, has been undergoing extremely rapid developments in the last two decades, gaining tremendous success in condensed matter physics, atomic physics, quantum information science, statistical physics, and so on.
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On the best rank-1 and rank-( R 1 R_{1} , R 2 R_{2} ,…, R N R_{N} ) approximation of higher-order tensors
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T. Xiang, J.-Z. Lou, and Z.-B. Su · 2001
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Two-dimensional tensor product variational formulation
T. Nishino, Y. Hieida, K. Okunishi, N. Maeshima, Y. Akutsu, and A. Gendiar · 2001
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A. O. Mitrushenkov, G. Fano, F. Ortolani, R. Linguerri, and P. Palmieri · 2001
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E. Jeckelmann · 2002
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A. Peres · 2002
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Latent heat calculation of the three-dimensional q= 3, 4, and 5 Potts models by the tensor product variational approach
A. Gendiar and T. Nishino · 2002
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Efficient classical simulation of slightly entangled quantum computations
G. Vidal · 2003
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Efficient classical simulation of slightly entangled quantum computations
G. Vidal · 2003
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M. B. Hastings · 2004
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G. Vidal · 2004
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Diverging entanglement length in gapped quantum spin systems
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