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We demonstrate the use of tensor networks for image classification with the TensorNetwork open source library.
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Z.-Z. Sun et al., Generative Tensor Network Classification Model for Supervised Machine Learning
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C. Roberts et al. TensorNetwork: A Library for Physics and Machine Learning
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F. Verstraete, and J. I. Cirac, Renormalization algorithms for Quantum-Many Body Systems in two and higher dimensions
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Y. Shi, L. Duan, and G. Vidal, Classical simulation of quantum many-body systems with a tree tensor network
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D. Perez-Garcia, F. Verstraete, M. M.Wolf, and J. I. Cirac, Matrix Product State Representations
2007
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G. Vidal, Entanglement renormalization
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M. Levin, C. P. Nave, Tensor renormalization group approach to 2D classical lattice models
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G. Vidal, A class of quantum many-body states that can be efficiently simulated
2008
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G. Evenbly, G. Vidal, Algorithms for entanglement renormalization, Phys. Rev. B 79, 144108 (2009), arXiv: 0707.1454
2009
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2015
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G. Evenbly, G. Vidal, Tensor Network Renormalization
2015
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M. Abadi et al. TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems
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B. Czech, L. Lamprou, S. McCandlish, and J. Sully, Tensor Networks from Kinematic Space
2016
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2009
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J. I. Cirac, F. Verstraete, Renormalization and tensor product states in spin chains and lattices
2009
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2009
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2010
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F. Verstraete and J. I. Cirac, Continuous Matrix Product States for Quantum Fields
2010
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G. Evenbly, G. Vidal, Tensor network states and geometry
2011
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B. Swingle, Entanglement Renormalization and Holography
2012
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E. M. Stoudenmire, D. J. Schwab, Supervised Learning with Quantum-Inspired Tensor Networks
2016
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2017
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2017
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2017
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2018
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E. M. Stoudenmire Learning Relevant Features of Data with Multi-scale Tensor Networks
2018
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G. Evenbly, Gauge fixing, canonical forms, and optimal truncations in tensor networks with closed loops
2018
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Y. Levine, O. Sharir, N. Cohen, A. Shashua, Quantum entanglement in deep learning architectures
2019
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J. Miller, TorchMPS
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