Advantages of density in tensor network geometries for gradient based training | Paper Citation Record

Observation f4e08824-81d5-4762-9235-2ca0fddce4d8 · outbound

This paper cites Matrix Product States and Projected Entangled Pair States: Concepts, Symmetries, and Theorems.

Advantages of density in tensor network geometries for gradient based training Matrix Product States and Projected Entangled Pair States: Concepts, Symmetries, and Theorems

Reference 1

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Advantages of density in tensor network geometries for gradient based training Unresolved cited work

Reference 2

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Observation c7f6767a-5238-453e-9050-d5a084fe329a · outbound

This paper cites Gray and G.

Advantages of density in tensor network geometries for gradient based training Gray and G

Reference 3

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This paper cites Vidal, Efficient classical simulation of slightly entan- gled quantum computations, Physical Review Letters 91, 10.1103/physrevlett.91.147902 (2003).

Advantages of density in tensor network geometries for gradient based training Vidal, Efficient classical simulation of slightly entan- gled quantum computations, Physical Review Letters 91, 10.1103/physrevlett.91.147902 (2003)

Reference 4

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Observation dbac6b2f-de52-4bd1-9a99-15f3a1736ac9 · outbound

This paper cites Infinite size density matrix renormalization group, revisited.

Advantages of density in tensor network geometries for gradient based training Infinite size density matrix renormalization group, revisited

Reference 5

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Observation 5bc38f53-8ab7-4e51-9c09-f387684e647e · outbound

This paper cites Vidal, Entanglement Renormalization, Physical Re- view Letters 99, 220405 (2007).

Advantages of density in tensor network geometries for gradient based training Vidal, Entanglement Renormalization, Physical Re- view Letters 99, 220405 (2007)

Reference 6

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Observation c20242bb-0148-44f0-ae05-d6337b54b383 · outbound

This paper cites Renormalization algorithms for Quantum-Many Body Systems in two and higher dimensions.

Advantages of density in tensor network geometries for gradient based training Renormalization algorithms for Quantum-Many Body Systems in two and higher dimensions

Reference 7

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Observation 94fc219e-c941-4187-a842-9f8779a18c9b · outbound

This paper cites Beguˇ si´ c, J.

Advantages of density in tensor network geometries for gradient based training Beguˇ si´ c, J

Reference 8

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This paper cites Verstraete, V.

Advantages of density in tensor network geometries for gradient based training Verstraete, V

Reference 9

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Observation 251aed32-1e2b-4831-a45d-0ca5a28fd929 · outbound

This paper cites Schollw¨ ock, The density-matrix renormalization group in the age of matrix product states, Annals of Physics 326, 96–192 (2011).

Advantages of density in tensor network geometries for gradient based training Schollw¨ ock, The density-matrix renormalization group in the age of matrix product states, Annals of Physics 326, 96–192 (2011)

Reference 10

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Observation faf86930-ed2e-40be-8ace-2b10e5b03ba3 · outbound

This paper cites Supervised Learning with Quantum-Inspired Tensor Networks.

Advantages of density in tensor network geometries for gradient based training Supervised Learning with Quantum-Inspired Tensor Networks

Reference 11

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This paper cites Anomaly Detection with Tensor Networks.

Advantages of density in tensor network geometries for gradient based training Anomaly Detection with Tensor Networks

Reference 12

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Observation 1b76b4a0-4521-4a66-be07-93b860a88afb · outbound

This paper cites A Practical Guide to the Numerical Implementation of Tensor Networks I: Contractions, Decompositions and Gauge Freedom.

Advantages of density in tensor network geometries for gradient based training A Practical Guide to the Numerical Implementation of Tensor Networks I: Contractions, Decompositions and Gauge Freedom

Reference 13

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Observation f5b7cf34-7433-4ed4-93ec-8c9027368276 · outbound

This paper cites Zhao, R.-G.

Advantages of density in tensor network geometries for gradient based training Zhao, R.-G

Reference 14

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Observation 70a16402-05f7-4f57-aded-908ae6e40374 · outbound

This paper cites Cervero Mart ´ ın, K.

Advantages of density in tensor network geometries for gradient based training Cervero Mart ´ ın, K

Reference 15

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This paper cites size of largest tensor.

Advantages of density in tensor network geometries for gradient based training size of largest tensor

Reference 16

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Observation d52e9d36-fb1c-4549-a094-e9a5446dff21 · outbound

This paper cites Parallel implementation of the Density Matrix Renormalization Group method achieving a quarter petaFLOPS performance on a single DGX-H100 GPU node.

Advantages of density in tensor network geometries for gradient based training Parallel implementation of the Density Matrix Renormalization Group method achieving a quarter petaFLOPS performance on a single DGX-H100 GPU node

Reference 17

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Observation 3904f02d-db15-4ff4-ba0c-77218be4f328 · outbound

This paper cites Liu, L.-W.

Advantages of density in tensor network geometries for gradient based training Liu, L.-W

Reference 18

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Observation c0478f7f-bdb7-4fcd-b0bf-d8cecde9729f · outbound

This paper cites Horodecki, P.

Advantages of density in tensor network geometries for gradient based training Horodecki, P

Reference 19

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Observation a029eee8-b141-41bc-903d-2fc65f9b239b · outbound

This paper cites Benenti, G.

Advantages of density in tensor network geometries for gradient based training Benenti, G

Reference 20

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This paper cites an unresolved cited work.

Advantages of density in tensor network geometries for gradient based training Unresolved cited work

Reference 21

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Observation 923f1861-1ceb-487c-bc7e-50db410c8784 · outbound

This paper cites Antenna” structure that reduces the distance between sites with respect to an MPS, with- out allowing tensors with more than 3 virtual indices, whereas“Balanced.

Advantages of density in tensor network geometries for gradient based training Antenna” structure that reduces the distance between sites with respect to an MPS, with- out allowing tensors with more than 3 virtual indices, whereas“Balanced

Reference 22

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Observation 5a6bec26-9d7d-42e4-94a4-2f994c1fa5a0 · outbound

This paper cites Multipartite entanglement.

Advantages of density in tensor network geometries for gradient based training Multipartite entanglement

Reference 23

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Observation 2f7c7913-f8c6-4697-a3ba-df7030b598e7 · outbound

This paper cites A Practical Introduction to Tensor Networks: Matrix Product States and Projected Entangled Pair States.

Advantages of density in tensor network geometries for gradient based training A Practical Introduction to Tensor Networks: Matrix Product States and Projected Entangled Pair States

Reference 24

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Observation aa9784e0-12c1-406f-af7a-8136afb371ad · outbound

This paper cites Sharma, P.

Advantages of density in tensor network geometries for gradient based training Sharma, P

Reference 25

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Observation 2c762558-3ba4-41b1-b00c-18b4db91416a · outbound

This paper cites Language Modeling Using Tensor Trains.

Advantages of density in tensor network geometries for gradient based training Language Modeling Using Tensor Trains

Reference 26

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Advantages of density in tensor network geometries for gradient based training Unresolved cited work

Reference 27

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Observation bd314712-76cf-4645-b366-ab71d4094523 · outbound

This paper cites Or´ us, Tensor networks for complex quantum systems, Nature Reviews Physics 1, 538–550 (2019).

Advantages of density in tensor network geometries for gradient based training Or´ us, Tensor networks for complex quantum systems, Nature Reviews Physics 1, 538–550 (2019)

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Observation 2c95f43e-53e5-4e07-99ed-3f51bcf0b7c1 · outbound

This paper cites Affleck, T.

Advantages of density in tensor network geometries for gradient based training Affleck, T

Reference 29

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Observation 79c614d6-01cf-4af8-9406-e581fd0b838d · outbound

This paper cites Evenbly and G.

Advantages of density in tensor network geometries for gradient based training Evenbly and G

Reference 30

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Observation 7a3b8076-a81e-4b83-bc54-a2e52db49f07 · outbound

This paper cites Area laws for the entanglement entropy - a review.

Advantages of density in tensor network geometries for gradient based training Area laws for the entanglement entropy - a review

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Observation 47e4bbf0-e72d-4219-9fd5-8232653c2bb1 · outbound

This paper cites Shi, L.-M.

Advantages of density in tensor network geometries for gradient based training Shi, L.-M

Reference 32

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Observation 0c3d0e5b-328b-4dc5-aac9-4f240b97e4a5 · outbound

This paper cites Vidal, Entanglement renormalization, Phys.

Advantages of density in tensor network geometries for gradient based training Vidal, Entanglement renormalization, Phys

Reference 33

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This paper cites Okunishi, H.

Advantages of density in tensor network geometries for gradient based training Okunishi, H

Reference 34

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This paper cites Haghshenas, M.

Advantages of density in tensor network geometries for gradient based training Haghshenas, M

Reference 35

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This paper cites DMRG Approach to Optimizing Two-Dimensional Tensor Networks.

Advantages of density in tensor network geometries for gradient based training DMRG Approach to Optimizing Two-Dimensional Tensor Networks

Reference 36

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This paper cites Hikihara, H.

Advantages of density in tensor network geometries for gradient based training Hikihara, H

Reference 37

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This paper cites Hikihara, H.

Advantages of density in tensor network geometries for gradient based training Hikihara, H

Reference 38

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This paper cites A Survey on Machine Learning from Few Samples.

Advantages of density in tensor network geometries for gradient based training A Survey on Machine Learning from Few Samples

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Observation c373792a-bb5a-4be3-818b-1847e5cdd036 · outbound

This paper cites Fuksa, M.

Advantages of density in tensor network geometries for gradient based training Fuksa, M

Reference 40

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This paper cites Cerezo, G.

Advantages of density in tensor network geometries for gradient based training Cerezo, G

Reference 41

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This paper cites Memory-Efficient Quantum Circuit Simulation by Using Lossy Data Compression.

Advantages of density in tensor network geometries for gradient based training Memory-Efficient Quantum Circuit Simulation by Using Lossy Data Compression

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Observation f877d4bd-c57a-4fa1-9cec-b92b1da99a70 · outbound

This paper cites Efficient Quantum Circuit Simulation by Tensor Network Methods on Modern GPUs.

Advantages of density in tensor network geometries for gradient based training Efficient Quantum Circuit Simulation by Tensor Network Methods on Modern GPUs

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This paper cites Sanchez-Ramirez, J.

Advantages of density in tensor network geometries for gradient based training Sanchez-Ramirez, J

Reference 44

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Observation eefe2552-d7c3-44d3-a0c6-4da2142de317 · outbound

This paper cites Vidal, Efficient simulation of one-dimensional quantum many-body systems, Phys.

Advantages of density in tensor network geometries for gradient based training Vidal, Efficient simulation of one-dimensional quantum many-body systems, Phys

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Observation 9b93995f-e7f4-4264-aa67-651f03b2fdb5 · outbound

This paper cites Hashizume, J.

Advantages of density in tensor network geometries for gradient based training Hashizume, J

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Advantages of density in tensor network geometries for gradient based training Unresolved cited work

Reference 47

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This paper cites Does provable absence of barren plateaus imply classical simulability?.

Advantages of density in tensor network geometries for gradient based training Does provable absence of barren plateaus imply classical simulability?

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Observation 8a1ebbb8-ced6-4e21-8add-b5276fe15700 · outbound

This paper cites Schuld and N.

Advantages of density in tensor network geometries for gradient based training Schuld and N

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This paper cites On the Trainability and Classical Simulability of Learning Matrix Product States Variationally.

Advantages of density in tensor network geometries for gradient based training On the Trainability and Classical Simulability of Learning Matrix Product States Variationally

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This paper cites Automatic differentiation in machine learning: a survey.

Advantages of density in tensor network geometries for gradient based training Automatic differentiation in machine learning: a survey

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This paper cites Gray, quimb: A python package for quantum informa- tion and many-body calculations, Journal of Open Source Software 3, 819 (2018).

Advantages of density in tensor network geometries for gradient based training Gray, quimb: A python package for quantum informa- tion and many-body calculations, Journal of Open Source Software 3, 819 (2018)

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This paper cites Bradbury, R.

Advantages of density in tensor network geometries for gradient based training Bradbury, R

Reference 53

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Reference 54

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