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TensorNetworks

Guides

  • TensorNetworks

Tech Notes

  • Building Tensor Networks
  • Contraction Paths and Execution
  • Matrix Product States
  • Tensor Networks Overview
  • Young Tableaux and Tensor Symmetries

Symbols

  • ActivateTensors
  • BinaryTensorNetwork
  • BinaryTensorNetworkQ
  • CanonicalPath
  • CanonicalPathQ
  • ContractIndices
  • ContractionTree
  • EinsteinSummation
  • GreedyContractionPath
  • HookFactor
  • HookLength
  • HookLengths
  • IndexedMultiply
  • InitializeTensorNetwork
  • MetricTensor
  • MetricTensorQ
  • MPSCanonicalForm
  • MPSCanonicalQ
  • MPSEntanglementEntropy
  • MPSNormalize
  • MPSNorm
  • MPSOverlap
  • MPSSchmidtValues
  • MPSTruncate
  • OptimalContractionPath
  • PartitionQ
  • PathIndexContractions
  • PathQ
  • PathToTreePath
  • RandomTensorNetwork
  • SparseTensorNetwork
  • TableauColumns
  • TableauDimension
  • TableauRows
  • TableauShape
  • TableauSize
  • TensorNetworkAdd
  • TensorNetworkContraction
  • TensorNetworkContractions
  • TensorNetworkContract
  • TensorNetworkData
  • TensorNetworkDelete
  • TensorNetworkFreeIndices
  • TensorNetworkGraphData
  • TensorNetworkGraphQ
  • TensorNetworkIndexDimensions
  • TensorNetworkIndexGraph
  • TensorNetworkIndices
  • TensorNetwork
  • TensorNetworkQ
  • TensorNetworkRemoveCycles
  • TensorNetworkReplaceIndices
  • TensorNetworkSize
  • TensorNetworkTensors
  • TensorNetworkToNetGraph
  • ToTensorNetworkGraph
  • TransposePartition
  • TreePathQ
  • TreePathToPath
  • YoungProject
  • YoungSymmetrize
  • YoungTableau
  • YoungTableauQ
Wolfram`TensorNetworks`
MPSNorm
​
MPSNorm
[mps]
computes the norm
ψ
of a matrix-product state.
​
Details and Options
​
Examples  
(11)
Basic Examples  
(3)
Set a seed for reproducibility:
In[1]:=
SeedRandom[42];
Build a length-6, bond-4, physical-2 MPS:
In[2]:=
mps=RandomTensorNetwork["MPS"[6,4,2]];
Compute its norm:
In[3]:=
MPSNorm[mps]
Out[3]=
8.32729
​
The squared norm equals the self-overlap:
In[1]:=
mps=RandomTensorNetwork["MPS"[6,4,2]];​​Chop[MPSNorm[mps]^2-MPSOverlap[mps,mps]]
Out[1]=
0
​
A normalized MPS has unit norm:
In[1]:=
mps=RandomTensorNetwork["MPS"[6,4,2]];​​MPSNorm[MPSNormalize[mps]]
Out[1]=
1.
Scope  
(3)

Applications  
(2)

Properties & Relations  
(3)

SeeAlso
MPSOverlap
 
▪
MPSNormalize
 
▪
MPSCanonicalForm
 
▪
MPSSchmidtValues
 
▪
RandomTensorNetwork
 
▪
Norm
 
▪
Sqrt
 
▪
Abs
TechNotes
▪
MPS Algorithms
RelatedGuides
▪
TensorNetworks
""

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