Wolfram Language Paclet Repository

Community-contributed installable additions to the Wolfram Language

Primary Navigation

    • Cloud & Deployment
    • Core Language & Structure
    • Data Manipulation & Analysis
    • Engineering Data & Computation
    • External Interfaces & Connections
    • Financial Data & Computation
    • Geographic Data & Computation
    • Geometry
    • Graphs & Networks
    • Higher Mathematical Computation
    • Images
    • Knowledge Representation & Natural Language
    • Machine Learning
    • Notebook Documents & Presentation
    • Scientific and Medical Data & Computation
    • Social, Cultural & Linguistic Data
    • Strings & Text
    • Symbolic & Numeric Computation
    • System Operation & Setup
    • Time-Related Computation
    • User Interface Construction
    • Visualization & Graphics
    • Random Paclet
    • Alphabetical List
  • Using Paclets
    • Get Started
    • Download Definition Notebook
  • Learn More about Wolfram Language

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`
MPSNormalize
​
MPSNormalize
[mps]
rescales the MPS
mps
to unit norm by dividing every tensor by
MPSNorm
[mps]
1/n
​
.
​
Details and Options
Examples  
(6)
Scope  
(3)
From an arbitrary MPS  
(1)
Generate a small three-site MPS and inspect its norm:
In[1]:=
mpsSmall=BlockRandom[SeedRandom[7];RandomTensorNetwork["MPS"[3,2,2]]]
In[2]:=
MPSNorm[mpsSmall]
Out[2]=
1.45605
​
MPSNormalize
returns a new
TensorNetwork
object that represents the rescaled state:
In[1]:=
mpsSmall=BlockRandom[SeedRandom[7];RandomTensorNetwork["MPS"[3,2,2]]];​​MPSNormalize[mpsSmall]
Out[1]=
TensorNetwork
Tensors: 3
Binary: Yes
Free indices: 3
Sparse: No
Output dimension: 8
​

​
The norm of the rescaled object is 1:
In[1]:=
mpsSmall=BlockRandom[SeedRandom[7];RandomTensorNetwork["MPS"[3,2,2]]];​​MPSNorm[MPSNormalize[mpsSmall]]
Out[1]=
1.
Per-site rescale factor  
(1)

Zero-norm MPS unchanged  
(1)

Applications  
(2)

Properties & Relations  
(1)

SeeAlso
MPSNorm
 
▪
MPSOverlap
 
▪
MPSCanonicalForm
 
▪
MPSCanonicalQ
 
▪
MPSSchmidtValues
 
▪
RandomTensorNetwork
 
▪
BinaryTensorNetwork
TechNotes
▪
MPS Algorithms
RelatedGuides
▪
TensorNetworks
Build a six-site MPS with bond dimension 4 and physical dimension 2, then rescale it to unit norm. The result is a new TensorNetwork object with the same tensor shapes:
In[1]:=
mps=BlockRandom[SeedRandom[42];RandomTensorNetwork["MPS"[6,4,2]]]
In[2]:=
MPSNormalize[mps]
Out[2]=
TensorNetwork
Tensors: 6
Binary: Yes
Free indices: 6
Sparse: No
Output dimension: 64
​

​
Rescale a smaller three-site MPS in the same way:
In[3]:=
mpsSmall=BlockRandom[SeedRandom[7];RandomTensorNetwork["MPS"[3,2,2]]]
In[4]:=
MPSNormalize[mpsSmall]
Out[4]=
TensorNetwork
Tensors: 3
Binary: Yes
Free indices: 3
Sparse: No
Output dimension: 8
​

​
The output is an MPS regardless of the bond and physical dimensions of the input. Take an MPS with bond dimension 6 and physical dimension 3:
In[5]:=
mpsLarger=BlockRandom[SeedRandom[3];RandomTensorNetwork["MPS"[4,6,3]]]
In[6]:=
MPSNormalize[mpsLarger]
Out[6]=
TensorNetwork
Tensors: 4
Binary: Yes
Free indices: 4
Sparse: No
Output dimension: 81
​

​
The rescaled state has unit norm under
MPSNorm
:
In[7]:=
mps=BlockRandom[SeedRandom[42];RandomTensorNetwork["MPS"[6,4,2]]];​​MPSNorm[MPSNormalize[mps]]
Out[7]=
1.
""

© 2026 Wolfram. All rights reserved.

  • Legal & Privacy Policy
  • Contact Us
  • WolframAlpha.com
  • WolframCloud.com