Package: tensornetwork
A tensor network wrapper for TensorFlow, JAX, PyTorch, and Numpy.For an overview of tensor networks please see the following:Matrices as Tensor Network DiagramsCrash Course in Tensor Networks (video)Hand-waving and interpretive dance: an introductory course on tensor networksTensor Networks in a NutshellA Practical Introduction to Tensor NetworksMore information can be found in our TensorNetwork papers:TensorNetwork: A Library for Physics and Machine LearningTensorNetwork on TensorFlow: A Spin Chain Application Using Tree Tensor NetworksTensorNetwork on TensorFlow: Entanglement Renormalization for quantum critical lattice modelsTensorNetwork for Machine LearningInstallationpip3 install tensornetworkDocumentationFor details about the TensorNetwork API, see thereference documentation.TutorialsBasic API tutorialTensor Networks inside Neural Networks using KerasBasic ExampleHere, we build a simple 2 node contraction.importnumpyasnpimporttensornetworkastn# Create the nodesa=tn.Node(np.ones((10,)))b=tn.Node(np.ones((10,)))edge=a[0]^b[0]# Equal to tn.connect(a[0], b[0])final_node=tn.contract(edge)print(final_node.tensor)# Should print 10.0Optimized Contractions.Usually, it is more computationally effective to flatten parallel edges before contracting them in order to avoid trace edges.
We havecontract_betweenandcontract_parallelthat do this automatically for your convenience.# Contract all of the edges between a and b# and create a new node `c`.c=tn.contract_between(a,b)# This is the same as above, but much shorter.c=a@b# Contract all of edges that are parallel to edge# (parallel means connected to the same nodes).c=tn.contract_parallel(edge)Split NodeYou can split a node by doing a singular value decomposition.# This will return two nodes and a tensor of the truncation error.# The two nodes are the unitary matrices multiplied by the square root of the# singular values.# The `left_edges` are the edges that will end up on the `u_s` node, and `right_edges`# will be on the `vh_s` node.u_s,vh_s,trun_error=tn.split_node(node,left_edges,right_edges)# If you want the singular values in it's own node, you can use `split_node_full_svd`.u,s,vh,trun_error=tn.split_node_full_svd(node,left_edges,right_edges)Node and Edge names.You can optionally name your nodes/edges. This can be useful for debugging,
as all error messages will print the name of the broken edge/node.node=tn.Node(np.eye(2),name="Identity Matrix")print("Name of node:{}".format(node.name))edge=tn.connect(node[0],node[1],name="Trace Edge")print("Name of the edge:{}".format(edge.name))# Adding name to a contraction will add the name to the new edge created.final_result=tn.contract(edge,name="Trace Of Identity")print("Name of new node after contraction:{}".format(final_result.name))Named axes.To make remembering what an axis does easier, you can optionally name a node's axes.a=tn.Node(np.zeros((2,2)),axis_names=["alpha","beta"])edge=a["beta"]^a["alpha"]Edge reordering.To assert that your result's axes are in the correct order, you can reorder a node at any time during computation.a=tn.Node(np.zeros((1,2,3)))e1=a[0]e2=a[1]e3=a[2]a.reorder_edges([e3,e1,e2])# If you already know the axis values, you can equivalently do# a.reorder_axes([2, 0, 1])print(a.tensor.shape)# Should print (3, 1, 2)NCON interface.For a more compact specification of a tensor network and its contraction, there isncon(). For example:fromtensornetworkimportncona=np.ones((2,2))b=np.ones((2,2))c=ncon([a,b],[(-1,1),(1,-2)])print(c)Different backend support.Currently, we support JAX, TensorFlow, PyTorch and NumPy as TensorNetwork backends.
We also support tensors with Abelian symmetries via asymmetricbackend, see thereference
documentationfor more details.To change the default global backend, you can do:tn.set_default_backend("jax")# tensorflow, pytorch, numpy, symmetricOr, if you only want to change the backend for a singleNode, you can do:tn.Node(tensor,backend="jax")If you want to run your contractions on a GPU, we highly recommend using JAX, as it has the closet API to NumPy.DisclaimerThis library is inalphaand will be going through a lot of breaking changes. While releases will be stable enough for research, we do not recommend using this in any production environment yet.TensorNetwork is not an official Google product. Copyright 2019 The TensorNetwork Developers.CitationIf you are using TensorNetwork for your research please cite this work using the following bibtex entry:@misc{roberts2019tensornetwork,
      title={TensorNetwork: A Library for Physics and Machine Learning}, 
      author={Chase Roberts and Ashley Milsted and Martin Ganahl and Adam Zalcman and Bruce Fontaine and Yijian Zou and Jack Hidary and Guifre Vidal and Stefan Leichenauer},
      year={2019},
      eprint={1905.01330},
      archivePrefix={arXiv},
      primaryClass={physics.comp-ph}
}