TensorNetwork
A tensor network wrapper for TensorFlow, JAX, and Numpy.
For an overview of tensor networks please see the following:
Installation
pip3 install tensornetwork
To install on Docker
This will create a Docker image containing TensorNetwork. It will isolate a TensorNetwork installation from the rest of the system.

Install Docker on your host sytem.

Build the docker image for your system:
git clone https://github.com/google/TensorNetwork
cd TensorNetwork
docker build t google/tensornetwork . # This builds the actual image based on latest Ubuntu, and installs TensorNetwork with the needed dependencies.
To install on Docker for TensorNetwork development
To do your TensorNetwork development in a Docker virtual machine, you can use dev_tools/Dockerfile:
git clone https://github.com/google/TensorNetwork
cd TensorNetwork/dev_tools
docker build t google/tensornetworkdev . # This builds the actual image based on latest Ubuntu, cloning the TensorNetwork tree into it with the needed dependencies.
docker run it google/tensornetworkdev
If you want to contribute changes to TensorNetwork, you will instead want to fork the repository and submit pull requests from your fork.
Documentation
Currently, the best documentation we have is our publication.
TensorNetwork: A Library for Physics and Machine Learning
We plan on getting proper documentation very soon once the API has settled.
Note: The following examples assume a TensorFlow v2 interface
(in TF 1.13 or higher, run tf.enable_v2_behavior()
after
importing TensorFlow) but should also work with eager mode
(tf.enable_eager_execution()
). The actual library does work
under graph mode, but documentation is limited.
Basic Example
Here, we build a simple 2 node contraction.
import numpy as np
import tensorflow as tf
tf.enable_v2_behavior()
import tensornetwork
# Create the network
net = tensornetwork.TensorNetwork()
# Add the nodes
a = net.add_node(np.ones((10,), dtype=np.float32))
# Can use either np.array or tf.Tensor and can even mix them!
b = net.add_node(tf.ones((10,)))
edge = net.connect(a[0], b[0])
final_node = net.contract(edge)
print(final_node.get_tensor().numpy()) # Should print 10.0
Optimized Contractions.
Usually, it is more computationally effective to flatten parallel edges before contracting them in order to avoid trace edges.
net = tensornetwork.TensorNetwork()
a = net.add_node(tf.ones((2, 2, 2)))
b = net.add_node(tf.ones((2, 2, 2)))
e1 = net.connect(a[0], b[0])
# Edge contraction is communative, so the order doesn't matter.
e2 = net.connect(b[1], a[1])
e3 = net.connect(a[2], b[2])
flattened_edge = net.flatten_edges([e1, e2, e3])
print(net.contract(flattned_edge).get_tensor().numpy())
We also have contract_between
and contract_parallel
for your convenience.
# Contract all of the edges between a and b.
net.contract_between(a, b)
# Contract all of edges that are parallel to edge
# (parallel means connected to the same nodes).
net.contract_parallel(edge)
Split Node
You 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 matricies multiplied by the square root of the
# singluar 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 = net.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 = net.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.
net = tensornetwork.TensorNetwork()
node = net.add_node(np.eye(2), name="Identity Matrix")
print("Name of node: {}".format(node.name))
edge = net.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 = net.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.
net = tensornetwork.TensorNetwork()
a = net.add_node(np.zeros((2, 2)), axis_names=["alpha", "beta"])
edge = net.connect(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.
net = tensornetwork.TensorNetwork()
a = net.add_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 is ncon()
. For example:
from tensornetwork import ncon
a = tf.random_normal((2,2))
b = tf.random_normal((2,2))
c = ncon([a,b], [(1,0),(0,2)])
print(tf.norm(tf.matmul(a,b)  c)) # Should be zero
It is also possible to generate a TensorNetwork
:
from tensornetwork import ncon_network
a = tf.random_normal((2,2))
b = tf.random_normal((2,2))
net, e_con, e_out = ncon_network([a,b], [(1,0),(0,2)])
for e in e_con:
n = net.contract(e) # Contract edges in order
n.reorder_edges(e_out) # Permute final tensor as necessary
print(tf.norm(tf.matmul(a,b)  n.get_tensor()))
Different backend support.
Currently, we support TensorFlow, JAX, and NumPy as TensorNetwork backends.
To change the default global backend, you can do:
tensornetwork.set_default_backend("jax") # numpy, tensorflow
Or, if you only want to change the backend for a single TensorNetwork
, you can do:
tensornetwork.TensorNetwork(backend="jax")
Advanced examples
Some more sophisticated examples can be found under examples/
.
Trotter evolution of a wavefunction
Demonstrates timeevolution of a wavefunction, achieved by applying a quantum circuit derived from a Trotter decomposition of the propagator. To run from source, use
python m examples.wavefunctions.evolution_example
from the root directory.
Disclaimer
This library is in alpha and 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.