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Tensor network contraction function for Python 3.

Project description


ncon is a Python 3 package that implements the NCon function as described here: This Python implementation lacks some of the fancier features described in the paper, but the interface is the same.

ncon requires numpy and works with numpy ndarrays. It also works with the various tensors from this package, but does not require it.


pip install --user ncon


The only thing this package exports is the function ncon. It takes a list of tensors to be contracted, and a list index lists that specify what gets contracted with that. It returns a single tensor, that is the result of the contraction. Here's how the syntax works:

ncon(L, v, order=None, forder=None, check_indices=True):

The first argument L is a list of tensors. The second argument v is a list of list, one for each tensor in L. Each v[i] consists of integers, each of which labels an index of L[i]. Positive labels mark indices which are to be contracted (summed over). So if for instance v[m][i] == 2 and v[n][j] == 2, then the ith index of L[m] and the jth index of L[n] are to be identified and summed over. Negative labels mark indices which are to remain free (uncontracted).

The keyword argument order is a list of all the positive labels, which specifies the order in which the pair-wise tensor contractions are to be done. By default it is sorted(all-positive-numbers-in-v), so for instance [1,2,...]. Note that whenever an index joining two tensors is about to be contracted together, ncon contracts at the same time all indices connecting these two tensors, even if some of them only come up later in order.

Correspondingly forder specifies the order to which the remaining free indices are to be permuted. By default it is sorted(all-negative-numbers-in-v, reverse=True), meaning for instance [-1,-2,...].

If check_indices=True (the default) then checks are performed to make sure the contraction is well-defined. If not, an ValueError with a helpful description of what went wrong is provided.

If the syntax sounds a lot like Einstein summation, as implemented for example by np.einsum, then that's because it is. The benefits of ncon are that many tensor networkers are used to its syntax, and it is easy to dynamically generate index lists and contractions.


Here's a few examples, straight from the test file.

A matrix product:

from ncon import ncon
a = np.random.randn(3, 4)
b = np.random.randn(4, 5)
ab_ncon = ncon([a, b], ((-1, 1), (1, -2)))
ab_np =, b)
assert np.allclose(ab_ncon, ab_np)

Here the last index of a and the first index of b are contracted. The result is a tensor with two free indices, labeled by -1 and -2. The one labeled with -1 becomes the first index of the result. If we gave the additional argument forder=[-2,-1] the tranpose would be returned instead.

A more complicated example:

b = np.random.randn(5, 3, 6, 7, 6)
c = np.random.randn(7, 2)
d = np.random.randn(8)
e = np.random.randn(8, 9)
result_ncon = ncon(
    (a, b, c, d, e), ([3, -2, 2], [2, 3, 1, 4, 1], [4, -1], [5], [5, -3])
result_np = np.einsum("ijk,kilml,mh,q,qp->hjp", a, b, c, d, e)
assert np.allclose(result_ncon, result_np)

Notice that the network here is disconnected, d and e are not contracted with any of the others. When contracting disconnected networks, the connected parts are always contracted first, and their tensor product is taken at the end. Traces are also okay, like here on two indices of c. By default, the contractions are done in the order [1,2,3,4,5]. This may not be the optimal choice, in which case we should specify a better contraction order as a keyword argument.

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