ncon-torch 0.3.0

Tensor network contraction function with GPU and autograd support via PyTorch.

ncon-torch

ncon-torch is a fork of the ncon package, modified to include GPU and autograd support via PyTorch

Installation

pip install ncon-torch

Usage

See original package repo for examples.

GPU Benchmark

Below we compare NumPy and PyTorch based contractions of a two-qubit gate with an n-qubit state. The benchmark was done on Google Colab with a T4 GPU.

Automatic differentiation

ncon-torch is compatible with PyTorch's reverse-mode automatic differentiation (autograd). Any contraction involving tensors with requires_grad=True will propagate gradients as expected.

Minimal Example

import torch
from ncon_torch import ncon

A = torch.randn(2, 2, dtype=torch.float64, requires_grad=True)
B = torch.tensor([[1.0, 0.0], [0.0, -1.0]], dtype=torch.float64)

out = ncon([A, B], [[1, -1], [1, -2]])
loss = torch.sum(out**2)
loss.backward()

print("Gradient:", A.grad) 

To validate the efficiency of reverse-mode AD, we benchmarked the time to compute:

• The cost function (a scalar-valued tensor contraction)
• Gradients via reverse-mode AD (.backward())
• Gradients via finite-difference approximation (scipy.optimize.approx_fprime)

Despite increasing parameter count, reverse-mode AD maintains near-constant overhead relative to the forward cost, unlike finite differences which scale poorly.