Crate tenferro_burn 

Bridge between the Burn deep learning framework and tenferro tensor network operations.

This crate allows Burn tensors to be used with tenferro’s einsum and tensor network contraction routines, enabling seamless integration of tensor network methods into Burn-based deep learning pipelines.

Examples

use burn::backend::NdArray;
use burn::tensor::Tensor;
use tenferro_burn::einsum;

// Matrix multiplication via einsum
let a: Tensor<NdArray<f64>, 2> = Tensor::ones([3, 4], &Default::default());
let b: Tensor<NdArray<f64>, 2> = Tensor::ones([4, 5], &Default::default());
let c: Tensor<NdArray<f64>, 2> = einsum("ij,jk->ik", vec![a, b]);

Modules

backward

Backward-mode (autodiff) implementation of TensorNetworkOps for the [Autodiff<B, C>] backend.

convert

Conversion utilities between Burn tensor primitives and tenferro tensors.

forward

Forward-mode (inference) implementation of TensorNetworkOps for the NdArray backend.

Traits

TensorNetworkOps

Trait for backends that support tenferro tensor network operations.

Functions

einsum

High-level einsum on Burn tensors, dispatching to the backend’s TensorNetworkOps::tn_einsum implementation.