Architecture & Crate Guide

This page describes how tensor4all-rs is organised, what each crate does, and how to choose the right crate for your use case.

Crate Dependency Diagram

tensor4all-tensorbackend    (scalar types, storage)
        |
tensor4all-core             (Index, Tensor, contraction, SVD/QR)
        |
   +---------+-----------+-----------+
   |         |           |           |
 treetn  itensorlike  simplett    tcicore
   |                     |           |
   |              partitionedtt   tensorci
   |                                 |
 treetci                       quanticstci
                                     |
                              quanticstransform

tensor4all-hdf5 depends on tensor4all-core and tensor4all-itensorlike (for MPS serialization). tensor4all-capi depends on most crates and forms the C FFI layer used by language bindings.

Layer Descriptions

Foundation (internal)

Crate	Description
tensorbackend	Internal. Scalar types (`f64`, `Complex64`), storage backends, and the tenferro-rs bridge. Users do not need to depend on this crate directly.
core	Foundation for everything else. Provides the `Index` system, dynamic-rank `Tensor`, contraction, and SVD/QR/LU factorizations.

Tensor Train & Tree Tensor Networks

Crate	Description
treetn	Tree tensor networks with arbitrary graph topology. Supports canonicalization, truncation, and contraction.
itensorlike	ITensors.jl-inspired `TensorTrain` with orthogonality tracking and multiple canonical forms. Useful when compatibility with the ITensors.jl mental model is important.
simplett	Lightweight tensor train for numerical computation. The go-to crate for creating, evaluating, and compressing tensor trains without extra overhead.
partitionedtt	Partitioned tensor trains for subdomain decomposition. Builds on `simplett`.
treetci	Tree TCI: cross interpolation on tree-structured tensor networks.

Tensor Cross Interpolation

Crate	Description
tcicore	Internal. Matrix CI, LUCI/rrLU algorithms, and cached function evaluation. Users do not need to depend on this crate directly.
tensorci	Tensor Cross Interpolation. Contains TCI2 (primary algorithm) and TCI1 (legacy). Use this for low-level TCI control.
quanticstci	High-level Quantics TCI. Interpolates functions on discrete or continuous grids in the quantics format.

Quantics & Transforms

Crate	Description
quanticstransform	Quantics transformation operators: shift, flip, Fourier, affine, and more.

I/O & Bindings

Crate	Description
hdf5	HDF5 serialization compatible with ITensors.jl/ITensorMPS.jl file formats.
capi	C FFI for language bindings (Julia, Python, etc.). Out of scope for this guide; see Julia Bindings.

Which Crate Should I Use?

Goal	Recommended crate
TCI on a black-box function (high level)	`tensor4all-quanticstci`
TCI with fine-grained control	`tensor4all-tensorci`
Tree TCI	`tensor4all-treetci`
Simple tensor train (create, evaluate, compress)	`tensor4all-simplett`
Tensor train with ITensors.jl-style interface	`tensor4all-itensorlike`
Tree tensor networks	`tensor4all-treetn`
Subdomain decomposition via partitioned TT	`tensor4all-partitionedtt`
Quantics transform operators	`tensor4all-quanticstransform`
HDF5 I/O compatible with Julia	`tensor4all-hdf5`

tensor4all-tensorbackend and tensor4all-tcicore are implementation details. They are not part of the public API surface and you should not depend on them directly in application code. Their interfaces may change without notice.

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