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Tensor0

Symmetry-aware tensors for Python, powered by Rust and JAX.

PyPI version CI Documentation License: MIT

Work with conserved charges and symmetry-allowed tensor blocks, then compose, contract or differentiate them using JAX. Rust handles structural metadata; JAX handles numerical storage and automatic differentiation.

Pre-1.0 software. CUDA support is experimental.

Documentation · Installation · Usage · API · Support policy

Installation

Install from PyPI in a virtual environment:

# CPU
python -m pip install tensor0

# CUDA 12 (experimental)
python -m pip install "tensor0[cuda12]"

See CUDA support for GPU requirements and supported operations. Prebuilt wheels require no Rust or C++ compiler. See the installation guide for available platforms, supported Python versions, GPU setup and source builds.

A small symmetric tensor

import jax.numpy as jnp
from tensor0 import TensorMap, U1Irrep, hom, space, storage_dim

v = space(U1Irrep, {0: 2, 1: 3})
h = hom((v,), (v,))
tensor = TensorMap(h, jnp.arange(storage_dim(h), dtype=jnp.float32))

for sector, block in tensor.blocks():
    print(sector, block.shape)

Main capabilities

  • Symmetry-aware tensors — graded spaces, built-in sector families, and ordinary and diagonal tensor maps with symmetry-allowed blocks.
  • Tensor algebra — transforms, contractions, blockwise linear algebra, decompositions and truncation.
  • JAX integration — jit and automatic differentiation within documented operation-specific limits.

Start with the usage guide, contraction guide and executable examples. Before 1.0, documented public API patch releases remain backward-compatible; breaking changes require a minor release and migration notes. See the support policy.

Contributing and benchmarks

See CONTRIBUTING.md for development and verification links, CHANGELOG.md for release changes, and the release checklist for installed-artifact acceptance. See the reproducible benchmark suites for measurements and tested configurations.

Acknowledgments

Tensor0's main design and architecture are based on TensorKit.jl, bringing these ideas to Python with JAX-backed storage and automatic differentiation.

For citation metadata, see CITATION.cff.

Metadata

Release files for tensor0 0.2.0

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tensor0-0.2.0-cp314-cp314-manylinux_2_28_x86_64.whl CPython 3.14 CPython 3.14 Linux glibc 2.28+ x86-64 Details
tensor0-0.2.0-cp314-cp314-macosx_15_0_arm64.whl CPython 3.14 CPython 3.14 macOS 15.0+ ARM64 Details
tensor0-0.2.0-cp313-cp313-manylinux_2_28_x86_64.whl CPython 3.13 CPython 3.13 Linux glibc 2.28+ x86-64 Details
tensor0-0.2.0-cp313-cp313-macosx_15_0_arm64.whl CPython 3.13 CPython 3.13 macOS 15.0+ ARM64 Details
tensor0-0.2.0-cp312-cp312-manylinux_2_28_x86_64.whl CPython 3.12 CPython 3.12 Linux glibc 2.28+ x86-64 Details
tensor0-0.2.0-cp312-cp312-macosx_15_0_arm64.whl CPython 3.12 CPython 3.12 macOS 15.0+ ARM64 Details
tensor0-0.2.0-cp311-cp311-manylinux_2_28_x86_64.whl CPython 3.11 CPython 3.11 Linux glibc 2.28+ x86-64 Details
tensor0-0.2.0-cp311-cp311-macosx_15_0_arm64.whl CPython 3.11 CPython 3.11 macOS 15.0+ ARM64 Details

Total release size: 101.9 MB

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