tenax-tn 0.1.0

JAX-based tensor network library with symmetry-aware block-sparse tensors

Tenax

Website | Docs | PyPI

A JAX-based tensor network library with symmetry-aware block-sparse tensors and label-based contraction.

The name Tenax combines Tensor network + Jax, and is also Latin for "holding fast" — reflecting how tensor networks bind indices together through contraction.

Experimental project — This library is under active development and largely written with the assistance of Claude Code (AI). While we test extensively, AI-generated code can contain subtle bugs. Please verify results against known benchmarks before using them in research. Bug reports and contributions are welcome.

Features

• Block-sparse symmetric tensors — only symmetry-allowed charge sectors stored (U(1), Z_n)
• Label-based contraction — legs are identified by string/integer labels; shared labels are automatically contracted (Cytnx-style)
• opt_einsum integration — optimal contraction path finding for multi-tensor contractions
• Network class — graph-based tensor network container with contraction caching
• .net file support — cytnx-style declarative network topology; parse once, load tensors, contract repeatedly (template pattern)
• Algorithms — DMRG, iDMRG (1D chain & infinite cylinder), TRG, HOTRG, iPEPS (simple update with 1-site or 2-site unit cell & AD optimization), fermionic iPEPS (fPEPS), quasiparticle excitations
• AutoMPO — build Hamiltonian MPOs from symbolic operator descriptions (custom couplings, NNN, arbitrary spin); supports symmetric=True for U(1) block-sparse MPOs
• AD-based iPEPS optimization — gradient optimization via implicit differentiation through CTM fixed point, supporting 1-site and 2-site unit cells (Francuz et al. PRR 7, 013237)
• QR-based CTMRG projectors — optional QR projectors for faster CTM convergence (replaces expensive eigh)
• Split-CTMRG — ket/bra-separated CTM environment tensors for O(χ³D³) projector cost instead of O(χ³D⁶); works with both DenseTensor and SymmetricTensor via the Tensor protocol (Naumann et al., arXiv:2502.10298)
• Quasiparticle excitations — iPEPS excitation spectra at arbitrary Brillouin-zone momenta (Ponsioen et al. 2022)
• Polymorphic tensor arithmetic — +, -, *, -T, max_abs, inner(), conj(), dagger(), bar() work identically on DenseTensor and SymmetricTensor, enabling algorithm code that is agnostic to the underlying storage
• Block-sparse SVD, QR, and eigh — native symmetry-aware decompositions in tenax.linalg for SymmetricTensor
• Extensible symmetry system — non-Abelian symmetry interface for future SU(2) support
• Tensor display — ASCII box repr for DenseTensor and SymmetricTensor showing legs, dimensions, charges, and block stats; TensorNetwork.to_mermaid() for Mermaid diagram export
• Benchmark suite — CLI-driven performance benchmarks for all algorithms across CPU, CUDA, TPU, and Metal backends

Installation

pip install tenax-tn

With hardware acceleration:

pip install tenax-tn[cuda13]   # NVIDIA GPU (CUDA 13, recommended)
pip install tenax-tn[cuda12]   # NVIDIA GPU (CUDA 12)
pip install tenax-tn[tpu]      # Google Cloud TPU
pip install tenax-tn[metal]    # Apple Silicon GPU (experimental)

From source

git clone https://github.com/tenax-lab/tenax.git
cd tenax
uv sync --all-extras --dev     # or: pip install -e ".[dev]"

See the JAX installation guide for more accelerator options.

Quick Start

import jax
import jax.numpy as jnp
import numpy as np
from tenax import (
    U1Symmetry, TensorIndex, FlowDirection,
    SymmetricTensor, TensorNetwork, contract
)

# Define U(1) symmetric tensor indices with named legs
u1 = U1Symmetry()
phys_charges = np.array([-1, 1], dtype=np.int32)
bond_charges = np.array([-1, 0, 1], dtype=np.int32)
key = jax.random.PRNGKey(0)

A = SymmetricTensor.random_normal(
    indices=(
        TensorIndex(u1, phys_charges, FlowDirection.IN,  label="p0"),
        TensorIndex(u1, bond_charges, FlowDirection.IN,  label="left"),
        TensorIndex(u1, bond_charges, FlowDirection.OUT, label="bond"),
    ),
    key=key,
)
B = SymmetricTensor.random_normal(
    indices=(
        TensorIndex(u1, phys_charges, FlowDirection.IN,  label="p1"),
        TensorIndex(u1, bond_charges, FlowDirection.IN,  label="bond"),  # shared label
        TensorIndex(u1, bond_charges, FlowDirection.OUT, label="right"),
    ),
    key=jax.random.PRNGKey(1),
)

# Contract by matching shared labels — "bond" is summed over automatically
result = contract(A, B)
print(result.labels())  # ('p0', 'left', 'p1', 'right')

# Build a tensor network and contract
tn = TensorNetwork()
tn.add_node("A", A)
tn.add_node("B", B)
tn.connect_by_shared_label("A", "B")
result = tn.contract()

Network Blueprint (.net file) Example

from tenax import NetworkBlueprint

# Define network topology as a string (or read from a .net file)
bp = NetworkBlueprint("""
L: a, b, c
M: a, p, q, d
A: b, p, s, e
M2: e, q, t, f
R: d, f, g
TOUT: c, s, t, g
""")

# Load tensors (can be DenseTensor or SymmetricTensor)
bp.put_tensors({"L": L, "M": M, "A": A, "M2": M2, "R": R})
result = bp.launch()  # contracts the full network

# Reuse with different tensors (e.g. in a DMRG sweep)
bp.put_tensor("A", new_A)
result2 = bp.launch()

DMRG Example

from tenax.algorithms.dmrg import dmrg, build_mpo_heisenberg, DMRGConfig
from tenax.network.network import build_mps

L = 10  # chain length
mpo = build_mpo_heisenberg(L, Jz=1.0, Jxy=1.0)

# Build random initial MPS
# ...

config = DMRGConfig(max_bond_dim=50, num_sweeps=10)
result = dmrg(mpo, initial_mps, config)
print(f"Ground state energy: {result.energy:.8f}")

2D Cylinder DMRG Example

from tenax import AutoMPO, DMRGConfig, build_random_mps, dmrg

# Build Heisenberg Hamiltonian on a 6x3 cylinder via AutoMPO
Lx, Ly, N = 6, 3, 18
auto = AutoMPO(L=N, d=2)
for x in range(Lx):
    for y in range(Ly):
        # Within-ring bond (periodic y)
        i, j = x * Ly + y, x * Ly + (y + 1) % Ly
        auto += (1.0, "Sz", min(i,j), "Sz", max(i,j))
        auto += (0.5, "Sp", min(i,j), "Sm", max(i,j))
        auto += (0.5, "Sm", min(i,j), "Sp", max(i,j))
        # Between-ring bond (open x)
        if x < Lx - 1:
            i, j = x * Ly + y, (x + 1) * Ly + y
            auto += (1.0, "Sz", i, "Sz", j)
            auto += (0.5, "Sp", i, "Sm", j)
            auto += (0.5, "Sm", i, "Sp", j)

mpo = auto.to_mpo(compress=True)
mps = build_random_mps(N, physical_dim=2, bond_dim=16)
config = DMRGConfig(max_bond_dim=100, num_sweeps=10, verbose=True)
result = dmrg(mpo, mps, config)
print(f"E/N = {result.energy / N:.8f}")  # converges in a few sweeps

See examples/heisenberg_cylinder.py for a full working example with 4x2, 6x3, and 8x4 cylinders.

iDMRG Example

from tenax import idmrg, build_bulk_mpo_heisenberg, iDMRGConfig

W = build_bulk_mpo_heisenberg(Jz=1.0, Jxy=1.0)
config = iDMRGConfig(max_bond_dim=32, max_iterations=100, convergence_tol=1e-8)
result = idmrg(W, config)
print(f"Energy per site: {result.energy_per_site:.6f}")  # ~ -0.4431
print(f"Converged: {result.converged}")

Infinite Cylinder iDMRG Example

from tenax import build_bulk_mpo_heisenberg_cylinder, iDMRGConfig, idmrg

# Ly=4 cylinder: each super-site is a ring of 4 spins (d=16, D_w=14)
# Only even Ly is supported (odd Ly frustrates AFM order).
W = build_bulk_mpo_heisenberg_cylinder(Ly=4)
config = iDMRGConfig(max_bond_dim=200, max_iterations=200, convergence_tol=1e-4)
result = idmrg(W, config, d=16)
e_per_spin = result.energy_per_site / 4
print(f"Energy per spin: {e_per_spin:.6f}")

See examples/heisenberg_infinite_cylinder.py for Ly=2 and Ly=4 cylinders with ED cross-checks.

TRG Example

from tenax import TRGConfig, trg, compute_ising_tensor, ising_free_energy_exact

beta = 0.44  # near critical temperature
T = compute_ising_tensor(beta)

config = TRGConfig(max_bond_dim=16, num_steps=20)
log_z_per_n = trg(T, config)
f_trg = float(-log_z_per_n / beta)
f_exact = ising_free_energy_exact(beta)
print(f"TRG:   {f_trg:.8f}")
print(f"Exact: {f_exact:.8f}")

See examples/ising_trg.py and examples/ising_hotrg.py for full TRG and HOTRG examples at multiple temperatures compared against the Onsager exact solution.

AutoMPO Example

from tenax import AutoMPO, build_auto_mpo

# Class-based interface: build a Heisenberg chain
L = 10
auto = AutoMPO(L)
for i in range(L - 1):
    auto += (1.0, "Sz", i, "Sz", i + 1)
    auto += (0.5, "Sp", i, "Sm", i + 1)
    auto += (0.5, "Sm", i, "Sp", i + 1)
mpo = auto.to_mpo()

# Or use the functional interface with custom operators
import numpy as np
custom_ops = {
    "X": np.array([[0.0, 1.0], [1.0, 0.0]]),
    "Z": np.array([[1.0, 0.0], [0.0, -1.0]]),
    "Id": np.eye(2),
}
terms = [(1.0, "Z", i, "Z", i + 1) for i in range(L - 1)]
terms += [(0.5, "X", i) for i in range(L)]
mpo = build_auto_mpo(terms, L=L, site_ops=custom_ops)

# Build a symmetric (U(1) block-sparse) MPO
mpo_sym = auto.to_mpo(symmetric=True)

iPEPS Simple Update (2-site unit cell)

import jax.numpy as jnp
from tenax import iPEPSConfig, CTMConfig, ipeps

# Build a 2-site Heisenberg gate
Sz = 0.5 * jnp.array([[1.0, 0.0], [0.0, -1.0]])
Sp = jnp.array([[0.0, 1.0], [0.0, 0.0]])
Sm = jnp.array([[0.0, 0.0], [1.0, 0.0]])
gate = jnp.einsum("ij,kl->ikjl", Sz, Sz) \
     + 0.5 * (jnp.einsum("ij,kl->ikjl", Sp, Sm)
             + jnp.einsum("ij,kl->ikjl", Sm, Sp))

# 2-site checkerboard iPEPS — captures Neel order
config = iPEPSConfig(
    max_bond_dim=2,
    num_imaginary_steps=200,
    dt=0.3,
    ctm=CTMConfig(chi=10, max_iter=40),
    unit_cell="2site",
)
energy, peps, (env_A, env_B) = ipeps(gate, None, config)
print(f"Energy per site: {energy:.6f}")  # ~ -0.65

See examples/heisenberg_ipeps_su.py for 1-site and 2-site unit cell examples.

iPEPS AD Optimization and Excitations

import jax.numpy as jnp
from tenax import (
    iPEPSConfig, CTMConfig, optimize_gs_ad,
    ExcitationConfig, compute_excitations, make_momentum_path,
)

# Build a 2-site Heisenberg gate
Sz = 0.5 * jnp.array([[1.0, 0.0], [0.0, -1.0]])
Sp = jnp.array([[0.0, 1.0], [0.0, 0.0]])
Sm = jnp.array([[0.0, 0.0], [1.0, 0.0]])
gate = jnp.einsum("ij,kl->ikjl", Sz, Sz) \
     + 0.5 * (jnp.einsum("ij,kl->ikjl", Sp, Sm)
             + jnp.einsum("ij,kl->ikjl", Sm, Sp))

# AD ground-state optimization (Francuz et al. PRR 7, 013237)
# su_init=True runs simple update first for a better starting tensor
config = iPEPSConfig(
    max_bond_dim=2,
    ctm=CTMConfig(chi=16, max_iter=50),
    gs_num_steps=200,
    gs_learning_rate=1e-3,
    su_init=True,
)
A_opt, env, E_gs = optimize_gs_ad(gate, None, config)
print(f"Ground-state energy: {E_gs:.6f}")

# 2-site AD optimization for antiferromagnets (Néel order)
config_2site = iPEPSConfig(
    max_bond_dim=2,
    ctm=CTMConfig(chi=16, max_iter=50),
    gs_num_steps=200,
    gs_learning_rate=1e-3,
    unit_cell="2site",
    su_init=True,
)
(A_opt, B_opt), (env_A, env_B), E_gs = optimize_gs_ad(gate, None, config_2site)

# Use QR projectors for faster CTM convergence
config_qr = iPEPSConfig(
    max_bond_dim=2,
    ctm=CTMConfig(chi=16, max_iter=50, projector_method="qr"),
    gs_num_steps=200,
    gs_learning_rate=1e-3,
)
A_opt, env, E_gs = optimize_gs_ad(gate, None, config_qr)

# Quasiparticle excitations (Ponsioen et al. 2022)
momenta = make_momentum_path("brillouin", num_points=20)
exc_config = ExcitationConfig(num_excitations=3)
result = compute_excitations(A_opt, env, gate, E_gs, momenta, exc_config)
print(result.energies.shape)  # (20, 3)

See examples/heisenberg_ipeps_ad.py for AD optimization with random vs simple update initialization, and examples/heisenberg_ipeps_excitations.py for the full excitation spectrum along Gamma-X-M-Gamma.

Split-CTMRG

from tenax import CTMConfig, ctm_split, compute_energy_split_ctm

# Split-CTMRG keeps ket/bra layers separate for O(χ³D³) projector cost
# instead of O(χ³D⁶) — significant speedup at large bond dimension D
config = CTMConfig(chi=20, max_iter=100, chi_I=10)
env = ctm_split(A, config)
E = compute_energy_split_ctm(A, env, gate, d=2)

Examples

Runnable example scripts are in the examples/ directory:

Script	Algorithm	Model
heisenberg_cylinder.py	DMRG	Heisenberg on 4x2, 6x3, 8x4 cylinders
heisenberg_infinite_cylinder.py	iDMRG	Heisenberg on infinite Ly=2, Ly=4 cylinders
heisenberg_ipeps_su.py	iPEPS simple update	Heisenberg (1x1 and 2-site unit cells)
heisenberg_ipeps_ad.py	iPEPS AD optimization	Heisenberg (random vs SU init)
heisenberg_ipeps_excitations.py	iPEPS excitations	Heisenberg dispersion along Γ-X-M-Γ
spinless_fermion_fpeps.py	fPEPS simple update	Spinless fermions (free and interacting)
ising_trg.py	TRG	2D Ising vs Onsager exact
ising_hotrg.py	HOTRG	2D Ising vs Onsager exact

Run any example with:

uv run python examples/<script>.py

Symmetry System

from tenax import U1Symmetry, ZnSymmetry, ProductSymmetry, FermionParity
import numpy as np

# U(1): integer charges, fusion by addition
u1 = U1Symmetry()
charges = np.array([-1, 0, 1], dtype=np.int32)
print(u1.fuse(charges, charges))  # [-2, 0, 2]
print(u1.dual(charges))           # [1, 0, -1]

# Z_3: charges mod 3
z3 = ZnSymmetry(3)
print(z3.fuse(np.array([1, 2], dtype=np.int32),
              np.array([2, 2], dtype=np.int32)))  # [0, 1]

# Product symmetry: combine two symmetries (e.g., charge × S_z)
sym = ProductSymmetry(U1Symmetry(), U1Symmetry())
packed = ProductSymmetry.encode_charges(
    np.array([0, 1, -1], dtype=np.int32),  # charge
    np.array([1, 0, -1], dtype=np.int32),  # S_z
)
q1, q2 = ProductSymmetry.decode_charges(packed)

Limitations: ProductSymmetry combines exactly two factors by bit-packing two int16 charges into one int32. Nesting is not supported, so three-factor groups (e.g., U(1)×U(1)×Z₂) require a future MultiProductSymmetry. Each factor charge must fit in the int16 range [-32768, 32767].

Gotchas

Float64 precision and JAX_ENABLE_X64

Tenax defaults to float64 for all tensors and algorithms. Importing tenax automatically calls jax.config.update("jax_enable_x64", True), so 64-bit arithmetic is enabled out of the box.

If you import JAX before tenax and create arrays in that window, they will still be float32. To avoid surprises, either import tenax first or enable x64 manually:

import jax
jax.config.update("jax_enable_x64", True)

import tenax

MPO index convention

The MPO W-tensor uses the convention W[w_l, ket, bra, w_r] — the two middle indices are physical (ket on top, bra on bottom) and the outer indices are bond dimensions.

NumPy >= 2.0 casting

Adding a Python complex scalar (even 1+0j) into a float64 array raises UFuncOutputCastingError under NumPy >= 2.0. Use .real or an explicit complex128 dtype instead.

Local test failures on macOS x86_64

uv run pytest may fail on macOS x86_64 if jaxlib has no wheel for that platform.

Benchmarks

A CLI-driven benchmark suite measures wall-clock performance of every algorithm across hardware backends.

# Quick smoke test (TRG, small size, 1 trial)
python -m benchmarks.run --backend cpu --algorithm trg --size small --trials 1

# Full CPU baseline
python -m benchmarks.run --backend cpu -o benchmarks/results/cpu_baseline.json

# GPU comparison
python -m benchmarks.run --backend cuda -o benchmarks/results/cuda.json

# Specific algorithms and sizes
python -m benchmarks.run -b cpu -a dmrg idmrg -s small medium -n 5

# CSV output for analysis
python -m benchmarks.run -b cpu -a all -s all --csv results.csv

# Show available backends
python -m benchmarks.run --list-backends

Each run prints a summary table and saves full results (timings, parameters, device info) to JSON. See docs/guide/benchmarks.md for the complete guide.

Development

# Clone and install with dev dependencies
git clone https://github.com/tenax-lab/tenax.git
cd tenax
uv sync --all-extras --dev

# Install pre-commit hooks (ruff lint + format on every commit)
uv run pre-commit install

# Run tests
uv run pytest -m core          # fast core tests only
uv run pytest -m algorithm     # algorithm tests (DMRG, TRG, iPEPS, integration)
uv run pytest -m "not slow"    # skip expensive tests
uv run pytest                  # full suite

# Lint
uv run ruff check src/ tests/

Work-in-progress design documents live in design/.

Documentation

Full API documentation is built with Sphinx:

cd docs && make html

The generated HTML is in docs/_build/html/.

References

• H.-J. Liao, J.-G. Liu, L. Wang, T. Xiang, Phys. Rev. X 9, 031041 (2019) — AD-based iPEPS ground-state optimization
• A. Francuz, N. Schuch, B. Vanhecke, PRR 7, 013237 (2025) — Stable AD through CTM (SVD regularization, truncation correction, implicit differentiation)
• L. Ponsioen, F. F. Assaad, P. Corboz, SciPost Phys. 12, 006 (2022) — Quasiparticle excitations for iPEPS
• J. Naumann, E. L. Weerda, J. Eisert, M. Rizzi, P. Schmoll, arXiv:2502.10298 (2025) — Split-CTMRG with factored projectors for efficient iPEPS environments

License

MIT