Tenax

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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), iTEBD (numerically stable infinite TEBD, incl. inversion-free Hastings update), TRG, HOTRG, iPEPS (simple update with 1-site or 2-site unit cell & AD optimization), fermionic iPEPS (fPEPS), quasiparticle excitations
• GPU/TPU-accelerated DMRG — JIT-compiled sweeps via jax.lax.scan for dense tensors and per-operation JIT for block-sparse symmetric tensors; automatic warmup-to-JIT transition when bond dimensions are growing; multi-GPU sharding via GSPMD for large bond dimensions (DMRGConfig(accelerator="jit"|"sharded"))
• 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); L-BFGS with Hager-Zhang line search and metric preconditioning (Rader et al.), Adam (with cosine lr decay), and conjugate gradient optimizers; implicit AD via iterative VJP (default) and optional GMRES route; explicit AD through unrolled CTM iterations for 1-site C4v path; 2-site shared-tensor C4v path (unit_cell="2site" + gs_c4v=True) where a single C4v tensor is optimized and the second sublattice is derived by spin-π rotation, stable across χ=8–24 for spin-1/2 AFMs; opt-in reference-mode dense C4v Appendix C-F mode (ctm_ad_mode="c4v_reference") with Krylov implicit backward (bicgstab + gmres fallback); root implicit AD (ctm_ad_mode="root_implicit", dense 1x1 only; Burgelman et al. arXiv:2607.15030) driving the characteristic equations rather than back-propagating the CTM sweep, so no SVD/eigh backward appears in the gradient path — an accuracy/stability lever, not a speed one (~63x slower than explicit AD at D=2 χ=6, reproducing the paper's §VI.3), whose reason to exist is that explicit backprop NaNs on every entry at D=3 χ=4 where this path stays finite and FD-correct; sigma gauge fixing (forward_gauge="sigma") for stable elementwise CTM convergence; C4v symmetry enforcement via explicit basis parameterization; chi-ramping schedule (optimize_gs_ad_chi_schedule) for progressive refinement
• In-CTM χ-bump (variPEPS §2.8.2) — recommended reactive growth of the CTM bond dimension inside CTM convergence (CTMConfig.ctmrg_heuristic_increase_chi=True with chi_max set); the env is always converged at the new χ before the optimizer sees it, avoiding the zero-padded-env cliff-edge artifact that the legacy end-of-outer-step chi_auto_bump and scheduled chi_ramp introduce between L-BFGS steps. Both legacy knobs still work but emit DeprecationWarning (see issue #512) and will be removed in a future release. References: Naumann et al., SciPost Phys. Lect. Notes 86, 2024
• SVD and QR CTMRG projectors — SVD (Fishman) projectors (projector_method="svd", default) and eigh projectors, plus a reduced-corner QR-CTMRG projector (projector_method="qr", arXiv:2505.00494) on the dense single-site path, usable both forward-only and under AD ground-state optimization via gs_recipe="1x1" + gs_projector_method="qr" (Phase 2, dense; block-sparse is a later phase)
• 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). Note this is a projector cost bound, not a peak-memory one: the realized value_and_grad peak is 1.02–2.7× below the fused path depending on χ, and converges to ~1× at the memory ceiling (#825)
• Split-CTM energy entry points — compute_energy_split_ctm_tensor_2site and compute_energy_split_ctm_tensor_multisite for 2-site checkerboard and multisite unit cells (kagome PESS, etc.) at large D
• Split-CTM AD ground-state optimization — optimize_gs_ad with CTMConfig(fuse_virtual_legs=False) drives the single-site optimizer (unit_cell="1x1") and the 2-site checkerboard optimizer (unit_cell="2site"), both on the default gs_recipe="2x2" (single-site since #746; gs_recipe="1x1" remains reachable but collapses the environment to rank-1 corners and is bisection-only — see #726) through the split χ²·D⁴ forward instead of the fused χ²·D⁶ double layer: implicit AD via a Γ-gauge-fixed fixed-point custom_vjp (Neumann backward; the 2-site case differentiates the coupled (env_A, env_B) fixed point), with the line-search probe, warm-start, and final environment all routed through the same split forward (returns SplitCTMTensorEnv). The implicit gradient matches the trusted explicit-AD gradient to machine precision in the non-degenerate regime (~1e-15; the SU(2)-symmetric Heisenberg point carries a degenerate-SV SVD-backward floor on the explicit reference). DenseTensor only (SymmetricTensor/fermionic split AD is a later phase); fixed χ (the χ-changing knobs are rejected on this path); the memory win over fused is a large-D effect (D≳16) — measured at recipe="2x2" on one A100-80GB it reaches χ=96/48/32 at D=8/10/12 against the fused path's χ=64/48/16, i.e. 1.5× / 1.0× / 2.0× in χ, and the per-cell peak advantage shrinks from 2.66× at χ=16 to 1.02× at the ceiling (#825). References: Naumann et al., arXiv:2502.10298
• Honeycomb iPEPS CTM (native) — rank-4, 6-corner, 3-direction, 2-sublattice CTMRG for honeycomb iPEPS (replaces the dummy-bond brick-wall workaround). Public entry honeycomb_ctm_energy_implicit provides jax.custom_vjp with a JIT-fused GMRES backward; default Corboz biorthogonal projector + per-column phase fix; configurable energy_fn hook for kagome iPESS triangle energies. References: Lukin & Sotnikov, PRB 107, 054424 (2023) for the 6-corner CTMRG and the bipartite extension in PRE 109, 045305 (2024) §II.C.
• Quasiparticle excitations — iPEPS excitation spectra at arbitrary Brillouin-zone momenta (Ponsioen et al. 2022)
• Model gate helpers — pre-built 2-site Hamiltonian tensors: heisenberg_gate (dense DenseTensor with trivial charges), heisenberg_gate_u1sz (U(1)-Sz block-sparse SymmetricTensor with charges [+1, −1] for spin-↑/↓), xxz_gate (XXZ anisotropy), spinless_fermion_gate (fPEPS hopping + interaction with FermionParity symmetry)
• 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
• Sector-based TensorIndex — legs store sorted charge sectors and multiplicities for O(n_sectors) lookups; FuseInfo tracks parent legs so split_index can reverse fuse_indices
• Cython BLAS fast path — fused Cython Lanczos solver and block-sparse contractions via direct BLAS calls with zero Python reentry for high-performance CPU DMRG
• iDMRG transfer matrix environments — fixed-point environment computation for self-consistent infinite boundary conditions
• Extensible symmetry system — non-Abelian symmetry interface for future SU(2) support
• Benchmark suite — CLI-driven performance benchmarks for all algorithms across CPU, CUDA, TPU, and Metal backends

Installation

Note: The PyPI package (tenax-tn) is not yet available. Install from source using the instructions below.

git clone https://github.com/tenax-lab/tenax.git
cd tenax

# With uv (recommended)
uv sync --all-extras --dev

# Or with pip
pip install -e .

Hardware acceleration

Tenax uses JAX as its backend. To enable GPU or TPU acceleration, install the appropriate JAX variant before installing Tenax:

# NVIDIA GPU (CUDA 13, recommended)
pip install -U "jax[cuda13]"

# NVIDIA GPU (CUDA 12)
pip install -U "jax[cuda12]"

# Google Cloud TPU
pip install -U "jax[tpu]"

# Apple Silicon GPU (macOS only, experimental)
pip install jax-metal

See the JAX installation guide for the latest 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

Performance note: Tenax's DMRG uses a fused Cython BLAS pipeline on CPU for high-throughput block-sparse contractions. GPU/TPU acceleration is available via DMRGConfig(accelerator="jit") for dense tensors and accelerator="sharded" for multi-GPU runs.

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.

For large-χ dense HOTRG, set HOTRGConfig(device_mesh=mesh) (a 1-D jax.sharding.Mesh) to shard the dominant χ⁶ intermediate across multiple GPUs — ~1/N per-device peak memory and a higher reachable χ, at the same free energy. Since HOTRG is forward-only there is no autodiff-through-SVD barrier, so GSPMD sharding is effective here (unlike the CTM-AD path). See examples/probe_hotrg_multigpu.py.

The same coarse-graining works for the q-state Potts model (compute_potts_tensor produces any q >= 2; q = 2 reduces to Ising):

from tenax import HOTRGConfig, hotrg, compute_potts_tensor, potts_critical_beta

q = 3
beta_c = potts_critical_beta(q)  # ln(1 + sqrt(q)), the self-dual critical point
T = compute_potts_tensor(beta_c, q=q)

config = HOTRGConfig(max_bond_dim=16, num_steps=20)
log_z_per_n = hotrg(T, config)
print(f"Potts q={q} at beta_c={beta_c:.5f}:  ln(Z)/N = {float(log_z_per_n):.6f}")

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=0.05,
    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}")  # ~ -0.63

The energy ipeps() reports comes from the legacy 2-site CTM, which does not converge on a genuinely entangled state — it sits ~0.02 above the truth. For an accurate number, measure the returned state with ctm_tensor(recipe="2x2") (D=2 gives −0.65933, χ-converged). Simple update itself was fixed in #667; if you have results from before that, note it converged to the product state and that smaller dt made it worse — see the changelog.

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

Belief-propagation gauge (correct bond weights)

Simple update stores each bond's Schmidt spectrum straight from the SVD that produced it. A non-unitary gate on a neighbouring bond changes this bond's Schmidt values, and they are never recomputed, so the stored weights drift away from the spectra they are taken to be. bp_gauge_checkerboard re-derives all four of them by solving the belief-propagation fixed point (bond weights on a PEPS are BP messages) and re-gauges the tensors to match:

from tenax import BondWeights, bp_gauge_checkerboard

# A, B are bare Vidal Gamma tensors; lam_h, lam_v are the weights they carry.
stored = BondWeights(h_AB=lam_h, h_BA=lam_h, v_AB=lam_v, v_BA=lam_v)
A, B, weights, info = bp_gauge_checkerboard(A, B, stored)
print(info.converged, info.iterations)
print(weights.h_AB, weights.h_BA)   # the two horizontal bonds, resolved separately

The weights are required, and are not an initial guess: in Vidal form the state is ... Γ_A λ Γ_B ..., so λ is half of what you are handing over. A fresh random pair whose bonds really are unweighted passes BondWeights.ones(D, D).

Every step is a gauge transformation, so the physical state is unchanged to machine precision — only the weights move. Measured on simple update's own converged D=3 output, the stored spectrum is [1, 0.16586, 0.01564] where the BP-consistent one is [1, 0.14243, 0.01130]: 15% off on the second Schmidt value and ~35% on the tail. Use this before reading lambda as a Schmidt spectrum — entanglement entropy, truncation-error estimates, or the symmetric gauge handed to a CTM.

This corrects the weights, not simple update's dynamics; it does not change the state ipeps() converges to.

iPEPS AD Optimization and Excitations

import jax.numpy as jnp
from tenax import (
    iPEPSConfig,
    CTMConfig,
    optimize_gs_ad,
    optimize_gs_ad_chi_schedule,
    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)
)

# Explicit-AD configuration: L-BFGS + explicit AD + QR projectors.
# forward_gauge defaults to "phase" (variPEPS-style Frobenius + phase
# fix), correct for both implicit and explicit AD. Reaches E=-0.6628
# at D=2, chi=16 (literature: -0.6548 at D=2).
config = iPEPSConfig(
    max_bond_dim=2,
    ctm=CTMConfig(
        chi=16,
        max_iter=80,
        projector_method="qr",  # recommended projector for explicit AD
    ),
    gs_implicit_ad=False,  # opt into explicit AD (the default is implicit)
    gs_projector_method="qr",
    gs_optimizer="lbfgs",  # L-BFGS with Hager-Zhang line search
    gs_line_search_method="hager_zhang",
    gs_metric_precond=True,  # metric preconditioning (Rader et al.)
    gs_c4v=True,  # C4v basis parameterization
    su_init=True,
)
A_opt, env, E_gs = optimize_gs_ad(gate, None, config)
print(f"Ground-state energy: {E_gs:.6f}")

# Chi-ramping schedule: progressively increase chi for faster convergence.
# Each entry is (chi, num_steps) — run `num_steps` AD steps at logical χ=chi.
# Internally the schedule runs as a single `optimize_gs_ad` call with envs
# padded to max(chi) from step 1, so the JIT-compiled CTM / energy / backward
# kernels never see a shape change (issue #453).
chi_schedule = [(4, 30), (8, 30), (16, 20)]
A_opt, env, E_gs = optimize_gs_ad_chi_schedule(gate, None, config, chi_schedule)

# 2-site shared-tensor C4v AD for antiferromagnets (Neel order)
# A single C4v-parameterized tensor is optimized; B is derived from A via
# sublattice rotation B = e^{i pi sigma^y/2} on the physical leg.  This
# ties the two sublattices together and avoids the A/B drift that makes
# the unconstrained 2-site AD path unstable.  Spin-1/2 (d=2) only.
config_2site = iPEPSConfig(
    max_bond_dim=2,
    ctm=CTMConfig(chi=16, max_iter=100, min_iter=50),
    gs_optimizer="lbfgs",
    gs_explicit_ad_steps=10,
    gs_explicit_ad_warmup=2,
    gs_num_steps=50,
    gs_line_search=True,
    unit_cell="2site",
    gs_c4v=True,
    su_init=True,
    num_imaginary_steps=100,
    dt=0.05,
)
(A_opt, B_opt), (env_A, env_B), E_gs = optimize_gs_ad(gate, None, config_2site)

# SVD (Fishman) projectors — alternative to eigh and QR
config_svd = iPEPSConfig(
    max_bond_dim=2,
    ctm=CTMConfig(chi=16, max_iter=50, projector_method="svd"),
    gs_num_steps=200,
    gs_optimizer="lbfgs",
    gs_line_search_method="hager_zhang",
)
A_opt, env, E_gs = optimize_gs_ad(gate, None, config_svd)

# Opt-in reference-mode dense C4v mode (Francuz et al., App. C-F)
config_reference = iPEPSConfig(
    max_bond_dim=2,
    ctm=CTMConfig(
        chi=16,
        max_iter=80,
        projector_method="eigh",
        ctm_ad_mode="c4v_reference",
        adjoint_solver="bicgstab",
        adjoint_maxiter=50,
        adjoint_tol=1e-8,
    ),
    gs_implicit_ad=True,
    gs_c4v=True,
    unit_cell="1x1",
    gs_num_steps=100,
    gs_optimizer="adam",
)
A_opt, env, E_gs = optimize_gs_ad(gate, None, config_reference)

# Root implicit AD (Burgelman et al. arXiv:2607.15030), dense 1x1 only.
# Drives the characteristic equations instead of back-propagating the CTM
# sweep, so no SVD/eigh backward appears in the gradient path.
config_root = iPEPSConfig(
    max_bond_dim=2,
    ctm=CTMConfig(
        chi=6,
        max_iter=100,
        conv_tol=1e-10,
        ctm_ad_mode="root_implicit",
        # Relative clamp on the retained CTM spectrum. None (the default) uses
        # the derived eps**(1/3): the covariant equations depend on S cubically,
        # so a retained direction below that cannot be resolved in working
        # precision and would produce NaN gradients. Raise it only to diagnose
        # a state whose environment is rank-deficient -- clamping past the
        # genuinely-weighted directions breaks the equations rather than
        # regularising them, which the root-residual gate then rejects.
        rel_floor=None,
    ),
    unit_cell="1x1",
    gs_num_steps=20,
    gs_optimizer="adam",
)
A_opt, env, E_gs = optimize_gs_ad(gate, None, config_root)

# 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). That is a projector-cost bound, not a peak-memory one:
# measured against the fused path it buys ~1.5x in chi at D=8 and ~2x at D=12
# on one GPU, and nothing at D=10 (#825).
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)

Checking whether the CTM actually converged

ctm, ctm_2site and ctm_split return an environment whether or not the sweep met conv_tol — running out of max_iter is not an error. Pass return_meta=True for a CTMConvergenceInfo saying which happened, rather than inferring it from an energy that silently moves with max_iter (#839):

from tenax import CTMConfig, ctm_2site

env_A, env_B, info = ctm_2site(A, B, CTMConfig(chi=16), return_meta=True)
if not bool(info.converged):
    print(f"stopped at max_iter after {int(info.n_iter)} sweeps, "
          f"criterion still {float(info.diff):.2e}")

info.diff is the convergence criterion — the change in the corner singular values, not in the energy. ipeps() performs this check itself and warns.

Honeycomb iPEPS CTM (native rank-4)

Native rank-4 CTMRG for honeycomb iPEPS — six corners, three edge directions, two sublattices — without the dummy-bond brick-wall hack. Custom jax.custom_vjp forward with a JIT-fused GMRES backward.

import jax
import jax.numpy as jnp
import numpy as np
from tenax import (
    HONEYCOMB_DIRECTIONS,
    honeycomb_ctm_energy_implicit,
    honeycomb_ctm_run,
)
from tenax.core.index import FlowDirection, TensorIndex
from tenax.core.symmetry import U1Symmetry
from tenax.core.tensor import DenseTensor


def _make_site(D=2, d=2, key=jax.random.PRNGKey(0)):
    sym = U1Symmetry()
    virt = np.zeros(D, dtype=np.int32)
    phys = np.zeros(d, dtype=np.int32)
    indices = (
        TensorIndex.from_charges(sym, virt.copy(), FlowDirection.OUT, label="e0"),
        TensorIndex.from_charges(sym, virt.copy(), FlowDirection.OUT, label="e1"),
        TensorIndex.from_charges(sym, virt.copy(), FlowDirection.OUT, label="e2"),
        TensorIndex.from_charges(sym, phys.copy(), FlowDirection.IN, label="phys"),
    )
    re = jax.random.normal(key, (D, D, D, d))
    im = jax.random.normal(jax.random.fold_in(key, 1), (D, D, D, d))
    return DenseTensor((re + 1j * im).astype(jnp.complex128), indices)


# Spin-1/2 Heisenberg bond operator (4×4)
sx = 0.5 * np.array([[0, 1], [1, 0]], dtype=np.complex128)
sy = 0.5 * np.array([[0, -1j], [1j, 0]], dtype=np.complex128)
sz = 0.5 * np.array([[1, 0], [0, -1]], dtype=np.complex128)
H_bond = jnp.asarray(np.kron(sx, sx) + np.kron(sy, sy) + np.kron(sz, sz))

# Honeycomb iPEPS uses two rank-4 sites at coords (0,0) and (1,0); legs
# (e0, e1, e2, phys). All virtuals OUT, phys IN.
A = _make_site(D=2, d=2, key=jax.random.PRNGKey(0))
B = _make_site(D=2, d=2, key=jax.random.PRNGKey(1))
sites = {(0, 0): A, (1, 0): B}

# Forward only: returns the converged per-sublattice env dict + info.
envs, info = honeycomb_ctm_run(
    sites, chi=8, max_iter=80, conv_tol=1e-8,
    projector_method="biorthogonal",  # default; eigh/svd are A=B opt-ins
    forward_gauge="phase",            # default; sigma reserved for A=B opt-in
)

# Implicit-AD energy: takes jax.grad through the CTM fixed point via
# JIT-fused GMRES on (I - dF/denv) lambda = dE/denv.
energy = honeycomb_ctm_energy_implicit(
    sites, H_bond, chi=8, max_iter=80, conv_tol=1e-8,
)
grad_fn = jax.grad(
    lambda Ad: honeycomb_ctm_energy_implicit(
        {(0, 0): DenseTensor(Ad, A.indices), (1, 0): B},
        H_bond, chi=8, max_iter=40,
    )
)
gA = grad_fn(A.todense())

The default energy is the 3-edge nearest-neighbor bond sum Σ_α Tr(ρ_α · H_bond). Pass energy_fn=compute_honeycomb_triangle_energy for the kagome iPESS use case where each site is a 3-spin triangle and the Hamiltonian is the intra-triangle 3-spin operator.

Kagome iPESS with AD

Differentiable iPESS pipeline for kagome XXZ ground states (Liao et al., PRX 9, 031041, 2019). Two simplex tensors T_u, T_d and three site tensors R_a, R_b, R_c define the variational state; triangle simple update gives the SU warm start, then L-BFGS through the square-coarse-grained CTM (Convention C) refines (R_a, R_b, R_c, T_u, lambdas). T_d is held frozen during AD — its variational role is absorbed by the down-bond gauges.

import jax
from tenax import (
    CTMConfig,
    IPESSState,
    kagome_triangle_xxz_hamiltonian,
    kagome_xxz_pess_cg_gates,
    pess_simple_update,
    optimize_pess_ad,
)

D, d = 2, 3  # spin-1
H = kagome_triangle_xxz_hamiltonian(delta=1.0, d=d)
cg_gates = kagome_xxz_pess_cg_gates(delta=1.0, d=d)

state = IPESSState.random(D=D, d=d, key=jax.random.PRNGKey(0))
state = pess_simple_update(state, H,
                           dt_schedule=[(0.1, 200), (0.01, 200), (0.001, 100)],
                           D_max=D)

config = CTMConfig(chi=8, max_iter=30, conv_tol=1e-7,
                   projector_method="svd", forward_gauge="phase",
                   ctm_conv_method="elementwise")
state, e_per_site = optimize_pess_ad(state, cg_gates, config, max_iter=30)
print(f"E/site = {e_per_site:.6f}")  # spin-1 D=2 lands around -1.0

The full kagome Hamiltonian (3 up-triangle bonds + 3 down-triangle bonds per unit cell) is reconstructed via compute_energy_cg's intra-cell + horizontal/vertical/diagonal inter-cell 2-site RDMs; see examples/kagome_spin12_pess_ad_benchmark.py and examples/kagome_spin1_pess_ad_benchmark.py for full sweeps.

Multisite path (3-site kagome on a square unit cell)

For the multisite encoding pess_to_kagome_3site_multisite, where the kagome unit cell maps to three sites (u, v, w) on a square lattice and the energy uses 4 NN bonds + 2 marginalised-3-site contributions, use build_pess_loss_3site_multisite and optimize_pess_3site_multisite_ad:

from tenax import (
    build_pess_loss_3site_multisite,
    optimize_pess_3site_multisite_ad,
    pess_to_kagome_3site_multisite,
)
from tenax.algorithms._pess_multisite_energy import kagome_3site_bond_gates

bond_gates = kagome_3site_bond_gates(delta=1.0, d=d)
state, e_per_site = optimize_pess_3site_multisite_ad(
    state, bond_gates, config, max_iter=30,
)

The optimizer warm-starts CTM envs across L-BFGS steps via an internal env_cache, returns the best-seen energy across the trajectory, and gates CTMConfig at entry on the implicit-AD invariants (projector_method='svd', forward_gauge='phase', ctm_conv_method='elementwise').

Examples

Runnable example scripts are in the examples/ directory:

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)

Charge arithmetic

BaseSymmetry is the sanctioned boundary for every charge operation. Extension authors should call these rather than hand-rolling the arithmetic — the hand-rolled forms assume the group inverse is integer negation and the group operation is integer addition, which is true for U(1), accidentally true for Z_n, and false for the bit-packed charges of ProductSymmetry.

from tenax import U1Symmetry
import numpy as np

sym = U1Symmetry()
charges = np.array([-1, 0, 2], dtype=np.int32)

# Weight a charge by its leg's flow: IN (+1) unchanged, OUT (-1) inverted.
# Use this instead of `int(flow) * charge`.
sym.flow_charge(-1, charges)            # [1, 0, -2]

# Reduce to the canonical representative (`% n` for Z_n, identity for U(1)).
sym.canonicalize_charges(charges)

# Evaluate a conservation law. A block is valid exactly when the net charge
# equals `identity()`. Use this instead of `sum(flow * q for ...)`.
sym.net_charge([1, 1], flows=[1, -1])   # 0
sym.is_conserved([1, 1], flows=[1, -1]) # True

Charge width. Charges are stored as int32. Intermediate arithmetic in the conservation law uses charge_accumulator_dtype, which is int64 for U(1) and FermionicU1 — whose charges are unbounded by definition — and int32 elsewhere, since Z_n reduces mod n and ProductSymmetry's charges are bounded by their packing. This puts the overflow ceiling at 2⁶³ rather than 2³¹; it does not remove it.

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].

Which legs may be contracted

Two symmetric legs may be contracted when they have opposite flows and identical charges — what flip_flow() on a TensorIndex, or bar() on a tensor, produces. This is not the same as is_dual_of() / dual() / dagger(), which negate the charges: block-sparse contraction pairs blocks by charge value while dense contraction pairs by position, and negation permutes the position→charge map, so the two representations then compute different sums.

from tenax import FlowDirection, SymmetricTensor, TensorIndex, U1Symmetry, contract
import jax, numpy as np

sym = U1Symmetry()
charges = np.array([-1, 0, 1], dtype=np.int32)
free_a = TensorIndex.from_charges(sym, charges, FlowDirection.OUT, label="i")
free_b = TensorIndex.from_charges(sym, charges, FlowDirection.IN, label="j")
shared = TensorIndex.from_charges(sym, charges, FlowDirection.IN, label="k")

A = SymmetricTensor.random_normal((free_a, shared.flip_flow()), jax.random.PRNGKey(0))
B = SymmetricTensor.random_normal((shared, free_b), jax.random.PRNGKey(1))
contract(A, B)          # `k` is OUT on A and IN on B, with identical charges

Mixing the conventions makes contract() return a representation-dependent answer, silently (#834). Set TENAX_STRICT_CONTRACT=1 to make it raise ValueError instead — naming both legs — when the two representations would disagree:

TENAX_STRICT_CONTRACT=1 python my_script.py

It is opt-in rather than the default because the checks are structural while the disagreement depends on the blocks' values: the CTM initial environment contracts non-dual bonds and discards products by the thousand, and is exact anyway because those products are all zero. Turn it on when auditing a path, not in production.

While armed it also forces the reference per-block contraction, overriding the accelerated block-sparse backends (TENAX_BATCH_BLOCKSPARSE, TENAX_STACK_BLOCKSPARSE, TENAX_USE_CUTENSOR_BLOCKSPARSE) for the duration. Those paths drop out-of-set output keys without consulting the check, so an audit that left them enabled would report clean on the products it never inspected — and a diagnostic whose silence is unreliable is worse than none.

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)
• M. Rader, L. Gresista, C. Hubig, S. Montangero, A. Weichselbaum, J. von Delft, arXiv:2511.09546 (2025) — Metric preconditioning and Hager-Zhang line search for iPEPS optimization
• 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

Apache 2.0