using-tenpy

name	using-tenpy
description	Use when choosing or running TeNPy (Python) for MPS calculations — especially iTEBD (the algorithm MPSKit lacks), plus iDMRG, VUMPS, finite DMRG/TEBD, and finite-T purification — including model setup, the tangent-space gradient-norm probe (tangent_projector_test), symmetry via conserve, threading, or TeNPy/numpy-ABI setup failures.

TeNPy

Use TeNPy (Tensor Network Python; Hauschild & Pollmann) as the harness's Python MPS stack. Its decisive role is iTEBD — the infinite imaginary-time algorithm MPSKit does not provide — but it also has iDMRG, VUMPS, finite DMRG/TEBD, and finite-temperature purification.

Sources

• Method card: skills/method-mps/SKILL.md
• Install: make install tenpy — creates the isolated .venv-tenpy and installs physics-tenpy + matplotlib (TeNPy pins a numpy ABI, so it must not share the main .venv/anaconda base or imports hit ABI mismatches). Run scripts with .venv-tenpy/bin/python scripts/<name>.py.
• Smoke test (also run by the install target): .venv-tenpy/bin/python -c "import tenpy; print(tenpy.__version__)"
• Local API reference (key API + worked examples, with links to upstream docs): references/tenpy-api.md

Workflow

1. Confirm the model, geometry (bc_MPS='infinite' vs 'finite'), bond dimension chi_max, and conserve before code.
2. Choose the algorithm with /method-mps: iTEBD (TEBDEngine, imaginary time) is the usual reason to be in TeNPy; iDMRG / VUMPS also available. For infinite VUMPS/IDMRG production, MPSKit is the faster in-stack route — use TeNPy's VUMPS mainly as a cross-check or when staying in Python.
3. Build the model (SpinChain, Hubbard, …) and the initial MPS from a product state.
4. Run; converge on the right signal per algorithm — energy plateau and τ-refinement for iTEBD, the tangent-space gradient norm ‖B‖ (tangent_projector_test) for VUMPS/iDMRG.
5. Record chi_max, svd_min, the τ schedule and steps, conserve, the numpy/OpenMP thread env, and the final ‖B‖.

Parameter setup

Use this as the source for TeNPy-specific knobs unless the paper or official code fixes a value.

• Bond dimension chi_max — in trunc_params. The accuracy lever; D-series until energy asymptotes; scale for gapless.
• Truncation svd_min — singular values below this are dropped (e.g. 1e-14); the soft floor on the bond.
• Geometry bc_MPS — 'infinite' (uniform, with a unit cell set by the lattice L) or 'finite'. Unit cell must hold the order period (2-site for Néel; rotate to 1-site for critical XXZ).
• Symmetry conserve — None (no symmetry), 'Sz', 'parity', or 'N'. Pins the sector and speeds up; match the paper.
• iTEBD time step τ (dt) — refine in stages (0.1 → 5e-4); 2nd-order Trotter error ~τ². Build the gate with calc_U(order, dt, type_evo='imag'); evolve with evolve(N_steps, dt) (the method is evolve in TeNPy 1.1, not update). Run each τ-stage to its energy plateau — a small τ alone gives tiny per-step ΔE regardless of convergence.
• Initial state — MPS.from_lat_product_state(model.lat, [...]) in the target sector.

Knobs

Knob	Effect	Starting point
chi_max	dominant accuracy lever; cost ~χ³	χ-series until energy asymptotes
svd_min	truncation floor	1e-14
bc_MPS	infinite vs finite; unit cell	'infinite', cell = order period
conserve	block-diagonalizes → faster, pins sector	None for no symmetry; 'Sz' for speed
τ schedule (dt) + N_steps	iTEBD Trotter error + convergence	0.1→5e-4, each stage to its plateau
numpy / OpenMP threads	wall time; benchmark fairness	set OMP_NUM_THREADS etc before importing numpy; for a benchmark, confirm the count with the user

Code shape

Check exact APIs against the installed TeNPy docs before a production script; the harness-level shape:

import os
for v in ['OMP_NUM_THREADS','OPENBLAS_NUM_THREADS','MKL_NUM_THREADS']:
    os.environ[v] = '1'            # set BEFORE importing numpy; '1' for a clean benchmark
import numpy as np
from tenpy.models.spins import SpinChain
from tenpy.networks.mps import MPS
from tenpy.networks.mpo import MPOTransferMatrix
from tenpy.algorithms import tebd
from tenpy.algorithms.vumps import SingleSiteVUMPSEngine

# 1. Model (infinite, 2-site cell, no symmetry).
model = SpinChain({'L':2,'S':0.5,'Jx':1.0,'Jy':1.0,'Jz':2.0,'hz':0.0,
                   'bc_MPS':'infinite','conserve':None})

# 2. Initial product state.
psi = MPS.from_lat_product_state(model.lat, [['up'],['down']])

# 3. iTEBD: refine τ in stages, each to its energy plateau.
eng = tebd.TEBDEngine(psi, model, {'trunc_params':{'chi_max':54,'svd_min':1e-14}})
for dt in [0.1,0.05,0.02,0.01,5e-3,1e-3,5e-4]:
    eng.calc_U(2, dt, type_evo='imag')
    eng.evolve(2000, dt)          # method is evolve(N_steps, dt) in TeNPy 1.1
e_site = float(np.mean(model.bond_energies(psi)))

# 4. Tangent-space gradient norm ‖B‖ via the VUMPS engine's projector test.
p = psi.copy(); p.canonical_form()
veng = SingleSiteVUMPSEngine(p, model, {'trunc_params':{'chi_max':54,'svd_min':1e-14}})
env, _Es, _ = MPOTransferMatrix.find_init_LP_RP(model.H_MPO, veng.psi, calc_E=True)
sL, sR = veng.tangent_projector_test(env)
bnorm = float(np.sqrt(np.mean(np.asarray(sL)**2)))   # per-site RMS, left projector

Time estimate

Cost is (steps · L_cell · d · χ³) ÷ throughput.

• iTEBD per layer ∝ unit-cell × physical dim × χ³ (the SVD truncation dominates).
• iTEBD needs many steps and converges slowly near criticality — at fixed τ the per-step ΔE shrinks, so convergence is governed by τ-refinement plus total imaginary time, not step count alone.
• Memory modest (O(χ²)); compute is the gate.
• χ in the tens is seconds-to-minutes per τ-stage on a laptop; the full τ schedule for a critical system can run long — bound it.
• For a wall-time benchmark: confirm the thread count with the user (Python has no JIT warm-up, but exclude env-build and per-checkpoint measurement time) — reproduce-paper performance benchmark rules.

Use Another Route When

• The target is infinite VUMPS / IDMRG production and speed matters → MPSKit (/using-mpskit) is the faster in-stack Julia route; use TeNPy here mainly for iTEBD or a Python cross-check.
• The target is genuinely 2D / a wide system → PEPS (/using-pepskit).
• A finite Julia/ITensor workflow is preferred, or the paper used runnable official ITensor code → /using-itensors.