tenax

Guide graduate students through iPEPS calculations for 2D quantum lattice models using Tenax. Covers the full pipeline: simple update (imaginary time evolution), AD-based ground-state optimization via optimize_gs_ad with CTM environments, and quasiparticle excitation spectra via compute_excitations. Supports 1-site and 2-site unit cells. Use this skill when the user mentions iPEPS, PEPS, 2D tensor networks, projected entangled pair states, corner transfer matrix (CTM), simple update, imaginary time evolution for 2D, AD optimization of tensor networks, quasiparticle excitations, or Brillouin zone dispersion. Also trigger for "2D Heisenberg ground state", "Neel order", "2D phase diagram", or any request to go beyond 1D DMRG to 2D systems.

Help users design and run performance benchmarks for Tenax algorithms across hardware backends (CPU, CUDA, TPU, Metal). Generates the correct CLI commands for python -m benchmarks.run, interprets JSON/CSV output, and helps compare results across backends and algorithm configurations. Use this skill when the user mentions benchmarks, performance, timing, speed comparison, GPU vs CPU, profiling, or wants to measure how fast DMRG/iDMRG/TRG/iPEPS runs. Also trigger for "how fast is", "performance test", "scaling study", or "which backend is faster".

Diagnose and fix errors in Tenax tensor network code running on JAX. Covers four categories: (1) shape mismatches and tensor dimension errors, (2) JAX tracing and jit compilation issues, (3) gradient and autodiff problems, and (4) convergence failures in DMRG, iDMRG, TRG, iPEPS, and other algorithms. Use this skill whenever the user pastes a Tenax traceback, mentions a runtime error involving tensors or contractions, reports NaNs or non-converging energy, asks "why is my DMRG not converging", complains about slow JAX compilation, or shares code that produces unexpected shapes. Also trigger when the user says "debug", "error", "broken", "wrong shape", "NaN", or "doesn't converge" in the context of tensor networks, Tenax, tenax, or JAX. Trigger for SymmetricTensor errors, label mismatch, FlowDirection issues, or AutoMPO build failures.

Guide graduate students through a complete DMRG ground-state calculation using Tenax, from defining the Hamiltonian through AutoMPO to analyzing results. Pedagogical and step-by-step. Covers finite DMRG, iDMRG (1D chain and infinite cylinder), and 2D cylinder DMRG. Use this skill whenever the user wants to run DMRG, find a ground state with tensor networks, set up an MPS calculation, build an MPO Hamiltonian, or asks about DMRG sweeps, bond dimensions, canonical forms, or convergence — especially in the context of Tenax or tenax. Also trigger for "ground state energy", "variational MPS", "finite DMRG", "infinite DMRG", "iDMRG", "cylinder DMRG", "AutoMPO", "Heisenberg chain", "build MPO", or "how do I use DMRG for [model]".

Help users migrate tensor network code from ITensor (Julia/C++) to Tenax. Maps ITensor concepts (Index, ITensor, MPS, MPO, AutoMPO, siteinds) to Tenax equivalents. Use this skill when the user mentions ITensor, "coming from ITensor", "ITensor equivalent", "Julia tensor networks", or asks how Tenax compares to ITensor. Also trigger for "migrate from ITensor" or "I used to use ITensor".

Help users migrate tensor network code from TeNPy to Tenax. Maps TeNPy concepts (Site, MPS, MPO, Model, Engine) to Tenax equivalents. Use this skill when the user mentions TeNPy, "coming from TeNPy", "TeNPy equivalent", or asks how Tenax compares to TeNPy. Also trigger for "migrate from TeNPy", "I used to use TeNPy", or "tenpy".

Guide users through computing physical observables from Tenax ground states: local expectation values, correlation functions, entanglement entropy, and order parameters from MPS (DMRG/iDMRG) and iPEPS states. Use this skill when the user asks "how do I compute observables", "measure correlation function", "entanglement entropy", "expectation value", "order parameter", "magnetization", "structure factor", or "what can I measure from my ground state". Also trigger for "post-DMRG analysis", "reduced density matrix", or "how to extract physics from MPS".

Teach users Tenax's core tensor operations: creating DenseTensor and SymmetricTensor, label-based contraction, truncated SVD, full SVD, eigendecomposition (eigh), QR decomposition, and the TensorNetwork graph container. Use this skill when the user asks about basic tensor operations, "how does contraction work", "how to do SVD", "eigendecomposition", "create a tensor", "what are labels", DenseTensor, contract, truncated_svd, svd, eigh, qr_decompose, or wants to understand the building blocks before running algorithms. Also trigger for "tensor basics", "label-based contraction", "how do I contract two tensors", "entanglement spectrum", or "diagonalize a density matrix".

Run exact diagonalization (ED) and DMRG side-by-side on small quantum systems to teach students why DMRG works and where it breaks down. Compares ground state energies, wavefunctions, entanglement spectra, and correlation functions between the exact result and the DMRG approximation at various bond dimensions. Use this skill when the user wants to validate DMRG, compare exact vs approximate results, understand truncation error, study entanglement, or asks "is my DMRG result correct", "how accurate is DMRG at bond dimension χ", "what does the entanglement spectrum look like", or "show me why DMRG works". Also trigger for exact diagonalization, ED, Lanczos, full diagonalization, wavefunction overlap, fidelity, or Schmidt decomposition.

Help users install Tenax, configure JAX backends (CPU, CUDA, TPU, Metal), verify the installation, and run their first tensor network calculation. Use this skill for onboarding questions: "how do I install Tenax", "set up GPU", "first DMRG run", "getting started", "hello world", or any request from a new user who hasn't used the library before.

Generate Tenax homework problems for a graduate solid state physics course. Creates scaffolded assignments with starter code, hints, and solutions, tied to specific course topics. Supports four tiers of prompt complexity: factual recall, analytical application, synthesis/research, and adversarial (testing whether the student/model identifies incorrect premises). Use this skill when the instructor asks to create homework, assignments, problem sets, exercises, or exam questions involving Tenax, tensor networks, DMRG, or computational physics. Also trigger for "generate a problem about [topic]", "create a benchmark prompt", "make an exercise for week N", or "design an adversarial question".

Help users migrate tensor network code from quimb to Tenax. Maps quimb concepts (Tensor, TensorNetwork, DMRG, TEBD) to Tenax equivalents. Use this skill when the user mentions quimb, "coming from quimb", "quimb equivalent", or asks how Tenax compares to quimb. Also trigger for "migrate from quimb" or "I used to use quimb".

Guide users through fermionic iPEPS (fPEPS) calculations using Tenax's graded tensor formalism. Covers spinless fermions, the t-V model, FermionParity and FermionicU1 symmetries, and the fpeps() entry point. Use this skill when the user mentions fermionic PEPS, fPEPS, fermionic tensor networks, spinless fermions, t-V model, Hubbard model on a 2D lattice, FermionParity, FermionicU1, graded tensors, Koszul signs, fermionic exchange statistics, or Jordan-Wigner strings in 2D. Also trigger for "fermion sign problem in tensor networks", "anticommutation in PEPS", or "how do I handle fermions in 2D".

Build Hamiltonian MPOs from natural-language model descriptions using Tenax's AutoMPO. Translates physics ("Heisenberg ladder with NNN coupling and staggered field") into correct AutoMPO code with proper prefactors, site ordering, custom operators, compress and symmetric flags. Use this skill whenever the user wants to build an MPO, construct a Hamiltonian, set up a spin model, define coupling terms, create an operator for DMRG, or asks about AutoMPO, build_auto_mpo, operator terms, Sp Sm Sz conventions, site ordering for cylinders/ladders, or "how do I write the Hamiltonian for [model]". Also trigger for custom operators, NNN interactions, long-range couplings, staggered fields, or multi-site unit cells.

Teach users to design tensor network contractions using Tenax's NetworkBlueprint and TensorNetwork classes. Translates tensor network diagrams (described verbally or visually) into .net topology strings, then shows how to load tensors and contract efficiently. Use this skill when the user asks about NetworkBlueprint, TensorNetwork, .net files, tensor network contraction, contraction order, opt_einsum, "how to contract a network", "define a tensor network topology", or wants to build custom algorithms beyond DMRG/TRG/iPEPS. Also trigger for DMRG environment contractions, transfer matrices, expectation values, or any multi-tensor contraction pattern.

Help users migrate tensor network code from Cytnx to Tenax. Maps Cytnx concepts (UniTensor, Bond, Network) to their Tenax equivalents (DenseTensor, SymmetricTensor, TensorIndex, TensorNetwork, NetworkBlueprint), translates code patterns, and explains key design differences. Use this skill when the user mentions Cytnx, UniTensor, Bond, "coming from Cytnx", "convert Cytnx code", "Cytnx vs Tenax", or asks how Tenax compares to Cytnx. Also trigger for "migrate from Cytnx", "Cytnx equivalent", or "I used to use Cytnx".

Teach graduate students Tenax's symmetry system from the ground up: U(1) and Z_n symmetries, TensorIndex with charges and FlowDirection, SymmetricTensor construction and operations, block-sparse SVD and QR, and how symmetries connect to physics (charge conservation, selection rules). Use this skill when the user asks about SymmetricTensor, U1Symmetry, ZnSymmetry, TensorIndex, FlowDirection, block-sparse tensors, charge sectors, symmetry-aware DMRG, "what is FlowDirection", "how do charges work", "why use symmetric tensors", or wants to understand the block structure of tensors in Tenax.

Guide graduate students through Tensor Renormalization Group (TRG) and HOTRG calculations for 2D classical statistical mechanics models using Tenax. Covers building the initial tensor from the partition function, running coarse-graining, extracting free energy and thermodynamic quantities, and studying phase transitions. Use this skill when the user mentions TRG, HOTRG, tensor renormalization group, classical partition function, 2D Ising model, transfer matrix, coarse-graining, real-space renormalization, free energy calculation, critical temperature, critical exponents, or "how to study phase transitions with tensor networks". Also trigger for compute_ising_tensor, TRGConfig, or classical stat mech problems.