import varipeps import jax import jax.numpy as jnp # Config Setting ## Set maximal steps for the CTMRG routine varipeps.config.ad_custom_max_steps = 100 ## Set maximal steps for the fix point routine in the gradient calculation varipeps.config.ctmrg_max_steps = 100 ## Set convergence threshold for the CTMRG routine varipeps.config.ctmrg_convergence_eps = 1e-7 ## Set convergence threshold for the fix point routine in the gradient calculation varipeps.config.ad_custom_convergence_eps = 5e-8 ## Enable/Disable printing of the convergence of the single CTMRG/gradient fix point steps. ## Useful to enable this during debugging, should be disabled for batch runs varipeps.config.ctmrg_print_steps = True varipeps.config.ad_custom_print_steps = False ## Select the method used to calculate the descent direction during optimization varipeps.config.optimizer_method = varipeps.config.Optimizing_Methods.L_BFGS ## Select the method used to calculate the (full) projectors in the CTMRG routine varipeps.config.ctmrg_full_projector_method = varipeps.config.Projector_Method.FISHMAN ## Set maximal steps for the optimization routine varipeps.config.optimizer_max_steps = 2000 ## Increase enviroment bond dimension if truncation error is below this value varipeps.config.ctmrg_heuristic_increase_chi_threshold = 1e-4 # Set constants for the simulation modelName = "HeisenbergModel" # Interaction strength J = 1 # iPEPS bond dimension chiB = 2 # Physical dimension p = 2 # Maximal enviroment bond dimension maxChi = 64 # Start value for enviroment bond dimension startChi = maxChi # define spin-1/2 matrices Id = jnp.eye(2) Sx = jnp.array([[0, 1], [1, 0]]) / 2 Sy = jnp.array([[0, -1j], [1j, 0]]) / 2 Sz = jnp.array([[1, 0], [0, -1]]) / 2 # construct Hamiltonian terms hamiltonianGates = J * (jnp.kron(Sx, Sx) + jnp.kron(Sy, Sy) + jnp.kron(Sz, Sz)) # create function to compute expectation values for the square Heisenberg AFM exp_func = ( varipeps.expectation.triangular_two_sites.Triangular_Two_Sites_Expectation_Value( horizontal_gates=(hamiltonianGates,), vertical_gates=(hamiltonianGates,), diagonal_gates=(hamiltonianGates,), real_d=p, is_spiral_peps=True, spiral_unitary_operator=Sy, ) ) # Unit cell structure structure = [[0]] # Create random initialization for the iPEPS unit cell unitcell = varipeps.peps.PEPS_Unit_Cell.random( structure, # Unit cell structure p, # Physical dimension chiB, # iPEPS bond dimension startChi, # Start value for enviroment bond dimension float, # Data type for the tensors: float (real) or complex tensors max_chi=maxChi, # Maximal enviroment bond dimension peps_type=varipeps.peps.PEPS_Type.TRIANGULAR, # Select triangular PEPS ) # Run optimization result = varipeps.optimization.optimize_unitcell_fixed_spiral_vector( unitcell, jnp.array((2 / 3, 2 / 3), dtype=jnp.float64), # Spiral vector exp_func, autosave_filename=f"data/autosave_triangular_chiB_{chiB:d}_chiMax_{maxChi:d}.hdf5", ) # Calculate magnetic expectation values Mag_Gates = [Sx, Sy, Sz] def calc_magnetic(unitcell): mag_result = [] for ti, t in enumerate(unitcell.get_unique_tensors()): r = varipeps.expectation.triangular_one_site.calc_triangular_one_site( t.tensor, t, Mag_Gates ) mag_result += r return mag_result magnetic_exp_values = calc_magnetic(result.unitcell) # Define some auxiliary data which should be stored along the final iPEPS unit cell auxiliary_data = { "best_energy": result.fun, "best_run": result.best_run, "magnetic_exp_values": magnetic_exp_values, } for k in sorted(result.max_trunc_error_list.keys()): auxiliary_data[f"max_trunc_error_list_{k:d}"] = result.max_trunc_error_list[k] auxiliary_data[f"step_energies_{k:d}"] = result.step_energies[k] auxiliary_data[f"step_chi_{k:d}"] = result.step_chi[k] auxiliary_data[f"step_conv_{k:d}"] = result.step_conv[k] auxiliary_data[f"step_runtime_{k:d}"] = result.step_runtime[k] # save full iPEPS state result.unitcell.save_to_file( f"data/heisenberg_triangular_J_{J:d}_chiB_{chiB:d}_chiMax_{maxChi:d}.hdf5", auxiliary_data=auxiliary_data, )