from dataclasses import dataclass from functools import partial import h5py import jax import jax.numpy as jnp from jax import jit from varipeps import varipeps_config from varipeps.peps import PEPS_Tensor, PEPS_Unit_Cell from varipeps.contractions import apply_contraction_jitted from .model import Expectation_Model from .spiral_helpers import apply_unitary from .triangular_two_sites import ( calc_triangular_two_sites_horizontal, calc_triangular_two_sites_vertical, calc_triangular_two_sites_diagonal, ) from varipeps.utils.debug_print import debug_print from typing import Sequence, List, Tuple, Union, Optional @partial(jit, static_argnums=(3,)) def calc_triangular_next_nearest_neg_x_pos_y( peps_tensors, peps_tensor_objs, gates, real_result=False, ): density_matrix_top_left = apply_contraction_jitted( "triangular_ctmrg_next_nearest_neg_x_pos_y_expectation_top_left", [peps_tensors[0]], [peps_tensor_objs[0]], [], ) density_matrix_top_right = apply_contraction_jitted( "triangular_ctmrg_next_nearest_neg_x_pos_y_expectation_top_right", [peps_tensors[1]], [peps_tensor_objs[1]], [], ) density_matrix_bottom_left = apply_contraction_jitted( "triangular_ctmrg_next_nearest_neg_x_pos_y_expectation_bottom_left", [peps_tensors[2]], [peps_tensor_objs[2]], [], ) density_matrix_bottom_right = apply_contraction_jitted( "triangular_ctmrg_next_nearest_neg_x_pos_y_expectation_bottom_right", [peps_tensors[3]], [peps_tensor_objs[3]], [], ) density_matrix = jnp.tensordot( density_matrix_bottom_left, density_matrix_top_left, ((5, 6, 7), (0, 1, 2)) ) density_matrix = jnp.tensordot( density_matrix, density_matrix_bottom_right, ( ( 2, 3, 4, 5, 6, ), (0, 1, 2, 3, 4), ), ) density_matrix = jnp.tensordot( density_matrix, density_matrix_top_right, ((2, 3, 4, 5, 6, 7), (0, 1, 2, 3, 4, 5)), ) density_matrix = density_matrix.transpose(0, 2, 1, 3) density_matrix = density_matrix.reshape( density_matrix.shape[0] * density_matrix.shape[1], density_matrix.shape[2] * density_matrix.shape[3], ) norm = jnp.trace(density_matrix) if real_result: return [ jnp.real(jnp.tensordot(density_matrix, g, ((0, 1), (0, 1))) / norm) for g in gates ] else: return [ jnp.tensordot(density_matrix, g, ((0, 1), (0, 1))) / norm for g in gates ] @partial(jit, static_argnums=(3,)) def calc_triangular_next_nearest_pos_x_2_pos_y( peps_tensors, peps_tensor_objs, gates, real_result=False, ): density_matrix_top_left = apply_contraction_jitted( "triangular_ctmrg_next_nearest_pos_x_2_pos_y_expectation_top_left", [peps_tensors[0]], [peps_tensor_objs[0]], [], ) density_matrix_top_right = apply_contraction_jitted( "triangular_ctmrg_next_nearest_pos_x_2_pos_y_expectation_top_right", [peps_tensors[1]], [peps_tensor_objs[1]], [], ) density_matrix_bottom_left = apply_contraction_jitted( "triangular_ctmrg_next_nearest_pos_x_2_pos_y_expectation_bottom_left", [peps_tensors[2]], [peps_tensor_objs[2]], [], ) density_matrix_bottom_right = apply_contraction_jitted( "triangular_ctmrg_next_nearest_pos_x_2_pos_y_expectation_bottom_right", [peps_tensors[3]], [peps_tensor_objs[3]], [], ) density_matrix = jnp.tensordot( density_matrix_top_left, density_matrix_top_right, ((5, 6, 7), (0, 1, 2)) ) density_matrix = jnp.tensordot( density_matrix, density_matrix_bottom_left, ( ( 2, 3, 4, 5, 6, ), (0, 1, 2, 3, 4), ), ) density_matrix = jnp.tensordot( density_matrix, density_matrix_bottom_right, ((2, 3, 4, 5, 6, 7), (0, 1, 2, 3, 4, 5)), ) density_matrix = density_matrix.transpose(0, 2, 1, 3) density_matrix = density_matrix.reshape( density_matrix.shape[0] * density_matrix.shape[1], density_matrix.shape[2] * density_matrix.shape[3], ) norm = jnp.trace(density_matrix) if real_result: return [ jnp.real(jnp.tensordot(density_matrix, g, ((0, 1), (0, 1))) / norm) for g in gates ] else: return [ jnp.tensordot(density_matrix, g, ((0, 1), (0, 1))) / norm for g in gates ] @partial(jit, static_argnums=(3,)) def calc_triangular_next_nearest_2_pos_x_pos_y( peps_tensors, peps_tensor_objs, gates, real_result=False, ): density_matrix_top = apply_contraction_jitted( "triangular_ctmrg_next_nearest_2_pos_x_pos_y_expectation_top", [peps_tensors[0]], [peps_tensor_objs[0]], [], ) density_matrix_middle_left = apply_contraction_jitted( "triangular_ctmrg_next_nearest_2_pos_x_pos_y_expectation_middle_left", [peps_tensors[1]], [peps_tensor_objs[1]], [], ) density_matrix_middle_right = apply_contraction_jitted( "triangular_ctmrg_next_nearest_2_pos_x_pos_y_expectation_middle_right", [peps_tensors[2]], [peps_tensor_objs[2]], [], ) density_matrix_bottom = apply_contraction_jitted( "triangular_ctmrg_next_nearest_2_pos_x_pos_y_expectation_bottom", [peps_tensors[3]], [peps_tensor_objs[3]], [], ) density_matrix = jnp.tensordot( density_matrix_top, density_matrix_middle_right, ((5, 6, 7), (0, 1, 2)) ) density_matrix = jnp.tensordot( density_matrix, density_matrix_middle_left, ( ( 2, 3, 4, 5, 6, ), (0, 1, 2, 3, 4), ), ) density_matrix = jnp.tensordot( density_matrix, density_matrix_bottom, ((2, 3, 4, 5, 6, 7), (0, 1, 2, 3, 4, 5)) ) density_matrix = density_matrix.transpose(0, 2, 1, 3) density_matrix = density_matrix.reshape( density_matrix.shape[0] * density_matrix.shape[1], density_matrix.shape[2] * density_matrix.shape[3], ) norm = jnp.trace(density_matrix) if real_result: return [ jnp.real(jnp.tensordot(density_matrix, g, ((0, 1), (0, 1))) / norm) for g in gates ] else: return [ jnp.tensordot(density_matrix, g, ((0, 1), (0, 1))) / norm for g in gates ] @dataclass class Triangular_Next_Nearest_Neighbor_Expectation_Value(Expectation_Model): nearest_horizontal_gates: Sequence[jnp.ndarray] nearest_vertical_gates: Sequence[jnp.ndarray] nearest_diagonal_gates: Sequence[jnp.ndarray] next_nearest_neg_x_pos_y_gates: Sequence[jnp.ndarray] next_nearest_pos_x_2_pos_y_gates: Sequence[jnp.ndarray] next_nearest_2_pos_x_pos_y_gates: Sequence[jnp.ndarray] real_d: int normalization_factor: int = 1 is_spiral_peps: bool = False spiral_unitary_operator: Optional[jnp.ndarray] = None def __post_init__(self) -> None: if isinstance(self.nearest_horizontal_gates, jnp.ndarray): self.nearest_horizontal_gates = (self.nearest_horizontal_gates,) if isinstance(self.nearest_vertical_gates, jnp.ndarray): self.nearest_vertical_gates = (self.nearest_vertical_gates,) if isinstance(self.nearest_diagonal_gates, jnp.ndarray): self.nearest_diagonal_gates = (self.nearest_diagonal_gates,) if isinstance(self.next_nearest_neg_x_pos_y_gates, jnp.ndarray): self.next_nearest_neg_x_pos_y_gates = (self.next_nearest_neg_x_pos_y_gates,) if isinstance(self.next_nearest_pos_x_2_pos_y_gates, jnp.ndarray): self.next_nearest_pos_x_2_pos_y_gates = ( self.next_nearest_pos_x_2_pos_y_gates, ) if isinstance(self.next_nearest_2_pos_x_pos_y_gates, jnp.ndarray): self.next_nearest_2_pos_x_pos_y_gates = ( self.next_nearest_2_pos_x_pos_y_gates, ) if ( len(self.nearest_horizontal_gates) > 0 and len(self.nearest_vertical_gates) > 0 and len(self.nearest_diagonal_gates) > 0 and len(self.next_nearest_neg_x_pos_y_gates) > 0 and len(self.next_nearest_pos_x_2_pos_y_gates) > 0 and len(self.next_nearest_2_pos_x_pos_y_gates) > 0 and len(self.nearest_horizontal_gates) != len(self.nearest_vertical_gates) != len(self.nearest_diagonal_gates) != len(self.next_nearest_neg_x_pos_y_gates) != len(self.next_nearest_pos_x_2_pos_y_gates) != len(self.next_nearest_2_pos_x_pos_y_gates) ): raise ValueError("Length of horizontal and vertical gates mismatch.") if self.is_spiral_peps: self._spiral_D, self._spiral_sigma = jnp.linalg.eigh( self.spiral_unitary_operator ) def __call__( self, peps_tensors: Sequence[jnp.ndarray], unitcell: PEPS_Unit_Cell, spiral_vectors: Optional[Union[jnp.ndarray, Sequence[jnp.ndarray]]] = None, *, normalize_by_size: bool = True, only_unique: bool = True, ) -> Union[jnp.ndarray, List[jnp.ndarray]]: result_type = ( jnp.float64 if all(jnp.allclose(g, g.T.conj()) for g in self.nearest_horizontal_gates) and all(jnp.allclose(g, g.T.conj()) for g in self.nearest_vertical_gates) and all(jnp.allclose(g, g.T.conj()) for g in self.nearest_diagonal_gates) and all( jnp.allclose(g, g.T.conj()) for g in self.next_nearest_neg_x_pos_y_gates ) and all( jnp.allclose(g, g.T.conj()) for g in self.next_nearest_pos_x_2_pos_y_gates ) and all( jnp.allclose(g, g.T.conj()) for g in self.next_nearest_2_pos_x_pos_y_gates ) else jnp.complex128 ) result = [ jnp.array(0, dtype=result_type) for _ in range(len(self.nearest_horizontal_gates)) ] if self.is_spiral_peps: if isinstance(spiral_vectors, jnp.ndarray): spiral_vectors = (spiral_vectors,) working_h_gates = [ apply_unitary( h, jnp.array((0, 1)), spiral_vectors, self._spiral_D, self._spiral_sigma, self.real_d, 2, (1,), varipeps_config.spiral_wavevector_type, ) for h in self.nearest_horizontal_gates ] working_v_gates = [ apply_unitary( v, jnp.array((1, 0)), spiral_vectors, self._spiral_D, self._spiral_sigma, self.real_d, 2, (1,), varipeps_config.spiral_wavevector_type, ) for v in self.nearest_vertical_gates ] working_d_gates = [ apply_unitary( d, jnp.array((1, 1)), spiral_vectors, self._spiral_D, self._spiral_sigma, self.real_d, 2, (1,), varipeps_config.spiral_wavevector_type, ) for d in self.nearest_diagonal_gates ] working_nn_neg_pos_gates = [ apply_unitary( e, jnp.array((-1, 1)), spiral_vectors, self._spiral_D, self._spiral_sigma, self.real_d, 2, (1,), varipeps_config.spiral_wavevector_type, ) for e in self.next_nearest_neg_x_pos_y_gates ] working_nn_pos_2pos_gates = [ apply_unitary( e, jnp.array((1, 2)), spiral_vectors, self._spiral_D, self._spiral_sigma, self.real_d, 2, (1,), varipeps_config.spiral_wavevector_type, ) for e in self.next_nearest_pos_x_2_pos_y_gates ] working_nn_2pos_pos_gates = [ apply_unitary( e, jnp.array((2, 1)), spiral_vectors, self._spiral_D, self._spiral_sigma, self.real_d, 2, (1,), varipeps_config.spiral_wavevector_type, ) for e in self.next_nearest_2_pos_x_pos_y_gates ] else: working_h_gates = self.nearest_horizontal_gates working_v_gates = self.nearest_vertical_gates working_d_gates = self.nearest_diagonal_gates working_nn_neg_pos_gates = self.next_nearest_neg_x_pos_y_gates working_nn_pos_2pos_gates = self.next_nearest_pos_x_2_pos_y_gates working_nn_2pos_pos_gates = self.next_nearest_2_pos_x_pos_y_gates for x, iter_rows in unitcell.iter_all_rows(only_unique=only_unique): for y, view in iter_rows: horizontal_tensors_i = view.get_indices((0, slice(0, 2, None))) horizontal_tensors = [peps_tensors[i] for i in horizontal_tensors_i[0]] horizontal_tensor_objs = view[0, :2][0] step_result_horizontal = calc_triangular_two_sites_horizontal( horizontal_tensors, horizontal_tensor_objs, working_h_gates, result_type == jnp.float64, ) vertical_tensors_i = view.get_indices((slice(0, 2, None), 0)) vertical_tensors = [ peps_tensors[vertical_tensors_i[0][0]], peps_tensors[vertical_tensors_i[1][0]], ] vertical_tensor_objs = [view[0, 0][0][0], view[1, 0][0][0]] step_result_vertical = calc_triangular_two_sites_vertical( vertical_tensors, vertical_tensor_objs, working_v_gates, result_type == jnp.float64, ) diagonal_tensors_i = view.get_indices( (slice(0, 2, None), slice(0, 2, None)) ) diagonal_tensors = [ peps_tensors[diagonal_tensors_i[0][0]], peps_tensors[diagonal_tensors_i[1][1]], ] diagonal_tensor_objs = [view[0, 0][0][0], view[1, 1][0][0]] step_result_diagonal = calc_triangular_two_sites_diagonal( diagonal_tensors, diagonal_tensor_objs, working_d_gates, result_type == jnp.float64, ) nn_neg_pos_tensors_i = view.get_indices( (slice(-1, 1, None), slice(0, 2, None)) ) nn_neg_pos_tensors = [ peps_tensors[j] for i in nn_neg_pos_tensors_i for j in i ] nn_neg_pos_tensor_objs = [j for i in view[-1:1, :2] for j in i] step_result_nn_neg_pos = calc_triangular_next_nearest_neg_x_pos_y( nn_neg_pos_tensors, nn_neg_pos_tensor_objs, working_nn_neg_pos_gates, result_type == jnp.float64, ) nn_pos_2pos_tensors_i = view.get_indices( (slice(0, 2, None), slice(0, 3, None)) ) nn_pos_2pos_tensors = [ peps_tensors[j] for i in nn_pos_2pos_tensors_i for j in i ] nn_pos_2pos_tensors = nn_pos_2pos_tensors[:2] + nn_pos_2pos_tensors[4:] nn_pos_2pos_tensor_objs = [j for i in view[:2, :3] for j in i] nn_pos_2pos_tensor_objs = ( nn_pos_2pos_tensor_objs[:2] + nn_pos_2pos_tensor_objs[4:] ) step_result_nn_pos_2pos = calc_triangular_next_nearest_pos_x_2_pos_y( nn_pos_2pos_tensors, nn_pos_2pos_tensor_objs, working_nn_pos_2pos_gates, result_type == jnp.float64, ) nn_2pos_pos_tensors_i = view.get_indices( (slice(0, 3, None), slice(0, 2, None)) ) nn_2pos_pos_tensors = [ peps_tensors[j] for i in nn_2pos_pos_tensors_i for j in i ] nn_2pos_pos_tensors = ( nn_2pos_pos_tensors[:1] + nn_2pos_pos_tensors[2:4] + nn_2pos_pos_tensors[5:] ) nn_2pos_pos_tensor_objs = [j for i in view[:3, :2] for j in i] nn_2pos_pos_tensor_objs = ( nn_2pos_pos_tensor_objs[:1] + nn_2pos_pos_tensor_objs[2:4] + nn_2pos_pos_tensor_objs[5:] ) step_result_nn_2pos_pos = calc_triangular_next_nearest_2_pos_x_pos_y( nn_2pos_pos_tensors, nn_2pos_pos_tensor_objs, working_nn_2pos_pos_gates, result_type == jnp.float64, ) for sr_i, (sr_h, sr_v, sr_d, sr_np, sr_p2p, sr_2pp) in enumerate( zip( step_result_horizontal, step_result_vertical, step_result_diagonal, step_result_nn_neg_pos, step_result_nn_pos_2pos, step_result_nn_2pos_pos, strict=True, ) ): result[sr_i] += sr_h + sr_v + sr_d + sr_np + sr_p2p + sr_2pp if normalize_by_size: if only_unique: size = unitcell.get_len_unique_tensors() else: size = unitcell.get_size()[0] * unitcell.get_size()[1] size = size * self.normalization_factor result = [r / size for r in result] if len(result) == 1: return result[0] else: return result def save_to_group(self, grp: h5py.Group): cls = type(self) grp.attrs["class"] = f"{cls.__module__}.{cls.__qualname__}" grp_gates = grp.create_group("gates", track_order=True) grp_gates.attrs["len"] = len(self.nearest_horizontal_gates) for i, ( h_g, v_g, d_g, nn_neg_pos_g, nn_pos_2_pos_g, nn_2_pos_pos_g, ) in enumerate( zip( self.nearest_horizontal_gates, self.nearest_vertical_gates, self.nearest_diagonal_gates, self.next_nearest_neg_x_pos_y_gates, self.next_nearest_pos_x_2_pos_y_gates, self.next_nearest_2_pos_x_pos_y_gates, strict=True, ) ): grp_gates.create_dataset( f"nearest_horizontal_gate_{i:d}", data=h_g, compression="gzip", compression_opts=6, ) grp_gates.create_dataset( f"nearest_vertical_gate_{i:d}", data=v_g, compression="gzip", compression_opts=6, ) grp_gates.create_dataset( f"nearest_diagonal_gate_{i:d}", data=d_g, compression="gzip", compression_opts=6, ) grp_gates.create_dataset( f"next_nearest_neg_x_pos_y_gate_{i:d}", data=nn_neg_pos_g, compression="gzip", compression_opts=6, ) grp_gates.create_dataset( f"next_nearest_pos_x_2_pos_y_gate_{i:d}", data=nn_pos_2_pos_g, compression="gzip", compression_opts=6, ) grp_gates.create_dataset( f"next_nearest_2_pos_x_pos_y_gate_{i:d}", data=nn_2_pos_pos_g, compression="gzip", compression_opts=6, ) grp.attrs["real_d"] = self.real_d grp.attrs["normalization_factor"] = self.normalization_factor grp.attrs["is_spiral_peps"] = self.is_spiral_peps if self.is_spiral_peps: grp.create_dataset( "spiral_unitary_operator", data=self.spiral_unitary_operator, compression="gzip", compression_opts=6, ) @classmethod def load_from_group(cls, grp: h5py.Group): if not grp.attrs["class"] == f"{cls.__module__}.{cls.__qualname__}": raise ValueError( "The HDF5 group suggests that this is not the right class to load data from it." ) horizontal_gates = tuple( jnp.asarray(grp["gates"][f"nearest_horizontal_gate_{i:d}"]) for i in range(grp["gates"].attrs["len"]) ) vertical_gates = tuple( jnp.asarray(grp["gates"][f"nearest_vertical_gate_{i:d}"]) for i in range(grp["gates"].attrs["len"]) ) diagonal_gates = tuple( jnp.asarray(grp["gates"][f"nearest_diagonal_gate_{i:d}"]) for i in range(grp["gates"].attrs["len"]) ) next_nearest_neg_x_pos_y_gates = tuple( jnp.asarray(grp["gates"][f"next_nearest_neg_x_pos_y_gate_{i:d}"]) for i in range(grp["gates"].attrs["len"]) ) next_nearest_pos_x_2_pos_y_gates = tuple( jnp.asarray(grp["gates"][f"next_nearest_pos_x_2_pos_y_gate_{i:d}"]) for i in range(grp["gates"].attrs["len"]) ) next_nearest_2_pos_x_pos_y_gates = tuple( jnp.asarray(grp["gates"][f"next_nearest_2_pos_x_pos_y_gate_{i:d}"]) for i in range(grp["gates"].attrs["len"]) ) is_spiral_peps = grp.attrs["is_spiral_peps"] if is_spiral_peps: spiral_unitary_operator = jnp.asarray(grp["spiral_unitary_operator"]) else: spiral_unitary_operator = None return cls( horizontal_gates=horizontal_gates, vertical_gates=vertical_gates, diagonal_gates=diagonal_gates, next_nearest_neg_x_pos_y_gates=next_nearest_neg_x_pos_y_gates, next_nearest_pos_x_2_pos_y_gates=next_nearest_pos_x_2_pos_y_gates, next_nearest_2_pos_x_pos_y_gates=next_nearest_2_pos_x_pos_y_gates, real_d=grp.attrs["real_d"], normalization_factor=grp.attrs["normalization_factor"], is_spiral_peps=is_spiral_peps, spiral_unitary_operator=spiral_unitary_operator, ) def get_nn_neg_x_pos_y_gates(vertical_e, horizontal_e, diagonal_e, nn_neg_x_pos_y_e, d): Id_other_site = jnp.eye(d**2) vertical_base = jnp.kron(vertical_e, Id_other_site) vertical_base = vertical_base.reshape(d, d, d, d, d, d, d, d) horizontal_base = jnp.kron(horizontal_e, Id_other_site) horizontal_base = horizontal_base.reshape(d, d, d, d, d, d, d, d) diagonal_base = jnp.kron(diagonal_e, Id_other_site) diagonal_base = diagonal_base.reshape(d, d, d, d, d, d, d, d) nn_neg_x_pos_y_base = jnp.kron(nn_neg_x_pos_y_e, Id_other_site) nn_neg_x_pos_y_base = nn_neg_x_pos_y_base.reshape(d, d, d, d, d, d, d, d) vertical_13 = vertical_base.transpose((0, 2, 1, 3, 4, 6, 5, 7)) vertical_13 = vertical_13.reshape(d**4, d**4) vertical_24 = vertical_base.transpose((2, 0, 3, 1, 6, 4, 7, 5)) vertical_24 = vertical_24.reshape(d**4, d**4) horizontal_12 = horizontal_base.transpose((0, 1, 2, 3, 4, 5, 6, 7)) horizontal_12 = horizontal_12.reshape(d**4, d**4) horizontal_34 = horizontal_base.transpose((2, 3, 0, 1, 6, 7, 4, 5)) horizontal_34 = horizontal_34.reshape(d**4, d**4) diagonal_14 = diagonal_base.transpose((0, 2, 3, 1, 4, 6, 7, 5)) diagonal_14 = diagonal_14.reshape(d**4, d**4) nn_neg_x_pos_y_23 = nn_neg_x_pos_y_base.transpose((2, 0, 1, 3, 6, 4, 5, 7)) nn_neg_x_pos_y_23 = nn_neg_x_pos_y_23.reshape(d**4, d**4) return ( vertical_13, vertical_24, horizontal_12, horizontal_34, diagonal_14, nn_neg_x_pos_y_23, ) def get_nn_pos_x_2_pos_y_gates( vertical_e, horizontal_e, diagonal_e, nn_pos_x_2_pos_y_e, d ): Id_other_site = jnp.eye(d**2) vertical_base = jnp.kron(vertical_e, Id_other_site) vertical_base = vertical_base.reshape(d, d, d, d, d, d, d, d) horizontal_base = jnp.kron(horizontal_e, Id_other_site) horizontal_base = horizontal_base.reshape(d, d, d, d, d, d, d, d) diagonal_base = jnp.kron(diagonal_e, Id_other_site) diagonal_base = diagonal_base.reshape(d, d, d, d, d, d, d, d) nn_pos_x_2_pos_y_base = jnp.kron(nn_pos_x_2_pos_y_e, Id_other_site) nn_pos_x_2_pos_y_base = nn_pos_x_2_pos_y_base.reshape(d, d, d, d, d, d, d, d) vertical_23 = vertical_base.transpose((2, 0, 1, 3, 6, 4, 5, 7)) vertical_23 = vertical_23.reshape(d**4, d**4) horizontal_12 = horizontal_base.transpose((0, 1, 2, 3, 4, 5, 6, 7)) horizontal_12 = horizontal_12.reshape(d**4, d**4) horizontal_34 = horizontal_base.transpose((2, 3, 0, 1, 6, 7, 4, 5)) horizontal_34 = horizontal_34.reshape(d**4, d**4) diagonal_13 = diagonal_base.transpose((0, 2, 1, 3, 4, 6, 5, 7)) diagonal_13 = diagonal_13.reshape(d**4, d**4) diagonal_24 = diagonal_base.transpose((2, 0, 3, 1, 6, 4, 7, 5)) diagonal_24 = diagonal_24.reshape(d**4, d**4) nn_pos_x_2_pos_y_14 = nn_pos_x_2_pos_y_base.transpose((0, 2, 3, 1, 4, 6, 7, 5)) nn_pos_x_2_pos_y_14 = nn_pos_x_2_pos_y_14.reshape(d**4, d**4) return ( vertical_23, horizontal_12, horizontal_34, diagonal_13, diagonal_24, nn_pos_x_2_pos_y_14, ) def get_nn_2_pos_x_pos_y_gates( vertical_e, horizontal_e, diagonal_e, nn_2_pos_x_pos_y_e, d ): Id_other_site = jnp.eye(d**2) vertical_base = jnp.kron(vertical_e, Id_other_site) vertical_base = vertical_base.reshape(d, d, d, d, d, d, d, d) horizontal_base = jnp.kron(horizontal_e, Id_other_site) horizontal_base = horizontal_base.reshape(d, d, d, d, d, d, d, d) diagonal_base = jnp.kron(diagonal_e, Id_other_site) diagonal_base = diagonal_base.reshape(d, d, d, d, d, d, d, d) nn_2_pos_x_pos_y_base = jnp.kron(nn_2_pos_x_pos_y_e, Id_other_site) nn_2_pos_x_pos_y_base = nn_2_pos_x_pos_y_base.reshape(d, d, d, d, d, d, d, d) vertical_12 = vertical_base.transpose((0, 1, 2, 3, 4, 5, 6, 7)) vertical_12 = vertical_12.reshape(d**4, d**4) vertical_34 = vertical_base.transpose((2, 3, 0, 1, 6, 7, 4, 5)) vertical_34 = vertical_34.reshape(d**4, d**4) horizontal_23 = horizontal_base.transpose((2, 0, 1, 3, 6, 4, 5, 7)) horizontal_23 = horizontal_23.reshape(d**4, d**4) diagonal_13 = diagonal_base.transpose((0, 2, 1, 3, 4, 6, 5, 7)) diagonal_13 = diagonal_13.reshape(d**4, d**4) diagonal_24 = diagonal_base.transpose((2, 0, 3, 1, 6, 4, 7, 5)) diagonal_24 = diagonal_24.reshape(d**4, d**4) nn_2_pos_x_pos_y_14 = nn_2_pos_x_pos_y_base.transpose((0, 2, 3, 1, 4, 6, 7, 5)) nn_2_pos_x_pos_y_14 = nn_2_pos_x_pos_y_14.reshape(d**4, d**4) return ( vertical_12, vertical_34, horizontal_23, diagonal_13, diagonal_24, nn_2_pos_x_pos_y_14, ) @partial(jit, static_argnums=(4, 5)) def _calc_nn_neg_x_pos_y_gate( vertical_gates: Sequence[jnp.ndarray], horizontal_gates: Sequence[jnp.ndarray], diagonal_gates: Sequence[jnp.ndarray], nn_neg_x_pos_y_gates: Sequence[jnp.ndarray], d: int, result_length: int, ): result = [None] * result_length single_gates = [None] * result_length for i, (vertical_e, horizontal_e, diagonal_e, nn_neg_x_pos_y_e) in enumerate( zip( vertical_gates, horizontal_gates, diagonal_gates, nn_neg_x_pos_y_gates, strict=True, ) ): ( vertical_13, vertical_24, horizontal_12, horizontal_34, diagonal_14, nn_neg_x_pos_y_23, ) = get_nn_neg_x_pos_y_gates( vertical_e, horizontal_e, diagonal_e, nn_neg_x_pos_y_e, d ) result[i] = ( 1 / 5 * vertical_13 + 1 / 5 * vertical_24 + 1 / 5 * horizontal_12 + 1 / 5 * horizontal_34 + 1 / 5 * diagonal_14 + nn_neg_x_pos_y_23 ) single_gates[i] = ( vertical_13, vertical_24, horizontal_12, horizontal_34, diagonal_14, nn_neg_x_pos_y_23, ) return result, single_gates @partial(jit, static_argnums=(4, 5)) def _calc_nn_pos_x_2_pos_y_gate( vertical_gates: Sequence[jnp.ndarray], horizontal_gates: Sequence[jnp.ndarray], diagonal_gates: Sequence[jnp.ndarray], nn_pos_x_2_pos_y_gates: Sequence[jnp.ndarray], d: int, result_length: int, ): result = [None] * result_length single_gates = [None] * result_length for i, (vertical_e, horizontal_e, diagonal_e, nn_pos_x_2_pos_y_e) in enumerate( zip( vertical_gates, horizontal_gates, diagonal_gates, nn_pos_x_2_pos_y_gates, strict=True, ) ): ( vertical_23, horizontal_12, horizontal_34, diagonal_13, diagonal_24, nn_pos_x_2_pos_y_14, ) = get_nn_pos_x_2_pos_y_gates( vertical_e, horizontal_e, diagonal_e, nn_pos_x_2_pos_y_e, d ) result[i] = ( 1 / 5 * vertical_23 + 1 / 5 * horizontal_12 + 1 / 5 * horizontal_34 + 1 / 5 * diagonal_13 + 1 / 5 * diagonal_24 + nn_pos_x_2_pos_y_14 ) single_gates[i] = ( vertical_23, horizontal_12, horizontal_34, diagonal_13, diagonal_24, nn_pos_x_2_pos_y_14, ) return result, single_gates @partial(jit, static_argnums=(4, 5)) def _calc_nn_2_pos_x_pos_y_gate( vertical_gates: Sequence[jnp.ndarray], horizontal_gates: Sequence[jnp.ndarray], diagonal_gates: Sequence[jnp.ndarray], nn_2_pos_x_pos_y_gates: Sequence[jnp.ndarray], d: int, result_length: int, ): result = [None] * result_length single_gates = [None] * result_length for i, (vertical_e, horizontal_e, diagonal_e, nn_2_pos_x_pos_y_e) in enumerate( zip( vertical_gates, horizontal_gates, diagonal_gates, nn_2_pos_x_pos_y_gates, strict=True, ) ): ( vertical_12, vertical_34, horizontal_23, diagonal_13, diagonal_24, nn_2_pos_x_pos_y_14, ) = get_nn_2_pos_x_pos_y_gates( vertical_e, horizontal_e, diagonal_e, nn_2_pos_x_pos_y_e, d ) result[i] = ( 1 / 5 * vertical_12 + 1 / 5 * vertical_34 + 1 / 5 * horizontal_23 + 1 / 5 * diagonal_13 + 1 / 5 * diagonal_24 + nn_2_pos_x_pos_y_14 ) single_gates[i] = ( vertical_12, vertical_34, horizontal_23, diagonal_13, diagonal_24, nn_2_pos_x_pos_y_14, ) return result, single_gates @partial(jit, static_argnums=(3,)) def calc_triangular_next_nearest_neg_x_pos_y_new( peps_tensors, peps_tensor_objs, gates, real_result=False, ): density_matrix_top_left = apply_contraction_jitted( "triangular_ctmrg_next_nearest_neg_x_pos_y_expectation_top_left_open", [peps_tensors[0]], [peps_tensor_objs[0]], [], ) density_matrix_top_right = apply_contraction_jitted( "triangular_ctmrg_next_nearest_neg_x_pos_y_expectation_top_right", [peps_tensors[1]], [peps_tensor_objs[1]], [], ) density_matrix_bottom_left = apply_contraction_jitted( "triangular_ctmrg_next_nearest_neg_x_pos_y_expectation_bottom_left", [peps_tensors[2]], [peps_tensor_objs[2]], [], ) density_matrix_bottom_right = apply_contraction_jitted( "triangular_ctmrg_next_nearest_neg_x_pos_y_expectation_bottom_right_open", [peps_tensors[3]], [peps_tensor_objs[3]], [], ) density_matrix = jnp.tensordot( density_matrix_bottom_left, density_matrix_top_left, ((5, 6, 7), (0, 1, 2)) ) density_matrix = jnp.tensordot( density_matrix, density_matrix_bottom_right, ( ( 2, 3, 4, 5, 6, ), (0, 1, 2, 3, 4), ), ) density_matrix = jnp.tensordot( density_matrix, density_matrix_top_right, ((2, 3, 4, 7, 8, 9), (0, 1, 2, 3, 4, 5)), ) density_matrix = density_matrix.transpose(2, 6, 0, 4, 3, 7, 1, 5) density_matrix = density_matrix.reshape( density_matrix.shape[0] * density_matrix.shape[1] * density_matrix.shape[2] * density_matrix.shape[3], density_matrix.shape[4] * density_matrix.shape[5] * density_matrix.shape[6] * density_matrix.shape[7], ) norm = jnp.trace(density_matrix) if real_result: return [ jnp.real(jnp.tensordot(density_matrix, g, ((0, 1), (0, 1))) / norm) for g in gates ] else: return [ jnp.tensordot(density_matrix, g, ((0, 1), (0, 1))) / norm for g in gates ] @partial(jit, static_argnums=(3,)) def calc_triangular_next_nearest_pos_x_2_pos_y_new( peps_tensors, peps_tensor_objs, gates, real_result=False, ): density_matrix_top_left = apply_contraction_jitted( "triangular_ctmrg_next_nearest_pos_x_2_pos_y_expectation_top_left", [peps_tensors[0]], [peps_tensor_objs[0]], [], ) density_matrix_top_right = apply_contraction_jitted( "triangular_ctmrg_next_nearest_pos_x_2_pos_y_expectation_top_right_open", [peps_tensors[1]], [peps_tensor_objs[1]], [], ) density_matrix_bottom_left = apply_contraction_jitted( "triangular_ctmrg_next_nearest_pos_x_2_pos_y_expectation_bottom_left_open", [peps_tensors[2]], [peps_tensor_objs[2]], [], ) density_matrix_bottom_right = apply_contraction_jitted( "triangular_ctmrg_next_nearest_pos_x_2_pos_y_expectation_bottom_right", [peps_tensors[3]], [peps_tensor_objs[3]], [], ) density_matrix = jnp.tensordot( density_matrix_top_left, density_matrix_top_right, ((5, 6, 7), (0, 1, 2)) ) density_matrix = jnp.tensordot( density_matrix, density_matrix_bottom_left, ( ( 2, 3, 4, 5, 6, ), (0, 1, 2, 3, 4), ), ) density_matrix = jnp.tensordot( density_matrix, density_matrix_bottom_right, ((2, 3, 4, 7, 8, 9), (0, 1, 2, 3, 4, 5)), ) density_matrix = density_matrix.transpose(0, 2, 4, 6, 1, 3, 5, 7) density_matrix = density_matrix.reshape( density_matrix.shape[0] * density_matrix.shape[1] * density_matrix.shape[2] * density_matrix.shape[3], density_matrix.shape[4] * density_matrix.shape[5] * density_matrix.shape[6] * density_matrix.shape[7], ) norm = jnp.trace(density_matrix) if real_result: return [ jnp.real(jnp.tensordot(density_matrix, g, ((0, 1), (0, 1))) / norm) for g in gates ] else: return [ jnp.tensordot(density_matrix, g, ((0, 1), (0, 1))) / norm for g in gates ] @partial(jit, static_argnums=(3,)) def calc_triangular_next_nearest_2_pos_x_pos_y_new( peps_tensors, peps_tensor_objs, gates, real_result=False, ): density_matrix_top = apply_contraction_jitted( "triangular_ctmrg_next_nearest_2_pos_x_pos_y_expectation_top", [peps_tensors[0]], [peps_tensor_objs[0]], [], ) density_matrix_middle_left = apply_contraction_jitted( "triangular_ctmrg_next_nearest_2_pos_x_pos_y_expectation_middle_left_open", [peps_tensors[1]], [peps_tensor_objs[1]], [], ) density_matrix_middle_right = apply_contraction_jitted( "triangular_ctmrg_next_nearest_2_pos_x_pos_y_expectation_middle_right_open", [peps_tensors[2]], [peps_tensor_objs[2]], [], ) density_matrix_bottom = apply_contraction_jitted( "triangular_ctmrg_next_nearest_2_pos_x_pos_y_expectation_bottom", [peps_tensors[3]], [peps_tensor_objs[3]], [], ) density_matrix = jnp.tensordot( density_matrix_top, density_matrix_middle_right, ((5, 6, 7), (0, 1, 2)) ) density_matrix = jnp.tensordot( density_matrix, density_matrix_middle_left, ( ( 2, 3, 4, 5, 6, ), (0, 1, 2, 3, 4), ), ) density_matrix = jnp.tensordot( density_matrix, density_matrix_bottom, ((2, 3, 4, 7, 8, 9), (0, 1, 2, 3, 4, 5)) ) density_matrix = density_matrix.transpose(0, 4, 2, 6, 1, 5, 3, 7) density_matrix = density_matrix.reshape( density_matrix.shape[0] * density_matrix.shape[1] * density_matrix.shape[2] * density_matrix.shape[3], density_matrix.shape[4] * density_matrix.shape[5] * density_matrix.shape[6] * density_matrix.shape[7], ) norm = jnp.trace(density_matrix) if real_result: return [ jnp.real(jnp.tensordot(density_matrix, g, ((0, 1), (0, 1))) / norm) for g in gates ] else: return [ jnp.tensordot(density_matrix, g, ((0, 1), (0, 1))) / norm for g in gates ] @dataclass class Triangular_Next_Nearest_Neighbor_Expectation_Value_2(Expectation_Model): nearest_horizontal_gates: Sequence[jnp.ndarray] nearest_vertical_gates: Sequence[jnp.ndarray] nearest_diagonal_gates: Sequence[jnp.ndarray] next_nearest_neg_x_pos_y_gates: Sequence[jnp.ndarray] next_nearest_pos_x_2_pos_y_gates: Sequence[jnp.ndarray] next_nearest_2_pos_x_pos_y_gates: Sequence[jnp.ndarray] real_d: int normalization_factor: int = 1 is_spiral_peps: bool = False spiral_unitary_operator: Optional[jnp.ndarray] = None def __post_init__(self) -> None: if isinstance(self.nearest_horizontal_gates, jnp.ndarray): self.nearest_horizontal_gates = (self.nearest_horizontal_gates,) if isinstance(self.nearest_vertical_gates, jnp.ndarray): self.nearest_vertical_gates = (self.nearest_vertical_gates,) if isinstance(self.nearest_diagonal_gates, jnp.ndarray): self.nearest_diagonal_gates = (self.nearest_diagonal_gates,) if isinstance(self.next_nearest_neg_x_pos_y_gates, jnp.ndarray): self.next_nearest_neg_x_pos_y_gates = (self.next_nearest_neg_x_pos_y_gates,) if isinstance(self.next_nearest_pos_x_2_pos_y_gates, jnp.ndarray): self.next_nearest_pos_x_2_pos_y_gates = ( self.next_nearest_pos_x_2_pos_y_gates, ) if isinstance(self.next_nearest_2_pos_x_pos_y_gates, jnp.ndarray): self.next_nearest_2_pos_x_pos_y_gates = ( self.next_nearest_2_pos_x_pos_y_gates, ) if ( len(self.nearest_horizontal_gates) > 0 and len(self.nearest_vertical_gates) > 0 and len(self.nearest_diagonal_gates) > 0 and len(self.next_nearest_neg_x_pos_y_gates) > 0 and len(self.next_nearest_pos_x_2_pos_y_gates) > 0 and len(self.next_nearest_2_pos_x_pos_y_gates) > 0 and len(self.nearest_horizontal_gates) != len(self.nearest_vertical_gates) != len(self.nearest_diagonal_gates) != len(self.next_nearest_neg_x_pos_y_gates) != len(self.next_nearest_pos_x_2_pos_y_gates) != len(self.next_nearest_2_pos_x_pos_y_gates) ): raise ValueError("Length of horizontal and vertical gates mismatch.") if self.is_spiral_peps: self._spiral_D, self._spiral_sigma = jnp.linalg.eigh( self.spiral_unitary_operator ) tmp_result = _calc_nn_neg_x_pos_y_gate( self.nearest_vertical_gates, self.nearest_horizontal_gates, self.nearest_diagonal_gates, self.next_nearest_neg_x_pos_y_gates, self.real_d, len(self.nearest_vertical_gates), ) self._nn_neg_x_pos_y_tuple, self._nn_neg_x_pos_y_single_gates = tuple( tmp_result[0] ), tuple(tmp_result[1]) tmp_result = _calc_nn_pos_x_2_pos_y_gate( self.nearest_vertical_gates, self.nearest_horizontal_gates, self.nearest_diagonal_gates, self.next_nearest_pos_x_2_pos_y_gates, self.real_d, len(self.nearest_vertical_gates), ) self._nn_pos_x_2_pos_y_tuple, self._nn_pos_x_2_pos_y_single_gates = tuple( tmp_result[0] ), tuple(tmp_result[1]) tmp_result = _calc_nn_2_pos_x_pos_y_gate( self.nearest_vertical_gates, self.nearest_horizontal_gates, self.nearest_diagonal_gates, self.next_nearest_2_pos_x_pos_y_gates, self.real_d, len(self.nearest_vertical_gates), ) self._nn_2_pos_x_pos_y_tuple, self._nn_2_pos_x_pos_y_single_gates = tuple( tmp_result[0] ), tuple(tmp_result[1]) def __call__( self, peps_tensors: Sequence[jnp.ndarray], unitcell: PEPS_Unit_Cell, spiral_vectors: Optional[Union[jnp.ndarray, Sequence[jnp.ndarray]]] = None, *, normalize_by_size: bool = True, only_unique: bool = True, ) -> Union[jnp.ndarray, List[jnp.ndarray]]: result_type = ( jnp.float64 if all(jnp.allclose(g, g.T.conj()) for g in self.nearest_horizontal_gates) and all(jnp.allclose(g, g.T.conj()) for g in self.nearest_vertical_gates) and all(jnp.allclose(g, g.T.conj()) for g in self.nearest_diagonal_gates) and all( jnp.allclose(g, g.T.conj()) for g in self.next_nearest_neg_x_pos_y_gates ) and all( jnp.allclose(g, g.T.conj()) for g in self.next_nearest_pos_x_2_pos_y_gates ) and all( jnp.allclose(g, g.T.conj()) for g in self.next_nearest_2_pos_x_pos_y_gates ) else jnp.complex128 ) result = [ jnp.array(0, dtype=result_type) for _ in range(len(self.nearest_horizontal_gates)) ] # set working gates working_nn_neg_pos_gates = self._nn_neg_x_pos_y_tuple working_nn_pos_2pos_gates = self._nn_pos_x_2_pos_y_tuple working_nn_2pos_pos_gates = self._nn_2_pos_x_pos_y_tuple # apply unitary transformation if spiral PEPS if self.is_spiral_peps: if isinstance(spiral_vectors, jnp.ndarray): spiral_vectors = (spiral_vectors,) working_nn_neg_pos_gates = [ apply_unitary( e, jnp.array((0, 1)), spiral_vectors, self._spiral_D, self._spiral_sigma, self.real_d, 4, (1,), varipeps_config.spiral_wavevector_type, ) for e in working_nn_neg_pos_gates ] working_nn_neg_pos_gates = [ apply_unitary( e, jnp.array((1, 0)), spiral_vectors, self._spiral_D, self._spiral_sigma, self.real_d, 4, (2,), varipeps_config.spiral_wavevector_type, ) for e in working_nn_neg_pos_gates ] working_nn_neg_pos_gates = [ apply_unitary( e, jnp.array((1, 1)), spiral_vectors, self._spiral_D, self._spiral_sigma, self.real_d, 4, (3,), varipeps_config.spiral_wavevector_type, ) for e in working_nn_neg_pos_gates ] working_nn_pos_2pos_gates = [ apply_unitary( e, jnp.array((0, 1)), spiral_vectors, self._spiral_D, self._spiral_sigma, self.real_d, 4, (1,), varipeps_config.spiral_wavevector_type, ) for e in working_nn_pos_2pos_gates ] working_nn_pos_2pos_gates = [ apply_unitary( e, jnp.array((1, 1)), spiral_vectors, self._spiral_D, self._spiral_sigma, self.real_d, 4, (2,), varipeps_config.spiral_wavevector_type, ) for e in working_nn_pos_2pos_gates ] working_nn_pos_2pos_gates = [ apply_unitary( e, jnp.array((1, 2)), spiral_vectors, self._spiral_D, self._spiral_sigma, self.real_d, 4, (3,), varipeps_config.spiral_wavevector_type, ) for e in working_nn_pos_2pos_gates ] working_nn_2pos_pos_gates = [ apply_unitary( e, jnp.array((1, 0)), spiral_vectors, self._spiral_D, self._spiral_sigma, self.real_d, 4, (1,), varipeps_config.spiral_wavevector_type, ) for e in working_nn_2pos_pos_gates ] working_nn_2pos_pos_gates = [ apply_unitary( e, jnp.array((1, 1)), spiral_vectors, self._spiral_D, self._spiral_sigma, self.real_d, 4, (2,), varipeps_config.spiral_wavevector_type, ) for e in working_nn_2pos_pos_gates ] working_nn_2pos_pos_gates = [ apply_unitary( e, jnp.array((2, 1)), spiral_vectors, self._spiral_D, self._spiral_sigma, self.real_d, 4, (3,), varipeps_config.spiral_wavevector_type, ) for e in working_nn_2pos_pos_gates ] for x, iter_rows in unitcell.iter_all_rows(only_unique=only_unique): for y, view in iter_rows: nn_neg_pos_tensors_i = view.get_indices( (slice(0, 2, None), slice(0, 2, None)) ) nn_neg_pos_tensors = [ peps_tensors[j] for i in nn_neg_pos_tensors_i for j in i ] nn_neg_pos_tensor_objs = [j for i in view[:2, :2] for j in i] step_result_nn_neg_pos = calc_triangular_next_nearest_neg_x_pos_y_new( nn_neg_pos_tensors, nn_neg_pos_tensor_objs, working_nn_neg_pos_gates, result_type == jnp.float64, ) nn_pos_2pos_tensors_i = view.get_indices( (slice(0, 2, None), slice(0, 3, None)) ) nn_pos_2pos_tensors = [ peps_tensors[j] for i in nn_pos_2pos_tensors_i for j in i ] nn_pos_2pos_tensors = nn_pos_2pos_tensors[:2] + nn_pos_2pos_tensors[4:] nn_pos_2pos_tensor_objs = [j for i in view[:2, :3] for j in i] nn_pos_2pos_tensor_objs = ( nn_pos_2pos_tensor_objs[:2] + nn_pos_2pos_tensor_objs[4:] ) step_result_nn_pos_2pos = ( calc_triangular_next_nearest_pos_x_2_pos_y_new( nn_pos_2pos_tensors, nn_pos_2pos_tensor_objs, working_nn_pos_2pos_gates, result_type == jnp.float64, ) ) nn_2pos_pos_tensors_i = view.get_indices( (slice(0, 3, None), slice(0, 2, None)) ) nn_2pos_pos_tensors = [ peps_tensors[j] for i in nn_2pos_pos_tensors_i for j in i ] nn_2pos_pos_tensors = ( nn_2pos_pos_tensors[:1] + nn_2pos_pos_tensors[2:4] + nn_2pos_pos_tensors[5:] ) nn_2pos_pos_tensor_objs = [j for i in view[:3, :2] for j in i] nn_2pos_pos_tensor_objs = ( nn_2pos_pos_tensor_objs[:1] + nn_2pos_pos_tensor_objs[2:4] + nn_2pos_pos_tensor_objs[5:] ) step_result_nn_2pos_pos = ( calc_triangular_next_nearest_2_pos_x_pos_y_new( nn_2pos_pos_tensors, nn_2pos_pos_tensor_objs, working_nn_2pos_pos_gates, result_type == jnp.float64, ) ) for sr_i, (sr_np, sr_p2p, sr_2pp) in enumerate( zip( step_result_nn_neg_pos, step_result_nn_pos_2pos, step_result_nn_2pos_pos, strict=True, ) ): result[sr_i] += sr_np + sr_p2p + sr_2pp if normalize_by_size: if only_unique: size = unitcell.get_len_unique_tensors() else: size = unitcell.get_size()[0] * unitcell.get_size()[1] size = size * self.normalization_factor result = [r / size for r in result] if len(result) == 1: return result[0] else: return result def save_to_group(self, grp: h5py.Group): cls = type(self) grp.attrs["class"] = f"{cls.__module__}.{cls.__qualname__}" grp_gates = grp.create_group("gates", track_order=True) grp_gates.attrs["len"] = len(self.nearest_horizontal_gates) for i, ( h_g, v_g, d_g, nn_neg_pos_g, nn_pos_2_pos_g, nn_2_pos_pos_g, ) in enumerate( zip( self.nearest_horizontal_gates, self.nearest_vertical_gates, self.nearest_diagonal_gates, self.next_nearest_neg_x_pos_y_gates, self.next_nearest_pos_x_2_pos_y_gates, self.next_nearest_2_pos_x_pos_y_gates, strict=True, ) ): grp_gates.create_dataset( f"nearest_horizontal_gate_{i:d}", data=h_g, compression="gzip", compression_opts=6, ) grp_gates.create_dataset( f"nearest_vertical_gate_{i:d}", data=v_g, compression="gzip", compression_opts=6, ) grp_gates.create_dataset( f"nearest_diagonal_gate_{i:d}", data=d_g, compression="gzip", compression_opts=6, ) grp_gates.create_dataset( f"next_nearest_neg_x_pos_y_gate_{i:d}", data=nn_neg_pos_g, compression="gzip", compression_opts=6, ) grp_gates.create_dataset( f"next_nearest_pos_x_2_pos_y_gate_{i:d}", data=nn_pos_2_pos_g, compression="gzip", compression_opts=6, ) grp_gates.create_dataset( f"next_nearest_2_pos_x_pos_y_gate_{i:d}", data=nn_2_pos_pos_g, compression="gzip", compression_opts=6, ) grp.attrs["real_d"] = self.real_d grp.attrs["normalization_factor"] = self.normalization_factor grp.attrs["is_spiral_peps"] = self.is_spiral_peps if self.is_spiral_peps: grp.create_dataset( "spiral_unitary_operator", data=self.spiral_unitary_operator, compression="gzip", compression_opts=6, ) @classmethod def load_from_group(cls, grp: h5py.Group): if not grp.attrs["class"] == f"{cls.__module__}.{cls.__qualname__}": raise ValueError( "The HDF5 group suggests that this is not the right class to load data from it." ) horizontal_gates = tuple( jnp.asarray(grp["gates"][f"nearest_horizontal_gate_{i:d}"]) for i in range(grp["gates"].attrs["len"]) ) vertical_gates = tuple( jnp.asarray(grp["gates"][f"nearest_vertical_gate_{i:d}"]) for i in range(grp["gates"].attrs["len"]) ) diagonal_gates = tuple( jnp.asarray(grp["gates"][f"nearest_diagonal_gate_{i:d}"]) for i in range(grp["gates"].attrs["len"]) ) next_nearest_neg_x_pos_y_gates = tuple( jnp.asarray(grp["gates"][f"next_nearest_neg_x_pos_y_gate_{i:d}"]) for i in range(grp["gates"].attrs["len"]) ) next_nearest_pos_x_2_pos_y_gates = tuple( jnp.asarray(grp["gates"][f"next_nearest_pos_x_2_pos_y_gate_{i:d}"]) for i in range(grp["gates"].attrs["len"]) ) next_nearest_2_pos_x_pos_y_gates = tuple( jnp.asarray(grp["gates"][f"next_nearest_2_pos_x_pos_y_gate_{i:d}"]) for i in range(grp["gates"].attrs["len"]) ) is_spiral_peps = grp.attrs["is_spiral_peps"] if is_spiral_peps: spiral_unitary_operator = jnp.asarray(grp["spiral_unitary_operator"]) else: spiral_unitary_operator = None return cls( horizontal_gates=horizontal_gates, vertical_gates=vertical_gates, diagonal_gates=diagonal_gates, next_nearest_neg_x_pos_y_gates=next_nearest_neg_x_pos_y_gates, next_nearest_pos_x_2_pos_y_gates=next_nearest_pos_x_2_pos_y_gates, next_nearest_2_pos_x_pos_y_gates=next_nearest_2_pos_x_pos_y_gates, real_d=grp.attrs["real_d"], normalization_factor=grp.attrs["normalization_factor"], is_spiral_peps=is_spiral_peps, spiral_unitary_operator=spiral_unitary_operator, )