| from functools import partial |
|
|
| import jax |
| import jax.numpy as jnp |
| from jax import jit |
|
|
| from varipeps.contractions import apply_contraction_jitted |
|
|
|
|
| @partial(jit, static_argnums=(3,)) |
| def _one_site_workhorse( |
| peps_tensors, |
| peps_tensor_objs, |
| gates, |
| real_result=False, |
| ): |
| density_matrix = apply_contraction_jitted( |
| "triangular_ctmrg_one_site_expectation", |
| [peps_tensors[0]], |
| [peps_tensor_objs[0]], |
| [], |
| ) |
|
|
| 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 |
| ] |
|
|
|
|
| def calc_triangular_one_site( |
| peps_tensors, |
| peps_tensor_objs, |
| gates, |
| ): |
| if isinstance(peps_tensors, jnp.ndarray): |
| peps_tensors = (peps_tensors,) |
| peps_tensor_objs = (peps_tensor_objs,) |
|
|
| real_result = all(jnp.allclose(g, g.T.conj()) for g in gates) |
|
|
| return _one_site_workhorse( |
| peps_tensors, peps_tensor_objs, tuple(gates), real_result |
| ) |
|
|