new black

Author: refraction-ray

docs/source/tutorials/barren_plateaus_cn.ipynb

@@ -158,7 +158,9 @@
     "    dtype=\"float32\",\n",
     ")\n",
     "\n",
-    "e, grad = op_expectation_vmap_vvag(params, seed, n, nlayers)  # 不同随机电路的 ZZ 可观测量和梯度的期望\n",
+    "e, grad = op_expectation_vmap_vvag(\n",
+    "    params, seed, n, nlayers\n",
+    ")  # 不同随机电路的 ZZ 可观测量和梯度的期望\n",
     "\n",
     "grad_var = tf.math.reduce_std(tf.math.reduce_std(grad, axis=0), axis=0)[\n",
     "    0, 0\n",

docs/source/tutorials/nnvqe_cn.ipynb

@@ -115,7 +115,9 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "def MERA(inp, n, d=1, lamb=1.0, energy_flag=False):  # 对于单变量一维XXZ模型，我们固定lamb\n",
+    "def MERA(\n",
+    "    inp, n, d=1, lamb=1.0, energy_flag=False\n",
+    "):  # 对于单变量一维XXZ模型，我们固定lamb\n",
     "    params = K.cast(inp[\"params\"], \"complex128\")\n",
     "    delta = K.cast(inp[\"delta\"], \"complex128\")\n",
     "    c = tc.Circuit(n)\n",

docs/source/tutorials/optimization_and_expressibility_cn.ipynb

@@ -276,7 +276,9 @@
     "@tf.function\n",
     "def entropy(v):\n",
     "    ψ = get_state(v)\n",
-    "    ρ_reduced = tc.quantum.reduced_density_matrix(ψ, NA)  # 部分描绘出子系统A得到的降密度矩阵\n",
+    "    ρ_reduced = tc.quantum.reduced_density_matrix(\n",
+    "        ψ, NA\n",
+    "    )  # 部分描绘出子系统A得到的降密度矩阵\n",
     "    S = tc.quantum.renyi_entropy(ρ_reduced)  # Renyi 纠缠熵\n",
     "    return S"
    ]

docs/source/tutorials/tfim_vqe_diffreph_cn.ipynb

@@ -420,7 +420,9 @@
     "\n",
     "Jx = np.array([1.0 for _ in range(n - 1)])  # xx 相互作用的强度 (OBC)\n",
     "Bz = np.array([1.0 for _ in range(n)])  # 横向场强\n",
-    "hamiltonian_mpo = tn.matrixproductstates.mpo.FiniteTFI(Jx, Bz, dtype=dtype)  # 矩阵乘积算子\n",
+    "hamiltonian_mpo = tn.matrixproductstates.mpo.FiniteTFI(\n",
+    "    Jx, Bz, dtype=dtype\n",
+    ")  # 矩阵乘积算子\n",
     "hamiltonian_mpo = quoperator_mpo(hamiltonian_mpo)  # 从 mpo 生成 QuOperator"
    ]
   },

examples/matprod_vmap.py

@@ -2,6 +2,7 @@
 matrix product: a new twist
 rewrite matrix product in a vmap style
 """
+
 from functools import partial
 
 import numpy as np

tensorcircuit/abstractcircuit.py

@@ -1,6 +1,7 @@
 """
 Methods for abstract circuits independent of nodes, edges and contractions
 """
+
 # pylint: disable=invalid-name
 
 from typing import Any, Callable, Dict, List, Optional, Sequence, Union, Tuple

tensorcircuit/applications/dqas.py

@@ -1,6 +1,7 @@
 """
 Modules for DQAS framework
 """
+
 # possibly deprecated, multiprocessing is not the recommended way to do DQAS task now, using vmap!
 
 import sys
@@ -486,9 +487,9 @@ def qaoa_simple_train(
     if "prob_model_func" in kws:
         pmf = kws["prob_model_func"]
         del kws["prob_model_func"]
-        kws[
-            "prob_model"
-        ] = pmf()  # in case keras model cannot pickled for multiprocessing map
+        kws["prob_model"] = (
+            pmf()
+        )  # in case keras model cannot pickled for multiprocessing map
     if isinstance(graph, list):
 
         def graph_generator() -> Iterator[Graph]:

tensorcircuit/applications/layers.py

@@ -1,6 +1,7 @@
 """
 Module for functions adding layers of circuits
 """
+
 import itertools
 import logging
 import sys

tensorcircuit/applications/physics/fss.py

@@ -1,6 +1,7 @@
 """
 finite size scaling tools
 """
+
 from typing import List, Tuple, Optional
 
 import numpy as np

tensorcircuit/applications/utils.py

@@ -2,6 +2,7 @@
 A collection of useful function snippets that irrelevant with the main modules
 or await for further refactor
 """
+
 import logging
 from typing import Any, Callable, Iterator, Optional, Sequence, Tuple