fix example pylint error

Author: refraction-ray

README.md

@@ -35,7 +35,7 @@ TensorCircuit-NG is the only actively maintained official version and a [fully c
 
 Please begin with [Quick Start](/docs/source/quickstart.rst) in the [full documentation](https://tensorcircuit-ng.readthedocs.io/).
 
-For more information on software usage, sota algorithm implementation and engineer paradigm demonstration, please refer to 90+ [example scripts](/examples) and 40+ [tutorial notebooks](https://tensorcircuit-ng.readthedocs.io/en/latest/#tutorials). API docstrings and test cases in [tests](/tests) are also informative. One can also refer to AI-native docs for tensorcircuit-ng: [Devin Deepwiki](https://deepwiki.com/tensorcircuit/tensorcircuit-ng) and [Context7 MCP](https://context7.com/tensorcircuit/tensorcircuit-ng).
+For more information on software usage, sota algorithm implementation and engineer paradigm demonstration, please refer to 100+ [example scripts](/examples) and 40+ [tutorial notebooks](https://tensorcircuit-ng.readthedocs.io/en/latest/#tutorials). API docstrings and test cases in [tests](/tests) are also informative. One can also refer to AI-native docs for tensorcircuit-ng: [Devin Deepwiki](https://deepwiki.com/tensorcircuit/tensorcircuit-ng) and [Context7 MCP](https://context7.com/tensorcircuit/tensorcircuit-ng).
 
 For beginners, please refer to [quantum computing lectures with TC-NG](https://github.com/sxzgroup/qc_lecture) to learn both quantum computing basics and representative usage of TensorCircuit-NG.
 

examples/multihost_vqe.py

@@ -10,7 +10,7 @@
 """
 
 from itertools import product
-from typing import Dict, Tuple, Optional
+from typing import Dict, Tuple
 
 import numpy as np
 import tensorflow as tf
@@ -531,7 +531,8 @@ def main():
     fldc_num_layers = 3
     maxiter = 1000
     # For each ansatz, there are 100 independent VQE trials
-    # And the data are averaged over the best half of the 100 training trajectories starting from different initializations.
+    # And the data are averaged over the best half of the 100 training trajectories
+    # starting from different initializations.
     # It's time consuming to run GLDC ansatz with 100 trials; consider reducing trials for testing.
     # And GLDC ansatz requires NUM_QUBITS layers FDC structure, which may lead to very long training time.
     trials = 100