code: 
  
  import math
  
  import qiskit
  from qiskit import Aer, ClassicalRegister, QuantumCircuit, QuantumRegister, execute
  
  
  def       __a       (   SCREAMING_SNAKE_CASE = 1       ,   SCREAMING_SNAKE_CASE = 1      )      ->     qiskit.result.counts.Counts:
  
  
  
  
  
  
      '''simple docstring'''
      if isinstance(SCREAMING_SNAKE_CASE       ,   SCREAMING_SNAKE_CASE      ) or isinstance(SCREAMING_SNAKE_CASE       ,   SCREAMING_SNAKE_CASE      ):
          raise TypeError('''inputs must be integers.'''      )
      if (bit_a < 0) or (bit_a < 0):
          raise ValueError('''inputs must be positive.'''      )
      if (math.floor(SCREAMING_SNAKE_CASE      ) != bit_a) or (math.floor(SCREAMING_SNAKE_CASE      ) != bit_a):
          raise ValueError('''inputs must be exact integers.'''      )
      if (bit_a > 1) or (bit_a > 1):
          raise ValueError('''inputs must be less or equal to 1.'''      )
  
      # build registers
      __UpperCAmelCase									=			QuantumRegister(2       ,   '''qr'''      )
      __UpperCAmelCase									=			ClassicalRegister(2       ,   '''cr'''      )
  
      __UpperCAmelCase									=			QuantumCircuit(SCREAMING_SNAKE_CASE       ,   SCREAMING_SNAKE_CASE      )
  
      # entanglement the qubits
      quantum_circuit.h(1      )
      quantum_circuit.cx(1       ,   0      )
  
      # send the information
      __UpperCAmelCase									=			str(SCREAMING_SNAKE_CASE      ) + str(SCREAMING_SNAKE_CASE      )
  
      if c_information == "11":
          quantum_circuit.x(1      )
          quantum_circuit.z(1      )
      elif c_information == "10":
          quantum_circuit.z(1      )
      elif c_information == "01":
          quantum_circuit.x(1      )
      else:
          quantum_circuit.i(1      )
  
      # unentangled the circuit
      quantum_circuit.cx(1       ,   0      )
      quantum_circuit.h(1      )
  
      # measure the circuit
      quantum_circuit.measure(SCREAMING_SNAKE_CASE       ,   SCREAMING_SNAKE_CASE      )
  
      __UpperCAmelCase									=			Aer.get_backend('''aer_simulator'''      )
      __UpperCAmelCase									=			execute(SCREAMING_SNAKE_CASE       ,   SCREAMING_SNAKE_CASE       ,   shots=1_0_0_0      )
  
      return job.result().get_counts(SCREAMING_SNAKE_CASE      )
  
  
  if __name__ == "__main__":
         print(F"""Counts for classical state send: {superdense_coding(1,1)}""")
  
  
  
  
  
style_context: 
  
  import csv
  import json
  import os
  
  import datasets
  
  
  # TODO: Add BibTeX citation
  # Find for instance the citation on arxiv or on the dataset repo/website
  A_		:			Any										=   '\\n@InProceedings{huggingface:dataset,\ntitle = {A great new dataset},\nauthor={huggingface, Inc.\n},\nyear={2020}\n}\n'
  
  # TODO: Add description of the dataset here
  # You can copy an official description
  A_		:			Optional[Any]										=   '\\nThis new dataset is designed to solve this great NLP task and is crafted with a lot of care.\n'
  
  # TODO: Add a link to an official homepage for the dataset here
  A_		:			Dict										=   ''
  
  # TODO: Add the licence for the dataset here if you can find it
  A_		:			Dict										=   ''
  
  # TODO: Add link to the official dataset URLs here
  # The HuggingFace Datasets library doesn't host the datasets but only points to the original files.
  # This can be an arbitrary nested dict/list of URLs (see below in `_split_generators` method)
  A_		:			Union[str, Any]										=   {
      'first_domain': 'https://huggingface.co/great-new-dataset-first_domain.zip',
      'second_domain': 'https://huggingface.co/great-new-dataset-second_domain.zip',
  }
  class 					A_   (      datasets.GeneratorBasedBuilder						):
  
  
      '''simple docstring'''
  
  
  
  
  
  
      a__     							=       datasets.Version("1.1.0"						)
  
      # This is an example of a dataset with multiple configurations.
      # If you don't want/need to define several sub-sets in your dataset,
      # just remove the BUILDER_CONFIG_CLASS and the BUILDER_CONFIGS attributes.
  
      # If you need to make complex sub-parts in the datasets with configurable options
      # You can create your own builder configuration class to store attribute, inheriting from datasets.BuilderConfig
      # BUILDER_CONFIG_CLASS = MyBuilderConfig
  
      # You will be able to load one or the other configurations in the following list with
      # data = datasets.load_dataset('my_dataset', 'first_domain')
      # data = datasets.load_dataset('my_dataset', 'second_domain')
      a__     							=       [
          datasets.BuilderConfig(name="first_domain" ,     version=_a ,     description="This part of my dataset covers a first domain"						),
          datasets.BuilderConfig(name="second_domain" ,     version=_a ,     description="This part of my dataset covers a second domain"						),
      ]
  
      a__     							=       "first_domain"  # It's not mandatory to have a default configuration. Just use one if it make sense.
  
  
      def      lowerCAmelCase_						(self  )     ->      Tuple:
          # TODO: This method specifies the datasets.DatasetInfo object which contains informations and typings for the dataset
          if self.config.name == "first_domain":  # This is the name of the configuration selected in BUILDER_CONFIGS above
              __UpperCAmelCase									=			datasets.Features(
                  {
                      '''sentence''': datasets.Value('''string'''  ),
                      '''option1''': datasets.Value('''string'''  ),
                      '''answer''': datasets.Value('''string'''  )
                      # These are the features of your dataset like images, labels ...
                  }  )
          else:  # This is an example to show how to have different features for "first_domain" and "second_domain"
              __UpperCAmelCase									=			datasets.Features(
                  {
                      '''sentence''': datasets.Value('''string'''  ),
                      '''option2''': datasets.Value('''string'''  ),
                      '''second_domain_answer''': datasets.Value('''string'''  )
                      # These are the features of your dataset like images, labels ...
                  }  )
          return datasets.DatasetInfo(
              # This is the description that will appear on the datasets page.
              description=_DESCRIPTION     ,      features=lowercase__     ,      homepage=_HOMEPAGE     ,      license=_LICENSE     ,      citation=_CITATION     ,      )
  
  
      def      lowerCAmelCase_						(self     ,      lowercase__  )     ->      int:
          # TODO: This method is tasked with downloading/extracting the data and defining the splits depending on the configuration
          # If several configurations are possible (listed in BUILDER_CONFIGS), the configuration selected by the user is in self.config.name
  
          # dl_manager is a datasets.download.DownloadManager that can be used to download and extract URLS
          # It can accept any type or nested list/dict and will give back the same structure with the url replaced with path to local files.
          # By default the archives will be extracted and a path to a cached folder where they are extracted is returned instead of the archive
          __UpperCAmelCase									=			_URLS[self.config.name]
          __UpperCAmelCase									=			dl_manager.download_and_extract(lowercase__  )
          return [
              datasets.SplitGenerator(
                  name=datasets.Split.TRAIN     ,      gen_kwargs={
                      '''filepath''': os.path.join(lowercase__     ,      '''train.jsonl'''  ),
                      '''split''': '''train''',
                  }     ,      ),
              datasets.SplitGenerator(
                  name=datasets.Split.VALIDATION     ,      gen_kwargs={
                      '''filepath''': os.path.join(lowercase__     ,      '''dev.jsonl'''  ),
                      '''split''': '''dev''',
                  }     ,      ),
              datasets.SplitGenerator(
                  name=datasets.Split.TEST     ,      gen_kwargs={
                      '''filepath''': os.path.join(lowercase__     ,      '''test.jsonl'''  ),
                      '''split''': '''test'''
                  }     ,      ),
          ]
  
  
  
  
  
  
      def      lowerCAmelCase_						(self     ,      lowercase__     ,      lowercase__  )     ->      Tuple:
          # TODO: This method handles input defined in _split_generators to yield (key, example) tuples from the dataset.
          # The `key` is for legacy reasons (tfds) and is not important in itself, but must be unique for each example.
          with open(lowercase__     ,      encoding='''utf-8'''  ) as f:
              for key, row in enumerate(lowercase__  ):
                  __UpperCAmelCase									=			json.loads(lowercase__  )
                  if self.config.name == "first_domain":
                      # Yields examples as (key, example) tuples
                      yield key, {
                          "sentence": data["sentence"],
                          "option1": data["option1"],
                          "answer": "" if split == "test" else data["answer"],
                      }
                  else:
                      yield key, {
                          "sentence": data["sentence"],
                          "option2": data["option2"],
                          "second_domain_answer": "" if split == "test" else data["second_domain_answer"],
                      }
  
  
  
  
  