


"""simple docstring"""



import qiskit







def     __lowerCAmelCase		(		lowercase    :    int = 2					)      ->		qiskit.result.counts.Counts:


    """simple docstring"""





    snake_case       : int    =       qubits

    # Using Aer's simulator
    snake_case       : str    =       qiskit.Aer.get_backend("aer_simulator"					)

    # Creating a Quantum Circuit acting on the q register
    snake_case       : Union[str, Any]    =       qiskit.QuantumCircuit(lowercase		,				lowercase					)

    # Adding a H gate on qubit 0 (now q0 in superposition)
    circuit.h(0					)

    for i in range(1		,				lowercase					):
        # Adding CX (CNOT) gate
        circuit.cx(i - 1		,				lowercase					)

    # Mapping the quantum measurement to the classical bits
    circuit.measure(list(range(lowercase					)					)		,				list(range(lowercase					)					)					)

    # Now measuring any one qubit would affect other qubits to collapse
    # their super position and have same state as the measured one.

    # Executing the circuit on the simulator
    snake_case       : str    =       qiskit.execute(lowercase		,				lowercase		,				shots=1000					)

    return job.result().get_counts(lowercase					)


if __name__ == "__main__":
  print(F'''Total count for various states are: {quantum_entanglement(3)}''')









"""simple docstring"""



from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__snake_case       =       {
    """configuration_time_series_transformer""": [
        """TIME_SERIES_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """TimeSeriesTransformerConfig""",
    ],
}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __snake_case       =       [
      """TIME_SERIES_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
      """TimeSeriesTransformerForPrediction""",
      """TimeSeriesTransformerModel""",
      """TimeSeriesTransformerPreTrainedModel""",
  ]


if TYPE_CHECKING:
  from .configuration_time_series_transformer import (
      TIME_SERIES_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
      TimeSeriesTransformerConfig,
  )

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_time_series_transformer import (
        TIME_SERIES_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
        TimeSeriesTransformerForPrediction,
        TimeSeriesTransformerModel,
        TimeSeriesTransformerPreTrainedModel,
    )

else:
  import sys

  __snake_case       =       _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)





