

"""simple docstring"""






import qiskit





def 	lowercase      (   __UpperCamelCase = 2						) ->      List[Any]:
 __magic_name__     =      qubits

 # Using Aer's simulator
 __magic_name__     =      qiskit.Aer.get_backend('''aer_simulator'''						)

 # Creating a Quantum Circuit acting on the q register
 __magic_name__     =      qiskit.QuantumCircuit(UpperCAmelCase__      ,   UpperCAmelCase__						)

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

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

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

 # 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
 __magic_name__     =      qiskit.execute(UpperCAmelCase__      ,   UpperCAmelCase__      ,   shots=1000						)

 return job.result().get_counts(UpperCAmelCase__						)


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_tokenizers_available, is_torch_available


__lowerCamelCase										=		{
    "configuration_m2m_100": ["M2M_100_PRETRAINED_CONFIG_ARCHIVE_MAP", "M2M100Config", "M2M100OnnxConfig"],
    "tokenization_m2m_100": ["M2M100Tokenizer"],
}


try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __lowerCamelCase										=		[
     "M2M_100_PRETRAINED_MODEL_ARCHIVE_LIST",
     "M2M100ForConditionalGeneration",
     "M2M100Model",
     "M2M100PreTrainedModel",
 ]


if TYPE_CHECKING:
 from .configuration_mam_aaa import M2M_100_PRETRAINED_CONFIG_ARCHIVE_MAP, MaMaaaConfig, MaMaaaOnnxConfig
 from .tokenization_mam_aaa import MaMaaaTokenizer

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_mam_aaa import (
      M2M_100_PRETRAINED_MODEL_ARCHIVE_LIST,
      MaMaaaForConditionalGeneration,
      MaMaaaModel,
      MaMaaaPreTrainedModel,
  )


else:
 import sys

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

