





'''simple docstring'''




import qiskit







def _lowerCAmelCase     (      _lowerCAmelCase = 2    )->     qiskit.result.counts.Counts:
 __UpperCAmelCase					=   qubits

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

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

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

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

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

 # 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
 __UpperCAmelCase					=   qiskit.execute(_lowerCAmelCase       ,    _lowerCAmelCase       ,    shots=10_00    )

 return job.result().get_counts(_lowerCAmelCase    )


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


_A:					List[str]							=				{
    """configuration_luke""": ["""LUKE_PRETRAINED_CONFIG_ARCHIVE_MAP""", """LukeConfig"""],
    """tokenization_luke""": ["""LukeTokenizer"""],
}

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    _A:					Union[str, Any]							=				[
        """LUKE_PRETRAINED_MODEL_ARCHIVE_LIST""",
        """LukeForEntityClassification""",
        """LukeForEntityPairClassification""",
        """LukeForEntitySpanClassification""",
        """LukeForMultipleChoice""",
        """LukeForQuestionAnswering""",
        """LukeForSequenceClassification""",
        """LukeForTokenClassification""",
        """LukeForMaskedLM""",
        """LukeModel""",
        """LukePreTrainedModel""",
    ]


if TYPE_CHECKING:
    from .configuration_luke import LUKE_PRETRAINED_CONFIG_ARCHIVE_MAP, LukeConfig
    from .tokenization_luke import LukeTokenizer

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_luke import (
            LUKE_PRETRAINED_MODEL_ARCHIVE_LIST,
            LukeForEntityClassification,
            LukeForEntityPairClassification,
            LukeForEntitySpanClassification,
            LukeForMaskedLM,
            LukeForMultipleChoice,
            LukeForQuestionAnswering,
            LukeForSequenceClassification,
            LukeForTokenClassification,
            LukeModel,
            LukePreTrainedModel,
        )

else:
    import sys

    _A:					str							=				_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)





