


"""simple docstring"""






import qiskit
def 	lowerCamelCase			(	_UpperCamelCase						: Union[str, Any] = 2				)						->			qiskit.result.counts.Counts:

							'''simple docstring'''



							__UpperCAmelCase  :		Optional[int] 				=   qubits

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

							# Creating a Quantum Circuit acting on the q register
							__UpperCAmelCase  :		Optional[int] 				=   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
							__UpperCAmelCase  :		str 				=   qiskit.execute(UpperCAmelCase_			,     UpperCAmelCase_			,     shots=1_0_0_0				)

							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


a__					:			Dict          =     {
    '''configuration_canine''': ['''CANINE_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''CanineConfig'''],
    '''tokenization_canine''': ['''CanineTokenizer'''],
}

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			a__					:			Union[str, Any]          =     [
			    '''CANINE_PRETRAINED_MODEL_ARCHIVE_LIST''',
			    '''CanineForMultipleChoice''',
			    '''CanineForQuestionAnswering''',
			    '''CanineForSequenceClassification''',
			    '''CanineForTokenClassification''',
			    '''CanineLayer''',
			    '''CanineModel''',
			    '''CaninePreTrainedModel''',
			    '''load_tf_weights_in_canine''',
			]


if TYPE_CHECKING:
			from .configuration_canine import CANINE_PRETRAINED_CONFIG_ARCHIVE_MAP, CanineConfig
			from .tokenization_canine import CanineTokenizer

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_canine import (
						    CANINE_PRETRAINED_MODEL_ARCHIVE_LIST,
						    CanineForMultipleChoice,
						    CanineForQuestionAnswering,
						    CanineForSequenceClassification,
						    CanineForTokenClassification,
						    CanineLayer,
						    CanineModel,
						    CaninePreTrainedModel,
						    load_tf_weights_in_canine,
						)


else:
			import sys

			a__					:			List[Any]          =     _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)



