

import qiskit


def        __lowercase    (  UpperCAmelCase__ = 2      ):

					"""simple docstring"""






					__lowerCAmelCase					=							qubits

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

					# Creating a Quantum Circuit acting on the q register
					__lowerCAmelCase					=							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
					__lowerCAmelCase					=							qiskit.execute(UpperCAmelCase__		,					UpperCAmelCase__		,					shots=1_000      )

					return job.result().get_counts(UpperCAmelCase__      )


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









from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_torch_available,
)


lowerCamelCase      				=     {'''configuration_unispeech''': ['''UNISPEECH_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''UniSpeechConfig''']}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowerCamelCase      				=     [
		    '''UNISPEECH_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''UniSpeechForCTC''',
		    '''UniSpeechForPreTraining''',
		    '''UniSpeechForSequenceClassification''',
		    '''UniSpeechModel''',
		    '''UniSpeechPreTrainedModel''',
		]

if TYPE_CHECKING:
		from .configuration_unispeech import UNISPEECH_PRETRAINED_CONFIG_ARCHIVE_MAP, UniSpeechConfig

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_unispeech import (
				    UNISPEECH_PRETRAINED_MODEL_ARCHIVE_LIST,
				    UniSpeechForCTC,
				    UniSpeechForPreTraining,
				    UniSpeechForSequenceClassification,
				    UniSpeechModel,
				    UniSpeechPreTrainedModel,
				)

else:
		import sys

		lowerCamelCase      				=     _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)






