



'''simple docstring'''

import qiskit







def 						__UpperCamelCase				(							_lowercase,				_lowercase					) ->			qiskit.result.counts.Counts:
	_lowercase					:       Tuple       =		qiskit.Aer.get_backend('aer_simulator'					)

	_lowercase					:       Optional[Any]       =		qiskit.QuantumCircuit(4,				2					)
	# encode inputs in qubits 0 and 1
	if bita == 1:
		qc_ha.x(0					)
	if bita == 1:
		qc_ha.x(1					)
	qc_ha.barrier()

	# use cnots to write XOR of the inputs on qubit2
	qc_ha.cx(0,				2					)
	qc_ha.cx(1,				2					)

	# use ccx / toffoli gate to write AND of the inputs on qubit3
	qc_ha.ccx(0,				1,				3					)
	qc_ha.barrier()

	# extract outputs
	qc_ha.measure(2,				0					)  # extract XOR value
	qc_ha.measure(3,				1					)  # extract AND value

	# Execute the circuit on the qasm simulator
	_lowercase					:       Tuple       =		qiskit.execute(_lowercase,				_lowercase,				shots=1000					)

	# Return the histogram data of the results of the experiment
	return job.result().get_counts(_lowercase					)


if __name__ == "__main__":
						_A				:    Any     						=half_adder(1, 1)
						print(F'''Half Adder Output Qubit Counts: {counts}''')





'''simple docstring'''

_A				:    Dict     						='''
# Transformers installation
! pip install transformers datasets
# To install from source instead of the last release, comment the command above and uncomment the following one.
# ! pip install git+https://github.com/huggingface/transformers.git
'''

_A				:    Dict     						=[{'''type''': '''code''', '''content''': INSTALL_CONTENT}]
_A				:    Dict     						={
    '''{processor_class}''': '''FakeProcessorClass''',
    '''{model_class}''': '''FakeModelClass''',
    '''{object_class}''': '''FakeObjectClass''',
}
