





'''simple docstring'''



from __future__ import annotations

from decimal import Decimal

from numpy import array




def 					snake_case_   (		SCREAMING_SNAKE_CASE__							):







  '''simple docstring'''



  _snake_case  		=					Decimal

  # Check if the provided matrix has 2 rows and 2 columns
  # since this implementation only works for 2x2 matrices
  if len(SCREAMING_SNAKE_CASE__							) == 2 and len(matrix[0]							) == 2 and len(matrix[1]							) == 2:
    # Calculate the determinant of the matrix
    _snake_case  		=					float(
        d(matrix[0][0]							) * d(matrix[1][1]							) - d(matrix[1][0]							) * d(matrix[0][1]							)							)
    if determinant == 0:
      raise ValueError("This matrix has no inverse."							)

    # Creates a copy of the matrix with swapped positions of the elements
    _snake_case  		=					[[0.0, 0.0], [0.0, 0.0]]
    _snake_case		,		_snake_case  		=					matrix[1][1], matrix[0][0]
    _snake_case		,		_snake_case  		=					-matrix[1][0], -matrix[0][1]

    # Calculate the inverse of the matrix
    return [
        [(float(d(SCREAMING_SNAKE_CASE__							)							) / determinant) or 0.0 for n in row] for row in swapped_matrix
    ]
  elif (
      len(SCREAMING_SNAKE_CASE__							) == 3
      and len(matrix[0]							) == 3
      and len(matrix[1]							) == 3
      and len(matrix[2]							) == 3
  ):
    # Calculate the determinant of the matrix using Sarrus rule
    _snake_case  		=					float(
        (
            (d(matrix[0][0]							) * d(matrix[1][1]							) * d(matrix[2][2]							))
            + (d(matrix[0][1]							) * d(matrix[1][2]							) * d(matrix[2][0]							))
            + (d(matrix[0][2]							) * d(matrix[1][0]							) * d(matrix[2][1]							))
        )
        - (
            (d(matrix[0][2]							) * d(matrix[1][1]							) * d(matrix[2][0]							))
            + (d(matrix[0][1]							) * d(matrix[1][0]							) * d(matrix[2][2]							))
            + (d(matrix[0][0]							) * d(matrix[1][2]							) * d(matrix[2][1]							))
        )							)
    if determinant == 0:
      raise ValueError("This matrix has no inverse."							)

    # Creating cofactor matrix
    _snake_case  		=					[
        [d(0.0							), d(0.0							), d(0.0							)],
        [d(0.0							), d(0.0							), d(0.0							)],
        [d(0.0							), d(0.0							), d(0.0							)],
    ]
    _snake_case  		=					(d(matrix[1][1]							) * d(matrix[2][2]							)) - (
        d(matrix[1][2]							) * d(matrix[2][1]							)
    )
    _snake_case  		=					-(
        (d(matrix[1][0]							) * d(matrix[2][2]							)) - (d(matrix[1][2]							) * d(matrix[2][0]							))
    )
    _snake_case  		=					(d(matrix[1][0]							) * d(matrix[2][1]							)) - (
        d(matrix[1][1]							) * d(matrix[2][0]							)
    )
    _snake_case  		=					-(
        (d(matrix[0][1]							) * d(matrix[2][2]							)) - (d(matrix[0][2]							) * d(matrix[2][1]							))
    )
    _snake_case  		=					(d(matrix[0][0]							) * d(matrix[2][2]							)) - (
        d(matrix[0][2]							) * d(matrix[2][0]							)
    )
    _snake_case  		=					-(
        (d(matrix[0][0]							) * d(matrix[2][1]							)) - (d(matrix[0][1]							) * d(matrix[2][0]							))
    )
    _snake_case  		=					(d(matrix[0][1]							) * d(matrix[1][2]							)) - (
        d(matrix[0][2]							) * d(matrix[1][1]							)
    )
    _snake_case  		=					-(
        (d(matrix[0][0]							) * d(matrix[1][2]							)) - (d(matrix[0][2]							) * d(matrix[1][0]							))
    )
    _snake_case  		=					(d(matrix[0][0]							) * d(matrix[1][1]							)) - (
        d(matrix[0][1]							) * d(matrix[1][0]							)
    )

    # Transpose the cofactor matrix (Adjoint matrix)
    _snake_case  		=					array(SCREAMING_SNAKE_CASE__							)
    for i in range(3							):
      for j in range(3							):
        _snake_case  		=					cofactor_matrix[j][i]

        # Inverse of the matrix using the formula (1/determinant) * adjoint matrix
    _snake_case  		=					array(SCREAMING_SNAKE_CASE__							)
    for i in range(3							):
      for j in range(3							):
        inverse_matrix[i][j] /= d(SCREAMING_SNAKE_CASE__							)

        # Calculate the inverse of the matrix
    return [[float(d(SCREAMING_SNAKE_CASE__							)							) or 0.0 for n in row] for row in inverse_matrix]
  raise ValueError("Please provide a matrix of size 2x2 or 3x3."							)








import qiskit

def lowercase    (  SCREAMING_SNAKE_CASE = 2 )			->							qiskit.result.counts.Counts:


			'''simple docstring'''







			SCREAMING_SNAKE_CASE_								=						qubits

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

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

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

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

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

			# 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
			SCREAMING_SNAKE_CASE_								=						qiskit.execute(SCREAMING_SNAKE_CASE       ,    SCREAMING_SNAKE_CASE       ,    shots=10_00 )

			return job.result().get_counts(SCREAMING_SNAKE_CASE )


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