code: 
  
  
  
  
  """simple docstring"""
  
  
  
  
  import random
  class 							lowerCamelCase		:
  
  
  
  
  
  
  
     def __init__(							self    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_ = 0.01    ,      SCREAMING_SNAKE_CASE_ = 1000    ,      SCREAMING_SNAKE_CASE_ = -1    ,      ):
         UpperCamelCase				:   Tuple          =							sample
         if len(self.sample ) == 0:
             raise ValueError("""Sample data can not be empty""" )
         UpperCamelCase				:   List[Any]          =							target
         if len(self.target ) == 0:
             raise ValueError("""Target data can not be empty""" )
         if len(self.sample ) != len(self.target ):
             raise ValueError("""Sample data and Target data do not have matching lengths""" )
         UpperCamelCase				:   List[str]          =							learning_rate
         UpperCamelCase				:   Tuple          =							epoch_number
         UpperCamelCase				:   List[Any]          =							bias
         UpperCamelCase				:   Optional[int]          =							len(SCREAMING_SNAKE_CASE_ )
         UpperCamelCase				:   str          =							len(sample[0] )  # number of columns in dataset
         UpperCamelCase				:   Dict          =							[]
  
  
  
  
  
  
  
     def       a_							(							self ):
  
  
  
  
  
  
         for sample in self.sample:
             sample.insert(0    ,      self.bias )
  
         for _ in range(self.col_sample ):
             self.weight.append(random.random() )
  
         self.weight.insert(0    ,      self.bias )
  
         UpperCamelCase				:   Optional[int]          =							0
  
         while True:
             UpperCamelCase				:   Tuple          =							False
             for i in range(self.number_sample ):
                 UpperCamelCase				:   List[str]          =							0
                 for j in range(self.col_sample + 1 ):
                     UpperCamelCase				:   Optional[Any]          =							u + self.weight[j] * self.sample[i][j]
                 UpperCamelCase				:   List[Any]          =							self.sign(SCREAMING_SNAKE_CASE_ )
                 if y != self.target[i]:
                     for j in range(self.col_sample + 1 ):
                         UpperCamelCase				:   List[str]          =							(
                             self.weight[j]
                             + self.learning_rate
                             * (self.target[i] - y)
                             * self.sample[i][j]
                         )
                     UpperCamelCase				:   List[Any]          =							True
              # print('Epoch: \n',epoch_count)
             UpperCamelCase				:   List[Any]          =							epoch_count + 1
             # if you want control the epoch or just by error
             if not has_misclassified:
                 print(("""\nEpoch:\n""", epoch_count) )
                 print("""------------------------\n""" )
                 # if epoch_count > self.epoch_number or not error:
                 break
  
  
  
  
  
  
  
     def       a_							(							self    ,      SCREAMING_SNAKE_CASE_ ):
  
  
  
  
  
  
         if len(self.sample ) == 0:
             raise ValueError("""Sample data can not be empty""" )
         sample.insert(0    ,      self.bias )
         UpperCamelCase				:   str          =							0
         for i in range(self.col_sample + 1 ):
             UpperCamelCase				:   Dict          =							u + self.weight[i] * sample[i]
  
         UpperCamelCase				:   Any          =							self.sign(SCREAMING_SNAKE_CASE_ )
  
         if y == -1:
             print(("""Sample: """, sample) )
             print("""classification: P1""" )
         else:
             print(("""Sample: """, sample) )
             print("""classification: P2""" )
  
  
  
  
  
  
  
     def       a_							(							self    ,      SCREAMING_SNAKE_CASE_ ):
         return 1 if u >= 0 else -1
  
  
  
  
  
  
  
  __A :		Optional[int]      							=    [
      [-0.6508, 0.1097, 4.0009],
      [-1.4492, 0.8896, 4.4005],
      [2.0850, 0.6876, 12.0710],
      [0.2626, 1.1476, 7.7985],
      [0.6418, 1.0234, 7.0427],
      [0.2569, 0.6730, 8.3265],
      [1.1155, 0.6043, 7.4446],
      [0.0914, 0.3399, 7.0677],
      [0.0121, 0.5256, 4.6316],
      [-0.0429, 0.4660, 5.4323],
      [0.4340, 0.6870, 8.2287],
      [0.2735, 1.0287, 7.1934],
      [0.4839, 0.4851, 7.4850],
      [0.4089, -0.1267, 5.5019],
      [1.4391, 0.1614, 8.5843],
      [-0.9115, -0.1973, 2.1962],
      [0.3654, 1.0475, 7.4858],
      [0.2144, 0.7515, 7.1699],
      [0.2013, 1.0014, 6.5489],
      [0.6483, 0.2183, 5.8991],
      [-0.1147, 0.2242, 7.2435],
      [-0.7970, 0.8795, 3.8762],
      [-1.0625, 0.6366, 2.4707],
      [0.5307, 0.1285, 5.6883],
      [-1.2200, 0.7777, 1.7252],
      [0.3957, 0.1076, 5.6623],
      [-0.1013, 0.5989, 7.1812],
      [2.4482, 0.9455, 11.2095],
      [2.0149, 0.6192, 10.9263],
      [0.2012, 0.2611, 5.4631],
  ]
  
  __A :		Optional[int]      							=    [
      -1,
      -1,
      -1,
      1,
      1,
      -1,
      1,
      -1,
      1,
      1,
      -1,
      1,
      -1,
      -1,
      -1,
      -1,
      1,
      1,
      1,
      1,
      -1,
      1,
      1,
      1,
      1,
      -1,
      -1,
      1,
      -1,
      1,
  ]
  
  
  if __name__ == "__main__":
         import doctest
  
         doctest.testmod()
  
         __A :		str      							=    Perceptron(
             sample=samples, target=target, learning_rate=0.01, epoch_number=1000, bias=-1
         )
         network.training()
         print('''Finished training perceptron''')
         print('''Enter values to predict or q to exit''')
         while True:
                __A :		list      							=    []
                for i in range(len(samples[0])):
                       __A :		Dict      							=    input('''value: ''').strip()
                       if user_input == "q":
                              break
                       __A :		Union[str, Any]      							=    float(user_input)
                       sample.insert(i, observation)
                network.sort(sample)
  
  
  
  
  
style_context: 
  
  
  
  
  """simple docstring"""
  
  
  
  
  import numpy as np
  import qiskit
  from qiskit import Aer, ClassicalRegister, QuantumCircuit, QuantumRegister, execute
  def 					A_			(		snake_case_     :       float = np.pi / 2						,snake_case_     :       float = np.pi / 2						,snake_case_     :       float = np.pi / 2    ):
  
  
  
  
  
  
  
      '''simple docstring'''
      UpperCamelCase				:   Union[str, Any]          =							QuantumRegister(3						,"""qr"""    )  # Define the number of quantum bits
      UpperCamelCase				:   Any          =							ClassicalRegister(1						,"""cr"""    )  # Define the number of classical bits
  
      UpperCamelCase				:   Any          =							QuantumCircuit(snake_case_						,snake_case_    )  # Define the quantum circuit.
  
      # Build the circuit
      quantum_circuit.u(snake_case_						,snake_case_						,snake_case_						,0    )  # Quantum State to teleport
      quantum_circuit.h(1    )  # add hadamard gate
      quantum_circuit.cx(
          1						,2    )  # add control gate with qubit 1 as control and 2 as target.
      quantum_circuit.cx(0						,1    )
      quantum_circuit.h(0    )
      quantum_circuit.cz(0						,2    )  # add control z gate.
      quantum_circuit.cx(1						,2    )
  
      quantum_circuit.measure([2]						,[0]    )  # measure the qubit.
  
      # Simulate the circuit using qasm simulator
      UpperCamelCase				:   Tuple          =							Aer.get_backend("""aer_simulator"""    )
      UpperCamelCase				:   Any          =							execute(snake_case_						,snake_case_						,shots=1_0_0_0    )
  
      return job.result().get_counts(snake_case_    )
  
  
  if __name__ == "__main__":
    print(
        '''Total count for teleported state is: '''
        F'''{quantum_teleportation(np.pi/2, np.pi/2, np.pi/2)}'''
    )
  
  
  
  
  