





# Author: OMKAR PATHAK, Nwachukwu Chidiebere

# Use a Python dictionary to construct the graph.
from __future__ import annotations

from pprint import pformat
from typing import Generic, TypeVar

__lowerCamelCase			:	str					    =	TypeVar("""T""")



class    A__  (			Generic[T]     ):





      def __init__(					self			,				A_ = True  ):
       '''simple docstring'''




       UpperCamelCase      :   dict[T, list[T]]			      =  {}  # dictionary of lists
       UpperCamelCase      :   Union[str, Any]			      =  directed





      def 			__UpperCamelCase(					self			,				A_			,				A_  ):
       '''simple docstring'''




       if not self.directed:  # For undirected graphs
        # if both source vertex and destination vertex are both present in the
        # adjacency list, add destination vertex to source vertex list of adjacent
        # vertices and add source vertex to destination vertex list of adjacent
        # vertices.
        if source_vertex in self.adj_list and destination_vertex in self.adj_list:
         self.adj_list[source_vertex].append(A_  )
         self.adj_list[destination_vertex].append(A_  )
        # if only source vertex is present in adjacency list, add destination vertex
        # to source vertex list of adjacent vertices, then create a new vertex with
        # destination vertex as key and assign a list containing the source vertex
        # as it's first adjacent vertex.
        elif source_vertex in self.adj_list:
         self.adj_list[source_vertex].append(A_  )
         UpperCamelCase      :   Any			      =  [source_vertex]
        # if only destination vertex is present in adjacency list, add source vertex
        # to destination vertex list of adjacent vertices, then create a new vertex
        # with source vertex as key and assign a list containing the source vertex
        # as it's first adjacent vertex.
        elif destination_vertex in self.adj_list:
         self.adj_list[destination_vertex].append(A_  )
         UpperCamelCase      :   Any			      =  [destination_vertex]
        # if both source vertex and destination vertex are not present in adjacency
        # list, create a new vertex with source vertex as key and assign a list
        # containing the destination vertex as it's first adjacent vertex also
        # create a new vertex with destination vertex as key and assign a list
        # containing the source vertex as it's first adjacent vertex.
        else:
         UpperCamelCase      :   Union[str, Any]			      =  [destination_vertex]
         UpperCamelCase      :   List[str]			      =  [source_vertex]
       else:  # For directed graphs
        # if both source vertex and destination vertex are present in adjacency
        # list, add destination vertex to source vertex list of adjacent vertices.
        if source_vertex in self.adj_list and destination_vertex in self.adj_list:
         self.adj_list[source_vertex].append(A_  )
        # if only source vertex is present in adjacency list, add destination
        # vertex to source vertex list of adjacent vertices and create a new vertex
        # with destination vertex as key, which has no adjacent vertex
        elif source_vertex in self.adj_list:
         self.adj_list[source_vertex].append(A_  )
         UpperCamelCase      :   List[str]			      =  []
        # if only destination vertex is present in adjacency list, create a new
        # vertex with source vertex as key and assign a list containing destination
        # vertex as first adjacent vertex
        elif destination_vertex in self.adj_list:
         UpperCamelCase      :   Union[str, Any]			      =  [destination_vertex]
        # if both source vertex and destination vertex are not present in adjacency
        # list, create a new vertex with source vertex as key and a list containing
        # destination vertex as it's first adjacent vertex. Then create a new vertex
        # with destination vertex as key, which has no adjacent vertex
        else:
         UpperCamelCase      :   Union[str, Any]			      =  [destination_vertex]
         UpperCamelCase      :   Dict			      =  []

       return self





      def __repr__(					self  ):
       '''simple docstring'''




       return pformat(self.adj_list  )







import numpy as np
import qiskit



def 						A_							(	_lowerCAmelCase = 8				,  _lowerCAmelCase = None	)		->					str:
 UpperCamelCase      :   Tuple			      =  np.random.default_rng(seed=_lowerCAmelCase	)

 # Roughly 25% of the qubits will contribute to the key.
 # So we take more than we need.
 UpperCamelCase      :   List[str]			      =  6 * key_len
 # Measurement basis for Alice's qubits.
 UpperCamelCase      :   List[Any]			      =  rng.integers(2				,  size=_lowerCAmelCase	)
 # The set of states Alice will prepare.
 UpperCamelCase      :   List[Any]			      =  rng.integers(2				,  size=_lowerCAmelCase	)
 # Measurement basis for Bob's qubits.
 UpperCamelCase      :   Optional[int]			      =  rng.integers(2				,  size=_lowerCAmelCase	)

 # Quantum Circuit to simulate BB84
 UpperCamelCase      :   List[Any]			      =  qiskit.QuantumCircuit(_lowerCAmelCase				,  name="BB84"	)

 # Alice prepares her qubits according to rules above.
 for index, _ in enumerate(_lowerCAmelCase	):
  if alice_state[index] == 1:
   bbaa_circ.x(_lowerCAmelCase	)
  if alice_basis[index] == 1:
   bbaa_circ.h(_lowerCAmelCase	)
 bbaa_circ.barrier()

 # Bob measures the received qubits according to rules above.
 for index, _ in enumerate(_lowerCAmelCase	):
  if bob_basis[index] == 1:
   bbaa_circ.h(_lowerCAmelCase	)

 bbaa_circ.barrier()
 bbaa_circ.measure_all()

 # Simulate the quantum circuit.
 UpperCamelCase      :   Union[str, Any]			      =  qiskit.Aer.get_backend("aer_simulator"	)
 # We only need to run one shot because the key is unique.
 # Multiple shots will produce the same key.
 UpperCamelCase      :   Tuple			      =  qiskit.execute(_lowerCAmelCase				,  _lowerCAmelCase				,  shots=1				,  seed_simulator=_lowerCAmelCase	)
 # Returns the result of measurement.
 UpperCamelCase      :   Optional[Any]			      =  job.result().get_counts(_lowerCAmelCase	).most_frequent()

 # Extracting the generated key from the simulation results.
 # Only keep measurement results where Alice and Bob chose the same basis.
 UpperCamelCase      :   Tuple			      =  "".join(
     [
         result_bit
         for alice_basis_bit, bob_basis_bit, result_bit in zip(
             _lowerCAmelCase				,  _lowerCAmelCase				,  _lowerCAmelCase	)
         if alice_basis_bit == bob_basis_bit
     ]	)

 # Get final key. Pad with 0 if too short, otherwise truncate.
 UpperCamelCase      :   Tuple			      =  gen_key[:key_len] if len(_lowerCAmelCase	) >= key_len else gen_key.ljust(_lowerCAmelCase				,  "0"	)
 return key


if __name__ == "__main__":
    print(f"""The generated key is : {bbaa(8, seed=0)}""")
    from doctest import testmod

    testmod()
