



'''simple docstring'''




import qiskit



def    __lowerCamelCase						(      _lowercase			,       _lowercase	)   ->    qiskit.result.counts.Counts:
							UpperCAmelCase  :	int							  =	qiskit.Aer.get_backend("""aer_simulator"""	)

							# Create a Quantum Circuit acting on the q register
							UpperCAmelCase  :	List[str]							  =	qiskit.QuantumCircuit(_lowercase			,       _lowercase	)

							# Apply X (NOT) Gate to Qubits 0 & 1
							circuit.x(0	)
							circuit.x(1	)

							# Map the quantum measurement to the classical bits
							circuit.measure([0, 1]			,       [0, 1]	)

							# Execute the circuit on the qasm simulator
							UpperCAmelCase  :	Any							  =	qiskit.execute(_lowercase			,       _lowercase			,       shots=1_0_0_0	)

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


if __name__ == "__main__":
							a     :    Optional[int]      						=					single_qubit_measure(2, 2)
							print(F'''Total count for various states are: {counts}''')






'''simple docstring'''




import math
import random
from typing import Any

from .hill_climbing import SearchProblem



def    __lowerCamelCase						(      _lowercase			,       _lowercase = True			,       _lowercase = math.inf			,       _lowercase = -math.inf			,       _lowercase = math.inf			,       _lowercase = -math.inf			,       _lowercase = False			,       _lowercase = 1_0_0			,       _lowercase = 0.01			,       _lowercase = 1			,       )   ->    Any:
							UpperCAmelCase  :	Optional[int]							  =	False
							UpperCAmelCase  :	Any							  =	search_prob
							UpperCAmelCase  :	Any							  =	start_temperate
							UpperCAmelCase  :	Optional[Any]							  =	[]
							UpperCAmelCase  :	Optional[Any]							  =	0
							UpperCAmelCase  :	Optional[Any]							  =	None

							while not search_end:
														UpperCAmelCase  :	List[str]							  =	current_state.score()
														if best_state is None or current_score > best_state.score():
																					UpperCAmelCase  :	List[Any]							  =	current_state
														scores.append(_lowercase	)
														iterations += 1
														UpperCAmelCase  :	Dict							  =	None
														UpperCAmelCase  :	Union[str, Any]							  =	current_state.get_neighbors()
														while (
														    next_state is None and neighbors
														):  # till we do not find a neighbor that we can move to
																					UpperCAmelCase  :	int							  =	random.randint(0			,       len(_lowercase	) - 1	)  # picking a random neighbor
																					UpperCAmelCase  :	int							  =	neighbors.pop(_lowercase	)
																					UpperCAmelCase  :	Tuple							  =	picked_neighbor.score() - current_score

																					if (
																					    picked_neighbor.x > max_x
																					    or picked_neighbor.x < min_x
																					    or picked_neighbor.y > max_y
																					    or picked_neighbor.y < min_y
																					):
																												continue  # neighbor outside our bounds

																					if not find_max:
																												UpperCAmelCase  :	Union[str, Any]							  =	change * -1  # in case we are finding minimum
																					if change > 0:  # improves the solution
																												UpperCAmelCase  :	int							  =	picked_neighbor
																					else:
																												UpperCAmelCase  :	Optional[Any]							  =	(math.e) ** (
																												    change / current_temp
																												)  # probability generation function
																												if random.random() < probability:  # random number within probability
																																			UpperCAmelCase  :	Optional[int]							  =	picked_neighbor
														UpperCAmelCase  :	List[Any]							  =	current_temp - (current_temp * rate_of_decrease)

														if current_temp < threshold_temp or next_state is None:
																					# temperature below threshold, or could not find a suitable neighbor
																					UpperCAmelCase  :	Optional[int]							  =	True
														else:
																					UpperCAmelCase  :	Optional[int]							  =	next_state

							if visualization:
														from matplotlib import pyplot as plt

														plt.plot(range(_lowercase	)			,       _lowercase	)
														plt.xlabel("""Iterations"""	)
														plt.ylabel("""Function values"""	)
														plt.show()
							return best_state


if __name__ == "__main__":



							def    __lowerCamelCase						(      _lowercase			,       _lowercase	)   ->    str:
														return (x**2) + (y**2)

							# starting the problem with initial coordinates (12, 47)
							a     :    Dict      						=					SearchProblem(x=1_2, y=4_7, step_size=1, function_to_optimize=test_fa)
							a     :    Dict      						=					simulated_annealing(
							    prob, find_max=False, max_x=1_0_0, min_x=5, max_y=5_0, min_y=-5, visualization=True
							)
							print(
							    """The minimum score for f(x, y) = x^2 + y^2 with the domain 100 > x > 5 """
							    F'''and 50 > y > - 5 found via hill climbing: {local_min.score()}'''
							)

							# starting the problem with initial coordinates (12, 47)
							a     :    List[str]      						=					SearchProblem(x=1_2, y=4_7, step_size=1, function_to_optimize=test_fa)
							a     :    Dict      						=					simulated_annealing(
							    prob, find_max=True, max_x=1_0_0, min_x=5, max_y=5_0, min_y=-5, visualization=True
							)
							print(
							    """The maximum score for f(x, y) = x^2 + y^2 with the domain 100 > x > 5 """
							    F'''and 50 > y > - 5 found via hill climbing: {local_min.score()}'''
							)



							def    __lowerCamelCase						(      _lowercase			,       _lowercase	)   ->    Optional[Any]:
														return (3 * x**2) - (6 * y)

							a     :    Tuple      						=					SearchProblem(x=3, y=4, step_size=1, function_to_optimize=test_fa)
							a     :    Any      						=					simulated_annealing(prob, find_max=False, visualization=True)
							print(
							    """The minimum score for f(x, y) = 3*x^2 - 6*y found via hill climbing: """
							    F'''{local_min.score()}'''
							)

							a     :    List[Any]      						=					SearchProblem(x=3, y=4, step_size=1, function_to_optimize=test_fa)
							a     :    Dict      						=					simulated_annealing(prob, find_max=True, visualization=True)


							print(
							    """The maximum score for f(x, y) = 3*x^2 - 6*y found via hill climbing: """
							    F'''{local_min.score()}'''
							)

