Qubit behavior using deque sort with named tuples

example.py

# ...being from the future, this is the best
# AI response we have seen yet on this
# important topic, related to Menorah 
# mathematical symmetries and logical
# approach of defining a basis comparison
# though it lacks needed recursive caching
# for larger custom set operators usages

# blackbox.ai


# Q U E S T

#using a class simulation of qubit behaviors, 
#that involves deque type sorting and named 
#tuples, apply which probability of a set of 
#numbers is closer to palindrome from another 
#set of numbers


# R E S P O N S E:

#1.0) Initialize the necessary Python libraries.

import numpy as np

from collections import deque

from typing import NamedTuple


#2.0) Define a NamedTuple for Qubit state 
#representation.

class QubitState(NamedTuple):

    probabilities: tuple

    palindrome_probability: float
    

#3.0) Define a class simulation of qubit 
#behaviors.


class Qubit:

    def __init__(self, state_probabilities):

        self.state_probabilities = state_probabilities

        self.palindrome_probability = self.calculate_palindrome_probability()


    def calculate_palindrome_probability(self):

        state_probabilities = self.state_probabilities

        sorted_state_probabilities = deque(sorted(state_probabilities))

        sorted_state_probabilities.rotate(-len(sorted_state_probabilities) // 2)

        palindrome_probability = sum(

            probability for probability in sorted_state_probabilities

        )

        return palindrome_probability


    def compare_probabilities(self, other_qubit):

        if not isinstance(other_qubit, Qubit):

            raise ValueError("Comparison object must be a Qubit instance.")


        probabilities_distance = abs(

            self.palindrome_probability - other_qubit.palindrome_probability

        )

        return probabilities_distance
        
        
#4.0) Apply the which probability of a set of 
#numbers is closer to palindrome from 
#another set of numbers.


qubit1 = Qubit((0.2, 0.3, 0.5))

qubit2 = Qubit((0.3, 0.4, 0.3))


distance = qubit1.compare_probabilities(qubit2)


if distance < 1e-9:

    print("Both Qubits have approximately equal palindrome probabilities.")

elif qubit1.palindrome_probability < qubit2.palindrome_probability:

    print(f"Qubit1 has a smaller palindrome probability ({qubit1.palindrome_probability})")

else:

    print(f"Qubit2 has a smaller palindrome probability ({qubit2.palindrome_probability})")
    
    
#This approach utilizes the NamedTuple 
#class from the Python collections library to 
#create a named tuple representation of a 
#Qubit state. The Qubit class contains the 
#logic to calculate the palindrome probability 
#of a set of numbers, which is the sum of the 
#numbers after they are sorted and rotated to #the middle.

#The compare_probabilities method of the 
#Qubit class takes another Qubit instance as #input and returns the absolute difference 
#between the palindrome probabilities of the 
#two Qubits. The function checks if the input 
#is a Qubit instance and raises a ValueError if 
#it is not.

#Finally, the provided code snippet creates 
#two Qubit instances with different state 
#probabilities and compares their palindrome 
#probabilities to determine which one is 
#closer to a palindrome.